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Burbey, Ingrid, i Thomas L. Martin. "A survey on predicting personal mobility". International Journal of Pervasive Computing and Communications 8, nr 1 (30.03.2012): 5–22. http://dx.doi.org/10.1108/17427371211221063.
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PurposeLocation‐prediction enables the next generation of location‐based applications. The purpose of this paper is to provide a historical summary of research in personal location‐prediction. Location‐prediction began as a tool for network management, predicting the load on particular cellular towers or WiFi access points. With the increasing popularity of mobile devices, location‐prediction turned personal, predicting individuals' next locations given their current locations.Design/methodology/approachThis paper includes an overview of prediction techniques and reviews several location‐prediction projects comparing the raw location data, feature extraction, choice of prediction algorithms and their results.FindingsA new trend has emerged, that of employing additional context to improve or expand predictions. Incorporating temporal information enables location‐predictions farther out into the future. Appending place types or place names can improve predictions or develop prediction applications that could be used in any locale. Finally, the authors explore research into diverse types of context, such as people's personal contacts or health activities.Originality/valueThis overview provides a broad background for future research in prediction.
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Hanappi, Hardy. "Predictions and Hopes: Global Political Economy Dynamics of the Next Ten Years". Advances in Social Sciences Research Journal 11, nr 8 (12.08.2024): 66–87. http://dx.doi.org/10.14738/assrj.118.17381.
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Predictions and hopes are different things. Predictions are based on past empirical observations. They single out what seem to be essential variables and the relationships between them and assume that their importance will prevail in the future. Hopes add a component to a prediction, namely an evaluation, which refers back to the entity that produces the prediction. More favourable predictions are hoped to become a reality while others, which would see the entity in a worse position, are not hoped for. A closer look reveals that with a consideration of what predictions are used for by an entity, predictions and hopes are less independent. By predicting an event that one hopes for, the chances that it actually happens might be increased. E.g. in business environments predicting that a competitor will have no chance might intimidate the opponent and help to be victorious. On the other hand, predicting a bad result might induce the entity under consideration to change its current course of action. E.g. the catastrophe predictions of the Club of Rome in the Sixties were meant to be a self-destructing prediction to save the entity, human society, from running into environmental disaster. In both cases, predictions usually exaggerate to produce a stronger impact. Therefore, the way a prediction is formulated always to some extent carries the hopes or anxieties of the entity that produces it.
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Verhun, Volodymyr, i Mykhailo Granchak. "M&A PREDICTIONS: RECONSIDERING THEIR VALUE, END-USERS, AND METHODOLOGIES". Actual Problems of International Relations, nr 160 (2024): 138–51. http://dx.doi.org/10.17721/apmv.2024.160.1.138-151.
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The article explores market participants who may benefit from M&A predictions and how their goals may impact the requirements for M&A predictions. These participants (also called end-users of M&A predictions) are company shareholders considering selling their business, shareholders and company management considering acquiring one or a few other companies, shareholders and company management competing with potential M&A targets or buyers, and advisory firms providing investment banking services in the industries where M&A deals occur. Analyzing their goals while applying M&A predictions, the article concludes that the requirements for M&A predictions can be changed depending on these goals. These end-users may benefit from M&A predictions even if the deals they predict won’t happen. These end-users have the potential to significantly influence the outcome of the M&A events they are predicting. The M&A prediction quality criterion imposed by earlier research - the M&A prediction is correct only when a predicted M&A deal happens - can be relaxed depending on the end-users of M&A predictions and their goals. An M&A prediction will be more valuable for end-users if it includes information on both potential targets and potential buyers. M&A prediction may have a more significant value for end-users if it allows for predicting multiple counterparties for a potential party to an M&A deal. The article analyses the existing theoretical basis behind the M&A predictions and concludes that these theories are insufficient to cover all possible reasons behind the deals from the buyers’ and sellers’ perspectives – additional reasons exist that trigger M&A deals. Also, the existing theories are not always proven by the existing research, showing that their correctness may depend on the context. The article explores the current stance of M&A prediction methodologies, such as: binary state prediction models based on a linear combination of independent variables, starting from the earlier works focused on prediction variables for M&A targets to later works dedicated to adding new company-specific prediction variables of the targets and reflecting the context; alternative computational techniques to predict M&A targets, like non-parametric computational techniques, including machine learning; methodologies to predict M&A buyers; methodologies to predict pairs of buyers and targets, researching the relatedness between them. The article concludes that the M&A prediction methodology shall consider and reflect additional motivations for the M&A deal for targets and buyers and shall always include the context. Predicting only targets seems like a one-sided approach. On the contrary, predicting both parties of the deal seems like a promising prediction methodology. Non-parametric computational techniques based on a broader range of prediction variables, reflecting the motivations of the M&A deal’s parties and the context, look like a promising basic prediction methodology that should be further developed. Testing new M&A prediction methodologies within a specific sector for a longer time looks promising for increasing the robustness of the model's prediction ability. Finally, out-of-sample tests done over a longer time are necessary to check the models’ prediction ability.
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Tang, Li, Ping He Pan i Yong Yi Yao. "EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices". International Journal of Computers Communications & Control 13, nr 2 (13.04.2018): 268–79. http://dx.doi.org/10.15837/ijccc.2018.2.3187.
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This paper proposes a new computational intelligence model for predicting univariate time series, called EPAK, and a complex prediction model for stock market index synthesizing all the sector index predictions using EPAK as a kernel. The EPAK model uses a complex nonlinear feature extraction procedure integrating a forward rolling Empirical Mode Decomposition (EMD) for financial time series signal analysis and Principal Component Analysis (PCA) for dimension reduction to generate information-rich features as input to a new two-layer K-Nearest Neighbor (KNN) with Affinity Propagation (AP) clustering for prediction via regression. The EPAK model is then used as a kernel for predicting each of all the sector indices of the stock market. The sector indices predictions are then synthesized via weighted average to generate the prediction of the stock market index, yielding a complex prediction model for the stock market index. The EPAK model and the complex prediction model for stock index are tested on real historical financial time series in Chinese stock index including CSI 300 and ten sector indices, with results confirming the effectiveness of the proposed models.
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Yan, Xiaohui, Tianqi Zhang, Wenying Du, Qingjia Meng, Xinghan Xu i Xiang Zhao. "A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years". Journal of Marine Science and Engineering 12, nr 1 (13.01.2024): 159. http://dx.doi.org/10.3390/jmse12010159.
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Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction.
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Zhang, Chenglong, i Hyunchul Ahn. "E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors". Education Sciences 13, nr 11 (13.11.2023): 1130. http://dx.doi.org/10.3390/educsci13111130.
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This study focused on predicting at-risk groups of students at the Open University (OU), a UK university that offers distance-learning courses and adult education. The research was conducted by drawing on publicly available data provided by the Open University for the year 2013–2014. The semester’s time series was considered, and data from previous semesters were used to predict the current semester’s results. Each course was predicted separately so that the research reflected reality as closely as possible. Three different methods for selecting training data were listed. Since the at-risk prediction results needed to be provided to the instructor every week, four representative time points during the semester were chosen to assess the predictions. Furthermore, we used eight single and three integrated machine-learning algorithms to compare the prediction results. The results show that using the same semester code course data for training saved prediction calculation time and improved the prediction accuracy at all time points. In week 16, predictions using the algorithms with the voting classifier method showed higher prediction accuracy and were more stable than predictions using a single algorithm. The prediction accuracy of this model reached 81.2% for the midterm predictions and 84% for the end-of-semester predictions. Finally, the study used the Shapley additive explanation values to explore the main predictor variables of the prediction model.
