Relevant bibliographies by topics / Prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 March 2025

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Journal articles on the topic "Prediction"

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Burbey, Ingrid, and Thomas L. Martin. "A survey on predicting personal mobility." International Journal of Pervasive Computing and Communications 8, no. 1 (March 30, 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, no. 8 (August 12, 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, and Mykhailo Granchak. "M&A PREDICTIONS: RECONSIDERING THEIR VALUE, END-USERS, AND METHODOLOGIES." Actual Problems of International Relations, no. 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, and Yong Yi Yao. "EPAK: A Computational Intelligence Model for 2-level Prediction of Stock Indices." International Journal of Computers Communications & Control 13, no. 2 (April 13, 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, and Xiang Zhao. "A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years." Journal of Marine Science and Engineering 12, no. 1 (January 13, 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, and Hyunchul Ahn. "E-Learning at-Risk Group Prediction Considering the Semester and Realistic Factors." Education Sciences 13, no. 11 (November 13, 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, and Min Xia. "GCN–Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting." Applied Sciences 14, no. 5 (March 5, 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, and Robert Holte. "Improved Prediction of IDA*'s Performance via Epsilon-Truncation." Proceedings of the International Symposium on Combinatorial Search 2, no. 1 (August 19, 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 (September 7, 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, and 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, no. 4 (October 14, 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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Dissertations / Theses on the topic "Prediction"

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Carrión, Brännström Robin. "Aggregating predictions using Non-Disclosed Conformal Prediction." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385098.

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When data are stored in different locations and pooling of such data is not allowed, there is an informational loss when doing predictive modeling. In this thesis, a new method called Non-Disclosed Conformal Prediction (NDCP) is adapted into a regression setting, such that predictions and prediction intervals can be aggregated from different data sources without interchanging any data. The method is built upon the Conformal Prediction framework, which produces predictions with confidence measures on top of any machine learning method. The method is evaluated on regression benchmark data sets using Support Vector Regression, with different sizes and settings for the data sources, to simulate real life scenarios. The results show that the method produces conservatively valid prediction intervals even though in some settings, the individual data sources do not manage to create valid intervals. NDCP also creates more stable intervals than the individual data sources. Thanks to its straightforward implementation, data owners which cannot share data but would like to contribute to predictive modeling, would benefit from using this method.
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Miller, Mark Daniel. "Entangled predictive brain : emotion, prediction and embodied cognition." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33218.

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How does the living body impact, and perhaps even help constitute, the thinking, reasoning, feeling agent? This is the guiding question that the following work seeks to answer. The subtitle of this project is emotion, prediction and embodied cognition for good reason: these are the three closely related themes that tie together the various chapters of the following thesis. The central claim is that a better understanding of the nature of emotion offers valuable insight for understanding the nature of the so called 'predictive mind', including a powerful new way to think about the mind as embodied Recently a new perspective has arguably taken the pole position in both philosophy of mind and the cognitive sciences when it comes to discussing the nature of mind. This framework takes the brain to be a probabilistic prediction engine. Such engines, so the framework proposes, are dedicated to the task of minimizing the disparity between how they expect the world to be and how the world actually is. Part of the power of the framework is the elegant suggestion that much of what we take to be central to human intelligence - perception, action, emotion, learning and language - can be understood within the framework of prediction and error reduction. In what follows I will refer to this general approach to understanding the mind and brain as 'predictive processing'. While the predictive processing framework is in many ways revolutionary, there is a tendency for researchers interested in this topic to assume a very traditional 'neurocentric' stance concerning the mind. I argue that this neurocentric stance is completely optional, and that a focus on emotional processing provides good reasons to think that the predictive mind is also a deeply embodied mind. The result is a way of understanding the predictive brain that allows the body and the surrounding environment to make a robust constitutive contribution to the predictive process. While it's true that predictive models can get us a long way in making sense of what drives the neural-economy, I will argue that a complete picture of human intelligence requires us to also explore the many ways that a predictive brain is embodied in a living body and embedded in the social-cultural world in which it was born and lives.
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Björsell, Joachim. "Long Range Channel Predictions for Broadband Systems : Predictor antenna experiments and interpolation of Kalman predictions." Thesis, Uppsala universitet, Signaler och System, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-281058.