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Zhuang, Wei, Zhiheng Li, Ying Wang, Qingyu Xi i Min Xia. "GCN–Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting". Applied Sciences 14, nr 5 (5.03.2024): 2181. http://dx.doi.org/10.3390/app14052181.
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Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction, traditional deep learning methods often generate predictions for long sequences one by one, significantly impacting the efficiency of model predictions. As the scale of photovoltaic power stations expands and the demand for predictions increases, this sequential prediction approach may lead to slow prediction speeds, making it difficult to meet real-time prediction requirements. (2) Feature extraction is a crucial step in photovoltaic power generation prediction. However, traditional feature extraction methods often focus solely on surface features, and fail to capture the inherent relationships between various influencing factors in photovoltaic power generation data, such as light intensity, temperature, and more. To overcome these limitations, this paper proposes a mid-term PV power prediction model that combines Graph Convolutional Network (GCN) and Informer models. This fusion model leverages the multi-output capability of the Informer model to ensure the timely generation of predictions for long sequences. Additionally, it harnesses the feature extraction ability of the GCN model from nodes, utilizing graph convolutional modules to extract feature information from the ‘query’ and ‘key’ components within the attention mechanism. This approach provides more reliable feature information for mid-term PV power prediction, thereby ensuring the accuracy of long sequence predictions. Results demonstrate that the GCN–Informer model significantly reduces prediction errors while improving the precision of power generation forecasting compared to the original Informer model. Overall, this research enhances the prediction accuracy of PV power generation and contributes to advancing the field of clean energy.
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Lelis, Levi, Sandra Zilles i Robert Holte. "Improved Prediction of IDA*'s Performance via Epsilon-Truncation". Proceedings of the International Symposium on Combinatorial Search 2, nr 1 (19.08.2021): 108–16. http://dx.doi.org/10.1609/socs.v2i1.18198.
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Korf, Reid, and Edelkamp launched a line of research aimed at predicting how many nodes IDA* will expand with a given cost bound. This paper advances this line of research in three ways. First, we identify a source of prediction error that has hitherto been overlooked. We call it the ``discretization effect''. Second, we disprove the intuitively appealing idea that a ``more informed'' prediction system cannot make worse predictions than a ``less informed'' one. More informed systems are more susceptible to the discretization effect, and in several of our experiments the more informed system makes poorer predictions. Our third contribution is a method, called ``$\epsilon$-truncation'', which makes a prediction system less informed, in a carefully chosen way, so as to improve its predictions by reducing the discretization effect. In our experiments $\epsilon$-truncation rarely degraded predictions; in the vast majority of cases it improved predictions, often substantially.
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Rather, Akhter Mohiuddin. "A Hybrid Intelligent Method of Predicting Stock Returns". Advances in Artificial Neural Systems 2014 (7.09.2014): 1–7. http://dx.doi.org/10.1155/2014/246487.
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This paper proposes a novel method for predicting stock returns by means of a hybrid intelligent model. Initially predictions are obtained by a linear model, and thereby prediction errors are collected and fed into a recurrent neural network which is actually an autoregressive moving reference neural network. Recurrent neural network results in minimized prediction errors because of nonlinear processing and also because of its configuration. These prediction errors are used to obtain final predictions by summation method as well as by multiplication method. The proposed model is thus hybrid of both a linear and a nonlinear model. The model has been tested on stock data obtained from National Stock Exchange of India. The results indicate that the proposed model can be a promising approach in predicting future stock movements.
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Harahap, Rahma Sari, Iskandar Muda i Rina Br Bukit. "Analisis penggunaan metode Altman Z-Score dan Springate untuk mengetahui potensi terjadinya Financial Distress pada perusahaan manufaktur sektor industri dasar dan kimia Sub Sektor semen yang terdaftar di Bursa Efek Indonesia 2000-2020". Owner 6, nr 4 (14.10.2022): 4315–25. http://dx.doi.org/10.33395/owner.v6i4.1576.
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The objective of the research is to find out the result of predicting bankruptcy, using Altman Z-Score and Springate methods in the manufacturing companies of basic industrial and chemistry sectors, cement sub-sector listed on BEI (Indonesia Stock Exchange) in the period of 2000-2020 and to determine the most accurate predicting method of bankruptcy to be applied in the manufacturing companies in basic industrial and chemistry sectors, cement sub-sector. The research employs descriptive quantitative method. The samples are taken by using purposive sampling method with three manufacture companies in basic industrial and chemistry sectors and cement sub-sector. The data are analyzed by using the accuracy and error levels in each predicting method of bankruptcy, and each method shows different prediction. The result of financial distress prediction, using Altman Z-Score shows that there are 19 financial distress predictions, 26 non-financial distress predictions, and 18 gray area predictions. The result of financial distress prediction, using Springate method shows that there are 22 financial distress predictions and 41 non-financial distress predictions, the result of the calculation in accuracy and error levels, using Springate method, shows that Springate method is the most accurate with the accuracy level of 65.08% and the error level of 34.92% .
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PALOPOLI, LUIGI, i GIORGIO TERRACINA. "CooPPS: A SYSTEM FOR THE COOPERATIVE PREDICTION OF PROTEIN STRUCTURES". Journal of Bioinformatics and Computational Biology 02, nr 03 (wrzesień 2004): 471–95. http://dx.doi.org/10.1142/s0219720004000697.
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Predicting the three-dimensional structure of proteins is a difficult task. In the last few years several approaches have been proposed for performing this task taking into account different protein chemical and physical properties. As a result, a growing number of protein structure prediction tools is becoming available, some of them specialized to work on either some aspects of the predictions or on some categories of proteins; however, they are still not sufficiently accurate and reliable for predicting all kinds of proteins. In this context, it is useful to jointly apply different prediction tools and combine their results in order to improve the quality of the predictions. However, several problems have to be solved in order to make this a viable possibility. In this paper a framework and a tool is proposed which allows: (i) definition of a common reference applicative domain for different prediction tools; (ii) characterization of prediction tools through evaluating some quality parameters; (iii) characterization of the performances of a team of predictors jointly applied over a prediction problem; (iv) the singling out of the best team for a prediction problem; and (v) the integration of predictor results in the team in order to obtain a unique prediction. A system implementing the various steps of the proposed framework (CooPPS) has been developed and several experiments for testing the effectiveness of the proposed approach have been carried out.
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Lelis, Levi, Sandra Zilles i Robert Holte. "Time Complexity of Iterative-Deepening A*: The Informativeness Pathology (Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 25, nr 1 (4.08.2011): 1800–1801. http://dx.doi.org/10.1609/aaai.v25i1.8053.
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Korf, Reid, and Edelkamp launched a line of research aimed at predicting how many nodes IDA* will expand with a given depth bound. This paper advances this line of research in three ways. First, we identify a source of prediction error that has hitherto been overlooked. We call it the "discretization effect." Second, we disprove the intuitively appealing idea that a "more informed" prediction system cannot make worse predictions than a ``less informed'' one. More informed systems are more susceptible to the discretization effect, and in our experiments the more informed system makes poorer predictions. Our third contribution is a method, called "Epsilon-truncation," which makes a prediction system less informed, in a carefully chosen way, so as to improve its predictions by reducing the discretization effect. In our experiments Epsilon-truncation improved predictions substantially.
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Je-Gal, Hong, Young-Seo Park, Seong-Ho Park, Ji-Uk Kim, Jung-Hee Yang, Sewon Kim i Hyun-Suk Lee. "Time-Series Explanatory Fault Prediction Framework for Marine Main Engine Using Explainable Artificial Intelligence". Journal of Marine Science and Engineering 12, nr 8 (31.07.2024): 1296. http://dx.doi.org/10.3390/jmse12081296.
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As engine monitoring data has become more complex with an increasing number of sensors, fault prediction based on artificial intelligence (AI) has emerged. Existing fault prediction models using AI significantly improve the accuracy of predictions by effectively handling such complex data, but at the same time, the problem arises that the AI-based models cannot explain the rationale of their predictions to users. To address this issue, we propose a time-series explanatory fault prediction framework to provide an explainability even when using AI-based fault prediction models. It consists of a data feature reduction process, a fault prediction model training process using long short-term memory, and an interpretation process of the fault prediction model via an explainable AI method. In particular, the proposed framework can explain a fault prediction based on time-series data. Therefore, it indicates which part of the data was significant for the fault prediction not only in terms of sensor type but also in terms of time. Through extensive experiments, we evaluate the proposed framework using various fault data by comparing the prediction performance of fault prediction and by assessing how well the main pre-symptoms of the fault are extracted when predicting a fault.
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Wu, Xinhua, Nan Chen, Qianyun Du, Shuangshuang Mao i Xiaoming Ju. "Short-term wind power prediction model based on ARMA-GRU-QPSO and error correction". Journal of Physics: Conference Series 2427, nr 1 (1.02.2023): 012028. http://dx.doi.org/10.1088/1742-6596/2427/1/012028.
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Abstract Power system dispatch benefits from accurate wind power predictions. To increase the prediction precision for wind power, this paper proposes a combined model for predicting short-term wind power based on the autoregressive moving average-gated recurrent unit (ARMA-GRU). Firstly, we build the ARMA model and GRU model respectively to predict wind power. Then we optimize the combined model’s weights by quantum particle swarm algorithm (QPSO). Finally, we build an error correction model for the prediction errors to acquire the final results for the wind power predictions. Our experimental results prove the model’s reliability and the model’s high predictability is verified by comparing different prediction models.
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Chen, Zixuan, Guojie Wang, Xikun Wei, Yi Liu, Zheng Duan, Yifan Hu i Huiyan Jiang. "Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China". Atmosphere 15, nr 2 (25.01.2024): 155. http://dx.doi.org/10.3390/atmos15020155.
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Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions.
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alan, Gun, Kavin Kumar, Su rya i Kalai Chelvi. "Stock Market Prediction". International Academic Journal of Science and Engineering 9, nr 1 (18.07.2022): 18–22. http://dx.doi.org/10.9756/iajse/v9i2/iajse0909.
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Researchers have been investigating various approaches to accurately forecast stock market prices. Trading professionals can gain better insights regarding data, such as potential trends, by using useful prediction tools. Additionally, since the study predicts future market conditions, investors stand to gain significantly. Using machine learning algorithms for predicting is one such approach. The goal of this study is to increase the accuracy of stock market predictions made using stock valuation. Many academics have developed various approaches to address this issue, primarily using conventional approaches up to this point, like artificial neural networks, which are ways to identify hidden patterns in data and classify it for use in stock market prediction. This initiative suggests a fresh approach to stock forecasting.
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Sudriyanto, Sudriyanto, Fatimatus Syahro i Novi Fitriani. "PERBANDINGAN PERFORMA MODEL MACHINE LEARNING SUPPORT VECTOR MACHINE, NEURAL NETWORK, DAN K-NEAREST NEIGHBORS DALAM PREDIKSI HARGA SAHAM". Jurnal Advanced Research Informatika 2, nr 1 (8.12.2023): 13–21. http://dx.doi.org/10.24929/jars.v2i1.2983.
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This study aims to analyze the performance of three prediction models, namely K-Nearest Neighbors (K-NN), Neural Network (NN), and Support Vector Machine (SVM), in predicting the stock price of PT Astra International Tbk (ASII.JK). The research encompasses the initial stages through evaluation using optimal parameters for these three algorithms. The research findings reveal that the K-NN prediction model has the lowest Root Mean Square Error (RMSE) value, with a value of 0.037, indicating the most accurate prediction compared to the other models. Despite the NN model having an RMSE of 0.048, which is higher than K-NN, it still provides reasonably accurate predictions. Meanwhile, the SVM model has an RMSE of 0.075, indicating a higher level of error in its predictions. Based on these results, the recommendation is to utilize the K-NN model as the preferred choice for predicting the ASII.JK stock price.
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Jin, Long, Cai Yao i Xiao-Yan Huang. "A Nonlinear Artificial Intelligence Ensemble Prediction Model for Typhoon Intensity". Monthly Weather Review 136, nr 12 (1.12.2008): 4541–54. http://dx.doi.org/10.1175/2008mwr2269.1.
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Abstract A new nonlinear artificial intelligence ensemble prediction (NAIEP) model has been developed for predicting typhoon intensity based on multiple neural networks with the same expected output and using an evolutionary genetic algorithm (GA). The model is validated with short-range forecasts of typhoon intensity in the South China Sea (SCS); results show that the NAIEP model is clearly better than the climatology and persistence (CLIPER) model for 24-h forecasts of typhoon intensity. Using identical predictors and sample cases, predictions of the genetic neural network (GNN) ensemble prediction (GNNEP) model are compared with the single-GNN prediction model, and it has been proven theoretically that the former is more accurate. Computation and analysis of the generalization capacity of GNNEP also demonstrate that the prediction of the ensemble model integrates predictions of its optimized ensemble members, so the generalization capacity of the ensemble prediction model is also enhanced. This model better addresses the “overfitting” problem that generally exists in the traditional neural network approach to practical weather prediction.
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Ahmadi, Farrokh, Abbas Toloie Eshlaghi i Reza Radfar. "Examining and Comparing the Efficiency of MLP and SimpleRNN Algorithms in Cryptocurrency Price Prediction". Management Strategies and Engineering Sciences 6, nr 3 (2024): 121–37. https://doi.org/10.61838/msesj.6.3.12.
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Cryptocurrencies have been widely identified and established as a new form of electronic currency exchange, carrying significant implications for emerging economies and the global economy. This research focused on the "examination and comparison of the efficiency of MLP and SimpleRNN algorithms in predicting cryptocurrency prices" using the Python programming language. Price predictions for Bitcoin, Ethereum, Binance Coin, Cardano, and Ripple were made using two deep learning algorithms (including the MLP algorithm and the SimpleRNN algorithm) over the period from 2017 to 2023. The results of cryptocurrency price prediction using deep learning algorithms were satisfactory; and the comparison of predictions across all cryptocurrencies indicated minimal differences between the algorithms studied, suggesting that they were efficient and had low error rates. Based on the obtained results regarding Bitcoin price prediction, the best algorithm was SimpleRNN; for Ethereum price prediction, the best algorithm was MLP; for Binance Coin price prediction, the best algorithm was SimpleRNN; for Cardano price prediction, the best algorithm was MLP; and for Ripple price prediction, the best algorithm was MLP.
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Badjate, Sanjay L., i Sanjay V. Dudul. "Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series". Applied Computational Intelligence and Soft Computing 2009 (2009): 1–21. http://dx.doi.org/10.1155/2009/364532.