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The field of wireless communication is under massive development and the demands on the cellular system, especially, are constantly increasing as the utilizing devices are increasing in number and diversity. A key component of wireless communication is the knowledge of the channel, i.e, how the signal is affected when sent over the wireless medium. Channel prediction is one concept which can improve current techniques or enable new ones in order to increase the performance of the cellular system. Firstly, this report will investigate the concept of a predictor antenna on new, extensive measurements which represent many different environments and scenarios. A predictor antenna is a separate antenna that is placed in front of the main antenna on the roof of a vehicle. The predictor antenna could enable good channel prediction for high velocity vehicles. The measurements show to be too noisy to be used directly in the predictor antenna concept but show potential if the measurements can be noise-filtered without distorting the signal. The use of low-pass filter and Kalman filter to do this, did not give the desired results but the technique to do this should be further investigated. Secondly, a interpolation technique will be presented which utilizes predictions with different prediction horizon by estimating intermediate channel components using interpolation. This could save channel feedback resources as well as give a better robustness to bad channel predictions by letting fresh, local, channel predictions be used as quality reference of the interpolated channel estimates. For a linear interpolation between 8-step and 18-step Kalman predictions with Normalized Mean Square Error (NMSE) of -15.02 dB and -10.88 dB, the interpolated estimates had an average NMSE of -13.14 dB, while lowering the required feedback data by about 80 %. The use of a warning algorithm reduced the NMSE by a further 0.2 dB. It mainly eliminated the largest prediction error which otherwise could lead to retransmission, which is not desired.
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Bramlet, John. "Earthquake prediction and earthquake damage prediction /." Connect to resource, 1996. http://hdl.handle.net/1811/31764.

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Greco, Antonino. "The role of task relevance in the modulation of brain dynamics during sensory predictions." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/307050.

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Associative learning is a fundamental ability biological systems possess in order to adapt to a nonstationary environment. One of the core aspects of associative learning theoretical frameworks is that surprising events drive learning by signalling the need to update the system’s beliefs about the probability structure governing stimuli associations. Specifically, the central neural system generates internal predictions to anticipate the causes of its perceptual experience and compute a prediction error to update its generative model of the environment, an idea generally known as the predictive coding framework. However, it is not clear whether the brain generates these predictions only for goal-oriented behavior or they are more a general characteristic of the brain function. In this thesis, I explored the role of task relevance in modulating brain activity when exposed to sensory associative learning task. In the first study, participants were asked to perform a perceptual detection task while audio-visual stimuli were presented as distractors. These distractors possessed a probability structure that made some of them more paired than others. Results showed that occipital activity triggered by the conditioned stimulus was elicited just before the arrival of the unconditioned visual stimulus. Moreover, occipital activity after the onset of the unconditioned stimulus followed a pattern of precision-weighted prediction errors. In the second study, two more sessions were added to the task in the previous study in which the probability structure for all stimuli associations was identical and the whole experiment was spanned in six days across two weeks. Results showed a difference in the modulation of the beta band induced by the presentation of the unconditioned stimulus preceded by the predictive and unpredictive conditioned auditory stimuli by comparing the pre and post sessions activity. In the third study, participants were exposed to a similar task with respect to the second study with the modification that there was a condition in which the conditioned-unconditioned stimulus association was task-relevant, thus allowing to directly compare task-relevant and task-irrelevant associations. Results showed that both types of associations had similar patterns in terms of activity and functional connectivity when comparing the brain responses to the onset of the unconditioned visual stimulus. Taken together, these findings demonstrate irrelevant associations rely on the same neural mechanisms of relevant ones. Thus, even if task relevance plays a modulatory role on the strength of the neural effects of associative learning, predictive processes take place in sensory associative learning regardless of task relevance.
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Kock, Peter. "Prediction and predictive control for economic optimisation of vehicle operation." Thesis, Kingston University, 2013. http://eprints.kingston.ac.uk/35861/.

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Truck manufacturers are currently under pressure to reduce pollution and cost of transportation. The cost efficient way to reduce CO[sub]2 and cost is to reduce fuel consumption by adaptation of the vehicle speed to the driving conditions - by heuristic knowledge or mathematical optimisation. Due to their experience, professional drivers are capable of driving with great efficiency in terms of fuel consumption. The key research question addressed in this work is the comparison of the fuel efficiency for an unassisted drive by an experienced professional driver versus an enhanced drive using driver assistance system. The motivation for this is based on the advantage of such a system in terms of price (lower than driver's training) but potentially it can be challenging to obtain drivers' acceptance of the system. There is a range of fundamental issued that have to be addressed prior to the design and implementation of the driver assistance system. The first issue is related to the evaluation of the correctness of the prediction model under development, due to a range of inaccuracies introduced by slope errors in digital maps, imprecise modelling of combustion engine, vehicle physics etc. The second issue is related to the challenge in selecting a suitable method for optimisation of mixed integer non-linear systems. Dynamic Programming proved to be very suitable for this work and some methods of search space reduction are presented here. Also an analytical solution of the Bernoulli differential equation of the vehicle dynamics is presented and used here in order to reduce computing effort. Extensive simulation and driving tests were performed using different driving approaches to compare well trained human experts with a range of different driving assistance systems based on standard cruise control, heuristic and mathematical optimisation. Finally the acceptance of the systems by drivers been evaluated.
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Andeta, Jemal Ahmed. "Road-traffic accident prediction model : Predicting the Number of Casualties." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20146.