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Multistep ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in the recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multistep chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present some disadvantages when long-term prediction is required. In this paper focused time-lagged recurrent neural network (FTLRNN) model with gamma memory is developed for different prediction horizons. It is observed that this predictor performs remarkably well for short-term predictions as well as medium-term predictions. For coupled partial differential equations generated chaotic time series such as Mackey Glass and Duffing, FTLRNN-based predictor performs consistently well for different depths of predictions ranging from short term to long term, with only slight deterioration after k is increased beyond 50. For real-world highly complex and nonstationary time series like Sunspots and Laser, though the proposed predictor does perform reasonably for short term and medium-term predictions, its prediction ability drops for long term ahead prediction. However, still this is the best possible prediction results considering the facts that these are nonstationary time series. As a matter of fact, no other NN configuration can match the performance of FTLRNN model. The authors experimented the performance of this FTLRNN model on predicting the dynamic behavior of typical Chaotic Mackey-Glass time series, Duffing time series, and two real-time chaotic time series such as monthly sunspots and laser. Static multi layer perceptron (MLP) model is also attempted and compared against the proposed model on the performance measures like mean squared error (MSE), Normalized mean squared error (NMSE), and Correlation Coefficient (r). The standard back-propagation algorithm with momentum term has been used for both the models.
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Yang, Jiapeng. "Goldman Sachs’s Price Forecast Based on ARIMA and LSTM". Highlights in Business, Economics and Management 24 (22.01.2024): 2194–201. http://dx.doi.org/10.54097/zk7c4c90.
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The prediction of stock prices is a common and crucial problem in trading. Correctly predicting future stock prices enables traders to determine the optimal time to buy and sell stocks, increasing the probability of making profits. This study focuses on predicting the closing price of Goldman Sachs. Initially, an ARIMA (4,1,6) benchmark model was established based on the AIC information criteria for time series prediction. The model was then applied to make forward predictions. Subsequently, a two-layer LSTM model was constructed. The prediction results of both models were visualized, and the regression indicators were calculated for each model. By comparing the prediction results of the two algorithms, it was determined that LSTM model outperforms ARIMA on the dataset used in this paper. Furthermore, this paper highlights some shortcomings of the ARIMA model, including its unsuitability for long-term forecasting and its subjective parameter selection. In contrast, LSTM performs better in predicting turning points in stock prices.
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Shaji, Hima Elsa, Arun K. Tangirala i Lelitha Vanajakshi. "Joint clustering and prediction approach for travel time prediction". PLOS ONE 17, nr 9 (23.09.2022): e0275030. http://dx.doi.org/10.1371/journal.pone.0275030.
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Modeling and prediction of traffic systems is a challenging task due to the complex interactions within the system. Identification of significant regressors and using them to improve travel time predictions is a concept of interest. In previous studies, such regressors were identified offline and were static in nature. In this study, an iterative joint clustering and prediction approach is proposed to accurately predict spatiotemporal patterns in travel time. The clustering module is tied to the prediction module, and a prediction model is trained on each cluster. The combined clustering and prediction are then iterated until a chosen metric is optimized. This orients clusters of data towards prediction while enabling model development on subsets of travel time data with similar prediction complexity. The clusters created using the joint clustering and prediction approach confirmed to the real-world traffic scenario, forming clusters of high travel time at busy intersections and bus stops across the study stretch and forming clusters of low travel time in the sub-urban areas of the city. Further, a comparison of the developed framework with base methods demonstrated a decrease in prediction errors by at least 22.83%. This indicates that creating clusters of data that are sensitive to the quality of predictions using the joint clustering and prediction framework improves the accuracy of travel time predictions. The study also proposes criteria for choosing the best predictions when cluster-based predictions are used.
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Siemens, Angela, Spencer J. Anderson, S. Rod Rassekh, Colin J. D. Ross i Bruce C. Carleton. "A Systematic Review of Polygenic Models for Predicting Drug Outcomes". Journal of Personalized Medicine 12, nr 9 (27.08.2022): 1394. http://dx.doi.org/10.3390/jpm12091394.
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Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.
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Fang, Yiheng. "Prediction of the Ammonia Nitrogen Content with Improved Grey Model by Markov Chain". Highlights in Science, Engineering and Technology 88 (29.03.2024): 156–61. http://dx.doi.org/10.54097/zee1cd17.
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Water pollution prediction plays a crucial role in environmental protection and sustainable development. This study proposes an innovative approach to enhance the accuracy of water pollution prediction by combining the grey prediction model (GM) with Markov chain analysis. This research focuses on predicting the concentration of ammonia nitrogen (NH3-N) in Dongting Lake, a significant water body. Grey prediction models (GM) are utilized to forecast NH3-N content, addressing the challenge posed by incomplete or insufficient data. However, due to the dynamic nature of water quality indicators, GM models may have limitations in terms of accuracy. To overcome this issue, this study introduces the concept of Markov chains, incorporating historical state transitions into prediction models to achieve more precise forecasts. The research demonstrates a novel method for water pollution prediction that integrates GM models with Markov chain analysis, resulting in improved accuracy when predicting NH3-N concentrations. A comparison with traditional GM predictions highlights the effectiveness of this approach. The model's performance was evaluated using actual data from the China Automated Water Quality Monitoring Report. Combining grey prediction models with Markov chains outperforms traditional methods when it comes to predicting water pollution levels. The result contributes to advancing the field of water pollution forecasting by enhancing forecasting accuracy and providing informed decision support for environmental protection and management purposes.
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Wadi, Faska Aris Y. K., Putu Sugiartawan, Ni Nengah Dita Adriani i Ni Nengah Dita Adriani. "Analisa Prediksi Time Series Jumlah Kasus Covid-19 Dengan Metode BPNN Di Bali". Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) 4, nr 1 (14.01.2022): 24–33. http://dx.doi.org/10.33173/jsikti.124.
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The COVID-19 pandemic has not yet subsided. This epidemic has spread to almost all countries in the world. In Indonesia, especially in the province of Bali, which experienced a large number of additional positive cases, recoveries and deaths from COVID-19, an analysis was carried out. The purpose of this analysis is to be able to obtain accuracy in predicting the addition of COVID-19 cases, recoveries and deaths in the province of Bali, predictions are made using the covid-19 time series data used in making predictions. what was done obtained the best and not good prediction accuracy, prediction using one input and one output obtained the best precision model accuracy of 72% and for poor accuracy using three inputs and one output with a prediction model accuracy of 33% in the process Covid-19 predictions in Bali.
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Li, Ze. "Prediction of MBTI Personality Leveraging Machine Learning Algorithms". Applied and Computational Engineering 8, nr 1 (1.08.2023): 580–87. http://dx.doi.org/10.54254/2755-2721/8/20230275.
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In this study, the author attempted to implement a machine learning approach to determine users' corresponding MBTI personality types by relying only on the content of their online forum postings. Models based on different algorithms are built and trained, and the natural language of the collected data set is converted into machine language for machine learning and used in subsequent tests to determine the correctness of the predicting results. The data set is collected from the forum and divided into two parts, the training set is leveraged to train the model and the test data set is leveraged to make personality predictions and compare with the training data set to measure the correctness of the predicting outcomes. The results show that logistic regression algorithm and vectorized representation of text with TfidfVectorizer can best accomplish the prediction task. This study completed a preliminary comparison of algorithms for personality prediction from text, which became the basis for subsequent personality model predictions using other media.
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Masdiantini, Putu Riesty, i Ni Made Sindy Warasniasih. "Laporan Keuangan dan Prediksi Kebangkrutan Perusahaan". Jurnal Ilmiah Akuntansi 5, nr 1 (25.06.2020): 196. http://dx.doi.org/10.23887/jia.v5i1.25119.
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This study aims to determine differences in bankruptcy predictions at company’s sub-sector of cosmetics and household listed on the Indonesia Stock Exchange (IDX) using the Altman model, Springate model, Zmijewski model, Taffler model, and Fulmer model, and to determine the bankruptcy prediction model that is the most accurate of the five bankruptcy prediction models. This study uses secondary data in the form of company financial statements for the period 2014-2018. Data analysis techniques in this study used the Kruskal-Wallis test. The results showed there were differences in bankruptcy predictions using the Altman model, Springate model, Zmijewski model, Taffler model, and Fulmer model. The Zmijewski, Taffler, and Fulmer models have the same accuracy level of 100% so that the three prediction models are the most accurate prediction models for predicting the potential bankruptcy at companies sub-sector of cosmetics and household listed on the IDX.
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Liu, Gaoxiang, Xin Yu i Danyang Liu. "Predictive Model for Long-Term Lane Occupancy Rate Based on CT-Transformer and Variational Mode Decomposition". Applied Sciences 14, nr 12 (20.06.2024): 5346. http://dx.doi.org/10.3390/app14125346.
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Lane occupancy is a crucial indicator of traffic flow and is significant for traffic management and planning. However, predicting lane occupancy is challenging due to numerous influencing factors, such as weather, holidays, and events, which render the data nonsmooth. To enhance lane occupancy prediction accuracy, this study introduces a fusion model that combines the CT-Transformer (CSPNet-Attention and Two-stage Transformer framework) with the Temporal Convolutional Neural Network-Long Short-Term Memory (TCN-LSTM) models alongside the Variational Mode. This includes a long-term lane occupancy prediction model utilizing the Variational Mode Decomposition (VMD) technique. Initially, the Variational Mode Decomposition decomposes the original traffic flow data into multiple smooth subsequences. Subsequently, each subsequence’s autocorrelation and partial correlation coefficients ascertain the presence of seasonal characteristics. Based on these characteristics, the CT-Transformer and TCN-LSTM models process each subsequence for long-term lane occupancy rate prediction, respectively. Finally, predictions from both models are integrated using variable modes to derive the ultimate lane occupancy predictions. The core CT-Transformer model, an enhancement of the GBT (Two-stage Transformer) model, comprises two phases: autoregressive and prediction. The autoregressive phase leverages historical data for initial predictions inputted into the prediction phase. Here, the novel CSPNet-Attention mechanism replaces the conventional attention mechanism in the Encoder, reducing memory usage and computational resource loss, thereby enhancing the model’s accuracy and robustness. Experiments on the PeMS public dataset demonstrate that the proposed model surpasses existing methods in predicting long-term lane occupancy, offering decent reliability and generalizability.
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Jeong, Jiseok, i Changwan Kim. "Comparison of Machine Learning Approaches for Medium-to-Long-Term Financial Distress Predictions in the Construction Industry". Buildings 12, nr 10 (20.10.2022): 1759. http://dx.doi.org/10.3390/buildings12101759.
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A method for predicting the financial status of construction companies after a medium-to-long-term period can help stakeholders in large construction projects make decisions to select an appropriate company for the project. This study compares the performances of various prediction models. It proposes an appropriate model for predicting the financial distress of construction companies considering three, five, and seven years ahead of the prediction point. To establish the prediction model, a financial ratio was selected, which was adopted in existing studies on medium-to-long-term predictions in other industries, as an additional input variable. To compare the performances of the prediction models, single-machine learning and ensemble models’ performances were compared. The comprehensive performance comparison of these models was based on the average value of the prediction performance and the results of the Friedman test. The comparison result determined that the random subspace (RS) model exhibited the best performance in predicting the financial status of construction companies after a medium-to-long-term period. The proposed model can be effectively employed to help large-scale project stakeholders avoid damage caused by the financial distress of construction companies during the project implementation process.
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Merrill, Zachary, Subashan Perera i Rakié Cham. "Torso Segment Parameter Prediction in Working Adults". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, nr 1 (wrzesień 2018): 1257–61. http://dx.doi.org/10.1177/1541931218621289.
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Body segment parameters (BSPs) such as segment mass, center of mass, and radius of gyration are used as inputs in static and dynamic ergonomic and biomechanical models used to predict joint and muscle forces, and related risks of musculoskeletal injury. Because these models are sensitive to BSP values, accurate and representative parameters are necessary for injury risk prediction. While previous studies have determined segment parameters in the general population, as well as the impact of age and obesity levels on these parameters, estimated errors in the prediction of BSPs can be as large as 40% (Durkin, 2003). Thus, more precise values are required for attempting to predict injury risk in individuals. This study aims to provide statistical models for predicting torso segment parameters in working adults using whole body dual energy x-ray absorptiometry (DXA) scan data along with a set of anthropometric measurements. The statistical models were developed on a training subset of the study population, and validated on a testing subset. When comparing the model predictions to the actual BSPs of the testing subset, the predictions were, on average, within 5% of the calculated parameters, while previously developed predictions (de Leva, 1996) had average errors of up to 30%, indicating that the new statistical models greatly increase the accuracy in predicting BSPs.
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Roulston, Mark S., i Kim Kaivanto. "Joint-outcome prediction markets for climate risks". PLOS ONE 19, nr 8 (30.08.2024): e0309164. http://dx.doi.org/10.1371/journal.pone.0309164.
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Predicting future climate requires the integration of knowledge and expertise from a wide range of disciplines. Predictions must account for climate-change mitigation policies which may depend on climate predictions. This interdependency, or “circularity”, means that climate predictions must be conditioned on emissions of greenhouse gases (GHGs). Long-range forecasts also suffer from information asymmetry because users cannot use track records to judge the skill of providers. The problems of aggregation, circularity, and information asymmetry can be addressed using prediction markets with joint-outcome spaces, allowing simultaneous forecasts of GHG concentrations and temperature. The viability of prediction markets with highly granular, joint-outcome spaces was tested with markets for monthly UK rainfall and temperature. The experiments demonstrate these markets can aggregate the judgments of experts with relevant expertise, and suggest similarly structured markets, with longer horizons, could provide a mechanism to produce credible forecasts of climate-related risks for policy making, planning, and risk disclosure.
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Busari, Ibrahim, Debabrata Sahoo, R. Daren Harmel i Brian E. Haggard. "A Review of Machine Learning Models for Harmful Algal Bloom Monitoring in Freshwater Systems". Journal of Natural Resources and Agricultural Ecosystems 1, nr 2 (2023): 63–76. http://dx.doi.org/10.13031/jnrae.15647.
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Highlights Machine Learning (ML) models are identified, reviewed, and analyzed for HAB predictions. Data preprocessing is vital for efficient ML model development. ML models for toxin production and monitoring are limited. Abstract. Harmful algal blooms (HABs) are detrimental to livestock, humans, pets, the environment, and the global economy, which calls for a robust approach to their management. While process-based models can inform practitioners about HAB enabling conditions, they have inherent limitations in accurately predicting harmful algal blooms. To address these limitations, Machine Learning (ML) models can potentially leverage large volumes of IoT data to aid in near real-time predictions. ML models have evolved as efficient tools for understanding patterns and relationships between water quality parameters and HAB expansion. This review describes ML models currently used for predicting and forecasting HABs in freshwater ecosystems and presents model structures and their application for predicting algal parameters and related toxins. The review revealed that regression trees, random forest, Artificial Neural Network (ANN), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) are the most frequently used models for HABs monitoring. This review shows ML models' prowess in identifying significant variables influencing algal growth, HAB drivers, and multistep HAB prediction. Hybrid models also improve the prediction of algal-related parameters through improved optimization techniques and variable selection algorithms. While ML models often focus on algal biomass prediction, few studies apply ML models for toxin monitoring and prediction. This limitation can be associated with a lack of high-frequency toxin datasets for model development, and exploring this domain is encouraged. This review serves as a guide for policymakers and researchers to implement ML models for HAB prediction and reveals the potential of ML models for decision support and early prediction for HAB management. Keywords: Cyanobacteria, Freshwater, Harmful algal blooms, Machine learning, Water quality.
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Spichak, V. V., i O. K. Zakharova. "Neural Network Modeling of Electromagnetic Prediction of Geothermal Reservoir Properties". Физика земли 2023, nr 1 (1.01.2023): 67–80. http://dx.doi.org/10.31857/s0002333723010064.
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This work conducts neural network modeling of temperature, thermal conductivity, and permeability predictions for depths greater than those drilled, as well as for the immediate vicinity of the exploratory borehole. For this purpose, we use data from three boreholes drilled earlier in the Soultz-sous-Forêts geothermal site (France) and the results of the magnetotelluric sounding performed there. It is shown that the relative accuracy of the predictions depends significantly on the relationship between the depth of the drilled borehole and the target depth of the prediction. For instance, for all the examined parameters, prediction errors become less than 5% if the prediction is made for depths that do not exceed the borehole depth by more than two times. In this case, the average errors of temperature and thermal conductivity predictions for the vicinity of the drilled borehole were 3.6% and 6%, respectively. The obtained results justified a new scheme for predicting the thermophysical and porosity/permeability properties of rocks while drilling exploratory geothermal boreholes.
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de Zarzà, I., J. de Curtò, Enrique Hernández-Orallo i Carlos T. Calafate. "Cascading and Ensemble Techniques in Deep Learning". Electronics 12, nr 15 (5.08.2023): 3354. http://dx.doi.org/10.3390/electronics12153354.
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In this study, we explore the integration of cascading and ensemble techniques in Deep Learning (DL) to improve prediction accuracy on diabetes data. The primary approach involves creating multiple Neural Networks (NNs), each predicting the outcome independently, and then feeding these initial predictions into another set of NN. Our exploration starts from an initial preliminary study and extends to various ensemble techniques including bagging, stacking, and finally cascading. The cascading ensemble involves training a second layer of models on the predictions of the first. This cascading structure, combined with ensemble voting for the final prediction, aims to exploit the strengths of multiple models while mitigating their individual weaknesses. Our results demonstrate significant improvement in prediction accuracy, providing a compelling case for the potential utility of these techniques in healthcare applications, specifically for prediction of diabetes where we achieve compelling model accuracy of 91.5% on the test set on a particular challenging dataset, where we compare thoroughly against many other methodologies.
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Hu, Guang, i Yue Tang. "GERPM: A Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model". Mathematics 11, nr 14 (18.07.2023): 3160. http://dx.doi.org/10.3390/math11143160.
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Accurate prediction of urban residential rents is of great importance for landlords, tenants, and investors. However, existing rents prediction models face challenges in meeting practical demands due to their limited perspectives and inadequate prediction performance. The existing individual prediction models often lack satisfactory accuracy, while ensemble learning models that combine multiple individual models to improve prediction results often overlook the impact of spatial heterogeneity on residential rents. To address these issues, this paper proposes a novel prediction model called GERPM, which stands for Geographically Weighted Stacking Ensemble Learning-Based Urban Residential Rents Prediction Model. GERPM comprehensively analyzes the influencing factors of residential rents from multiple perspectives and leverages a geographically weighted stacking ensemble learning approach. The model combines multiple machine learning and deep learning models, optimizes parameters to achieve optimal predictions, and incorporates the geographically weighted regression (GWR) model to consider spatial heterogeneity. By combining the strengths of deep learning and machine learning models and taking into account geographical factors, GERPM aims to improve prediction accuracy and provide robust predictions for urban residential rents. The model is evaluated using housing data from Nanjing, a major city in China, and compared with representative individual prediction models, the equal weight combination model, and the ensemble learning model. The experimental results demonstrate that GERPM outperforms other models in terms of prediction performance. Furthermore, the model’s effectiveness and robustness are validated by applying it to other major cities in China, such as Shanghai and Hangzhou. Overall, GERPM shows promising potential in accurately predicting urban residential rents and contributing to the advancement of the rental market.
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Wu, Menglong, Yicheng Ye, Nanyan Hu, Qihu Wang, Huimin Jiang i Wen Li. "EMD-GM-ARMA Model for Mining Safety Production Situation Prediction". Complexity 2020 (8.06.2020): 1–14. http://dx.doi.org/10.1155/2020/1341047.
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In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used to predict high-frequency signal sequence, while the ARMA model was used to predict low-frequency signal sequence. Finally, aiming to predict the mining safety production situation, the EMD-GM-ARMA model was constructed via superimposing the prediction results of each subsequence, thereby compared to the ARIMA model, wavelet neural network model, and PSO-SVM model. The results demonstrated that the EMD-GM-ARMA model and the PSO-SVM model hold the highest prediction accuracy in the short-term prediction, and the wavelet neural network has the lowest prediction accuracy. The PSO-SVM model’s prediction accuracy decreases in medium- and long-term predictions while the EMD-GM-ARMA model still can maintain high prediction accuracy. Moreover, the relative error fluctuations of the EMD-GM-ARMA model are relatively stable in both short-term and medium-term predictions. This shows that the EMD-GM-ARMA model can provide high-precision predictions with high stability, proving the model to be feasible and effective in predicting the mining safety production situation.
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Bierkens, M. F. P., i L. P. H. van Beek. "Seasonal Predictability of European Discharge: NAO and Hydrological Response Time". Journal of Hydrometeorology 10, nr 4 (1.08.2009): 953–68. http://dx.doi.org/10.1175/2009jhm1034.1.
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Abstract In this paper the skill of seasonal prediction of river discharge and how this skill varies between the branches of European rivers across Europe is assessed. A prediction system of seasonal (winter and summer) discharge is evaluated using 1) predictions of the average North Atlantic Oscillation (NAO) index for the coming winter based on May SST anomalies of the North Atlantic; 2) a global-scale hydrological model; and 3) 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data. The skill of seasonal discharge predictions is investigated with a numerical experiment. Also Europe-wide patterns of predictive skill are related to the use of NAO-based seasonal weather prediction, the hydrological properties of the river basin, and a correct assessment of initial hydrological states. These patterns, which are also corroborated by observations, show that in many parts of Europe the skill of predicting winter discharge can, in theory, be quite large. However, this achieved skill mainly comes from knowing the correct initial conditions of the hydrological system (i.e., groundwater, surface water, soil water storage of the basin) rather than from the use of NAO-based seasonal weather prediction. These factors are equally important for predicting subsequent summer discharge.
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Ansah, Kwabena, Ismail Wafaa Denwar i Justice Kwame Appati. "Intelligent Models for Stock Price Prediction". Journal of Information Technology Research 15, nr 1 (styczeń 2022): 1–17. http://dx.doi.org/10.4018/jitr.298616.
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Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price prediction. Aside from predictions, the limitations, and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction is the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.
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Venikar, Isha, Jaai Joshi, Harsh Jalnekar i Shital Raut. "Stock Market Prediction Using LSTM". International Journal for Research in Applied Science and Engineering Technology 10, nr 12 (31.12.2022): 920–24. http://dx.doi.org/10.22214/ijraset.2022.47967.
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Abstract: This study proposes a model which will make use of an LSTM model for predicting stock prices. The stock prices will be predicted on the basis of past information. Stacked LSTM will be employed for the prediction because it utilizes the historic data, therefore, making the predictions more accurate since it is able to learn long term dependencies in data, which makes LSTM an ideal technique for stock market prediction due to its dynamic as well as complex nature. After training the model its accuracy will be checked by using the test data and then using the model the stock prices for the next 30 days will be forecasted using this model.
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Siswanto, Joko, Danny Manongga, Irwan Sembiring i Sutarto Wijono. "Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators". Jurnal Online Informatika 9, nr 1 (23.04.2024): 18–28. http://dx.doi.org/10.15575/join.v9i1.1245.
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The bus public transportation system has low reliability and ability to predict the number of passengers. The accuracy of predicting the number of passengers by public transport bus operators is still weak, which results in failure to implement solutions by operators. A prediction model with LSTM based on deep learning is proposed to predict passengers for 4 bus public transportation operators (Go Bus, New Zealand Bus, Pavlovich, and Ritchies) which are evaluated by MSLE, MAPE, and SMAPE with variations in epoch, batch size, and neurons. The dataset is a CSV performance report on Auckland Transport (AT) New Zealand metro patronage buses (01/01/2019-07/31/2023). The best prediction model was obtained from the lowest evaluation value and relatively fast time at variations of epoch 60, batch size 16, and neurons 32. The prediction results on training and testing data improved with the suitability of the model tuning. The proposed prediction model performs predictions 12 months later for 4 predictions simultaneously with predicted fluctuations occurring simultaneously. Strong negative correlation on New Zealand Bus-Pavlovich, strong positive correlation on Go Bus with Ritchies and Pavlovich. Predictions that are less closely related and dependent are New Zealand Bus against Go Bus, Pavlovich, and Ritchies. The proposed prediction modeling can be used as a basis for creating operator policies and strategies to deal with passenger fluctuations and for the development of new prediction models.
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Shen, Runjie, Ruimin Xing, Yiying Wang, Danqiong Hua i Ming Ma. "Ultra-short-term prediction method of photovoltaic electric field power based on ground-based cloud image segmentation". E3S Web of Conferences 185 (2020): 01052. http://dx.doi.org/10.1051/e3sconf/202018501052.
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As a large number of photovoltaic power stations are built and put into operation, the total amount of photovoltaic power generation accounts for an increasing proportion of the total electricity. The inability to accurately predict solar energy output has brought great uncertainty to the grid. Therefore, predicting the future power of photovoltaic fields is of great significance. According to different time scales, predictions are divided into long-term, medium-term and ultra-short-term predictions. The main difficulty of ultra-short-term forecasting lies in the power fluctuations caused by sudden and drastic changes in environmental factors. The shading of clouds is directly related to the irradiance received on the surface of the photovoltaic panel, which has become the main factor affecting the fluctuation of photovoltaic power generation. Therefore, sky images captured by conventional cameras installed near solar panels can be used to analyze cloud characteristics and improve the accuracy of ultra-short-term predictions. This paper uses historical power information of photovoltaic power plants and cloud image data, combined with machine learning methods, to provide ultra-short-term predictions of the power generation of photovoltaic power plants. First, the random forest method is used to use historical power generation data to establish a single time series prediction model to predict ultra-short-term power generation. Compared with the continuous model, the root mean square (RMSE) error of prediction is reduced by 28.38%. Secondly, the Unet network is used to segment the cloud image, and the cloud amount information is analyzed and input into the random forest prediction model to obtain the bivariate prediction model. The experimental results prove that, based on the cloud amount information contained in the cloud chart, the bivariate prediction model has an 11.56% increase in prediction accuracy compared with the single time series prediction model, and an increase of 36.66% compared with the continuous model.
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Kumari, Sweta, Syed Shahid Raza, Gopal Arora i Shambhu Bharadwaj. "Exploring machine learning in the context of environmental usage prediction". Multidisciplinary Science Journal 6 (26.07.2024): 2024ss0503. http://dx.doi.org/10.31893/multiscience.2024ss0503.
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The use of environmental prediction refers to predicting the impact that human activity will have on ecosystems, natural resources and other environmental factors in the future. This strategy looks at historical patterns, present situations and future predictions to hypothesize about the ecological effects of human activities, climate change and other factors. This research suggests machine learning(ML) techniques to predict environmental uses. Prediction accuracy declinesover time and models face challenges due to the need for observable data integration in sectors like agriculture, energy and waterfor successful sub-seasonal predictions. To tackle these issues, we proposed a Next Generation Bumble Bee Mating Optimized Naïve Bayes Algorithm (NGBBMO-NBA) method that is used to enhance weather prediction. The research gathers the SSF dataset to make predictions on the usage of the environment. We use a min-max normalization approach for data preprocessing. The principalcomponent analysis (PCA) method extracts features from the SSF data. Environmental uncertainty inhibits sub-seasonal projections. Our suggested method, NGBBMO-NBA, surpasses the current techniques for ecological prediction in terms of energy consumption (96.5%), F1-Score (96%), Mean Absolute Error (MAE) (97) and Root Mean Square Error (RMSE) (98.5).
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Singh, Arpita, Basar Imam Mazhari i Karan Gupta. "Stock Market Prediction and Visualisation". International Journal for Research in Applied Science and Engineering Technology 12, nr 4 (30.04.2024): 4460–67. http://dx.doi.org/10.22214/ijraset.2024.61019.
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Abstract: Stock price forecasting is a widely discussed and significant topic in both financial and academic circles. The stock market is inherently unpredictable, lacking clear rules for estimating or predicting share prices. Various methods, including technical analysis, fundamental analysis, time series analysis, and statistical analysis, have been employed to forecast stock prices. However, none of these methods consistently serve as reliable prediction tools. In this paper, we explore the implementation, prediction, and analysis of stock market prices. Artificial Neural Networks and Machine Learning prove effective for forecasting stock prices, returns, and modeling stock behavior. By conducting statistical analyses, we establish relationships between selected factors and share prices, contributing to more accurate predictions. While the stock market remains inherently uncertain, this paper aims to apply data analysis and prediction concepts to forecast stock prices. In the era of global digitization, stock market prediction has undergone significant technological advancements, transforming traditional trading models. As market capitalization continues to rise, stock trading becomes a focal point for financial investors. Analysts and researchers have developed tools and techniques to predict stock price movements, aiding decision-making. Advanced models leverage non-traditional textual data from social platforms for market prediction. Machine learning approaches, including text data analytics and ensemble methods, have significantly improved prediction accuracy. However, analyzing and predicting stock markets remains challenging due to dynamic, erratic, and chaotic data. This study explores machine learning-based approaches for stock market prediction, emphasizing a generic framework. By critically analyzing findings from the last decade (2011–2021) from digital libraries like ACM and Scopus, we provide insights for emerging researchers to delve into this promising area.
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Bakhtiari, Shahab, Sadegh Sulaimany, Mehrdad Talebi i Kabmiz Kalhor. "Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships". Cancer Informatics 19 (styczeń 2020): 117693512094221. http://dx.doi.org/10.1177/1176935120942216.
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Genetic variations such as single nucleotide polymorphisms (SNPs) can cause susceptibility to cancer. Although thousands of genetic variants have been identified to be associated with different cancers, the molecular mechanisms of cancer remain unknown. There is not a particular dataset of relationships between cancer and SNPs, as a bipartite network, for computational analysis and prediction. Link prediction as a computational graph analysis method can help us to gain new insight into the network. In this article, after creating a network between cancer and SNPs using SNPedia and Cancer Research UK databases, we evaluated the computational link prediction methods to foresee new SNP-Cancer relationships. Results show that among the popular scoring methods based on network topology, for relation prediction, the preferential attachment (PA) algorithm is the most robust method according to computational and experimental evidence, and some of its computational predictions are corroborated in recent publications. According to the PA predictions, rs1801394-Non-small cell lung cancer, rs4880-Non-small cell lung cancer, and rs1805794-Colorectal cancer are some of the best probable SNP-Cancer associations that have not yet been mentioned in any published article, and they are the most probable candidates for additional laboratory and validation studies. Also, it is feasible to improve the predicting algorithms to produce new predictions in the future.
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Lertampaiporn, Supatcha, Sirapop Nuannimnoi, Tayvich Vorapreeda, Nipa Chokesajjawatee, Wonnop Visessanguan i Chinae Thammarongtham. "PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins". BioMed Research International 2019 (19.11.2019): 1–11. http://dx.doi.org/10.1155/2019/5617153.
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Several computational approaches for predicting subcellular localization have been developed and proposed. These approaches provide diverse performance because of their different combinations of protein features, training datasets, training strategies, and computational machine learning algorithms. In some cases, these tools may yield inconsistent and conflicting prediction results. It is important to consider such conflicting or contradictory predictions from multiple prediction programs during protein annotation, especially in the case of a multiclass classification problem such as subcellular localization. Hence, to address this issue, this work proposes the use of the particle swarm optimization (PSO) algorithm to combine the prediction outputs from multiple different subcellular localization predictors with the aim of integrating diverse prediction models to enhance the final predictions. Herein, we present PSO-LocBact, a consensus classifier based on PSO that can be used to combine the strengths of several preexisting protein localization predictors specially designed for bacteria. Our experimental results indicate that the proposed method can resolve inconsistency problems in subcellular localization prediction for both Gram-negative and Gram-positive bacterial proteins. The average accuracy achieved on each test dataset is over 98%, higher than that achieved with any individual predictor.
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An, Zhongqi. "Real-Time Football Match Prediction Platform". ITM Web of Conferences 70 (2025): 04003. https://doi.org/10.1051/itmconf/20257004003.
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The integration of real-time data into sports analytics has significantly enhanced the accuracy of football match predictions, which is vital for team management, tactical planning, and commercial applications *such as sports betting. This paper presents a Python-based platform for predicting football match outcomes by collecting and processing real-time data from the SofaScore website. The platform employs machine learning models, including Random Forest, Support Vector Machines (SVM), and Neural Networks, combined with feature engineering techniques, to generate accurate predictions. A user-friendly interface is also developed to facilitate easy access and analysis of this data. The platform’s real-time data updating mechanism ensures prediction accuracy, while the integration of multiple models through a Stacking method further enhances reliability. The platform’s innovative design addresses key challenges in sports analytics by providing a robust tool for data-driven decision-making. Future work will focus on enhancing model algorithms and incorporating more complex data sources, such as social media sentiment analysis, to further improve prediction accuracy.
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Drisya, G. V., D. C. Kiplangat, K. Asokan i K. Satheesh Kumar. "Deterministic prediction of surface wind speed variations". Annales Geophysicae 32, nr 11 (19.11.2014): 1415–25. http://dx.doi.org/10.5194/angeo-32-1415-2014.
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Abstract. Accurate prediction of wind speed is an important aspect of various tasks related to wind energy management such as wind turbine predictive control and wind power scheduling. The most typical characteristic of wind speed data is its persistent temporal variations. Most of the techniques reported in the literature for prediction of wind speed and power are based on statistical methods or probabilistic distribution of wind speed data. In this paper we demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind dynamics exhibit chaotic behaviour. The predictions are remarkably accurate up to 1 h with a normalised RMSE (root mean square error) of less than 0.02 and reasonably accurate up to 3 h with an error of less than 0.06. Repeated application of these methods at 234 different geographical locations for predicting wind speeds at 30-day intervals for 3 years reveals that the accuracy of prediction is more or less the same across all locations and time periods. Comparison of the results with f-ARIMA model predictions shows that the deterministic models with suitable parameters are capable of returning improved prediction accuracy and capturing the dynamical variations of the actual time series more faithfully. These methods are simple and computationally efficient and require only records of past data for making short-term wind speed forecasts within practically tolerable margin of errors.
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Hu, Min, i Peng Cheng. "Long-Distance Shield Tunnelling Performance Prediction Based on Informer". Applied Sciences 15, nr 3 (6.02.2025): 1674. https://doi.org/10.3390/app15031674.
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Shield performance prediction plays a critical role in construction decision-making. However, current models suffer from significant performance degradation in long-distance prediction. To address this gap, we propose a novel Long-Distance Shield Performance Prediction model (LSPP), which leverages the long-term prediction capabilities of Informer. The LSPP model incorporates conventional monitoring data, tunnelling parameters, and stratigraphic spatial information and is optimized using a ProbSparse self-attention mechanism and dynamic decoding techniques. A series of experiments demonstrate that LSPP significantly outperforms traditional models, such as LSTM and GRUs, particularly in long-distance predictions and under conditions of stratigraphic changes. Notably, the model achieves an R2 of 0.82 when predicting penetration after six rings, making it highly accurate and stable for engineering decision-making.
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Soewono, Eddy Bambang, Maisevli Harika, Cahya Ramadhan i Muhammad Reyhan Soeharto. "Model ARIMA Terbaik Prediksi Latitude dan Longitude Kegiatan Kapal Imigran Ilegal". JURNAL MEDIA INFORMATIKA BUDIDARMA 5, nr 4 (26.10.2021): 1729. http://dx.doi.org/10.30865/mib.v5i4.3301.
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The migration of a person to another country without following the law is illegal immigration. Many problems are caused by this activity, ranging from population problems to increased crime. Predicting the emergence of ships carrying illegal immigrants can assist border patrols in planning patrols to planning defense equipment. Time series forecasting to predict the latitude and longitude of boats carrying illegal immigrants is the Autoregressive Integrated Moving Average (ARIMA) model. The case studies for this research are the Straits of Malacca and the Riau Islands. The prediction range is from one to four weeks to find the model with the smallest error. The ARIMA model for one-week prediction distance succeeded in obtaining the smallest RMSE. However, the smallest RMSE result (0.28730) was obtained for a four-week prediction distance with ARIMA model parameters (4,0,2) for longitude prediction. Meanwhile, the prediction of latitude. The best model is ARIMA (4,0,1), with an RMSE of 0.11457. For latitude and longitude predictions in the Riau Islands, the best models are ARIMA (3,0,0) with RMSE of 0.009074 and ARIMA (2,0,0) with RMSE 0.045815. Based on this study, the ARIMA model is suitable for predicting latitude and longitude data with a short prediction distance (one week)
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Shen, Yancen, Xiang Wang, Yixin Xie, Wei Wang i Rui Zhang. "Uncertainty Quantification Method for Trend Prediction of Oil Well Time Series Data Based on SDMI Loss Function". Processes 12, nr 12 (23.11.2024): 2642. http://dx.doi.org/10.3390/pr12122642.
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IoT sensors in oilfields gather real-time data sequences from oil wells. Accurate trend predictions of these data are crucial for production optimization and failure forecasting. However, oil well time series data exhibit strong nonlinearity, requiring not only precise trend prediction but also the estimation of uncertainty intervals. This paper first proposed a data denoising method based on Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) to reduce the noise present in oil well time series data. Subsequently, an SDMI loss function was introduced, combining the respective advantages of Soft Dynamic Time Warping and Mean Squared Error (MSE). The loss function additionally accepts the upper and lower bounds of the uncertainty prediction interval as input and is optimized with the prediction sequence. By predicting the data of the next 48 data points, the prediction results using the SDMI loss function and the existing three common loss functions are compared on multiple data sets. The prediction results before and after data denoising are compared and the results of predicting the uncertainty interval are shown. The experimental results demonstrate that the average coverage rate of the predicted uncertainty intervals across data from seven wells is 81.4%, and the prediction results accurately reflect the trends in real data.