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Efficient and effective road traffic prediction and management techniques are crucial in intelligent transportation systems. It can positively influence road advancement, safety enhancement, regulation formulation, and route planning to save living things in advance from road traffic accidents. This thesis considers road safety by predicting the number of casualties if an accident occurs using multiple traffic accident attributes. It helps individuals (drivers) or traffic offices to adjust and control their contributions for the occurrence of an accident before emerging it. Three candidate algorithms from different regression fit patterns are proposed and evaluated to conduct the thesis: the bagging, linear, and non-linear fitting patterns. The gradient boosting machines (GBoost) from the bagging, Linearsupport vector regression (LinearSVR) from the linear, and extreme learning machines (ELM) also from the non-linear side are the selected algorithms. RMSE and MAE performance evaluation metrics are applied to evaluate the models. The GBoost achieved a better performance than the other two with a low error rate and minimum prediction interval value for 95% prediction interval. A SHAP (SHapley Additive exPlanations) interpretation technique is applied to interpret each model at the global interpretation level using SHAP’s beeswarm plots. Finally, suggestions for future improvements are presented via the dataset and hyperparameter tuning.
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Peterson, Ashley Thomas. "Cavitation prediction." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612813.

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Åkermark, Alexander, and Mattias Hallefält. "Churn Prediction." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-41236.

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Churn analysis is an important tool for companies as it can reduce the costs that are related to customer churn. Churn prediction is the process of identifying users before they churn, this is done by implementing methods on collected data in order to find patterns that can be helpful when predicting new churners in the future.The objective of this report is to identify churners with the use of surveys collected from different golfclubs, their members and guests. This was accomplished by testing several different supervised machine learning algorithms in order to find the different classes and to see which supervised algorithms are most suitable for this kind of data.The margin of success was to have a greater accuracy than the percentage of major class in the datasetThe data was processed using label encoding, ONE-hot encoding and principal component analysis and was split into 10 folds, 9 training folds and 1 testing fold ensuring cross validation when iterated 10 times rearranging the test and training folds. Each algorithm processed the training data to create a classifier which was tested on the test data.The classifiers used for the project was K nearest neighbours, Support vector machine, multi-layer perceptron, decision trees and random forest.The different classifiers generally had an accuracy of around 72% and the best classifier which was random forest had an accuracy of 75%. All the classifiers had an accuracy above the margin of success.K-folding, confusion-matrices, classification report and other internal crossvalidation techniques were performed on the the data to ensure the quality of the classifier.The project was a success although there is a strong belief that the bottleneck for the project was the quality of the data in terms of new legislation when collecting and storing data that results in redundant and faulty data.
Churn analys är ett viktigt verktyg för företag då det kan reducera kostnaderna som är relaterade till kund churn. Churn prognoser är processen av att identifiera användare innan de churnas, detta är gjort med implementering av metoder på samlad data för att hitta mönster som är hjälpsamma när framtida användare ska prognoseras. Objektivet med denna rapport är att identifiera churnare med användning av enkäter samlade från golfklubbar och deras kunder och gäster. Det är uppnå att igenom att testa flera olika kontrollerade maskinlärnings algoritmer för att jämföra vilken algoritm som passar bäst. Felmarginalen uppgick till att ha en större träffsäkerhet än procenthalten av den dominanta klassen i datasetet. Datan behandlades med label encoding, ONE-hot encoding och principial komponent analys och delades upp i 10 delar, 9 träning och 1 test del för att säkerställa korsvalidering. Varje algoritm behandlade träningsdatan för att skapa att klassifierare som sedan testades på test datan. Klassifierarna som användes för projekted innefattar K nearest neighbours, Support vector machine, multi-layer perceptron, decision trees och random forest. De olika klassifierarna hade en generell träffssäkerhet omkring 72%, där den bästa var random forest med en träffssäkerhet på 75%. Alla klassifierare hade en träffsäkerhet än den felmarginal som st¨alldes. K-folding, confusion matrices, classification report och andra interna korsvaliderings tekniker användes för att säkerställa kvaliteten på klassifieraren. Projektet var lyckat, men det finns misstanke om att flaskhalsen för projektet låg inom kvaliteten på datan med hänsyn på villkor för ny lagstiftning vid insamling och lagring av data som leder till överflödiga och felaktiga uppgifter.
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Jahedpari, Fatemeh. "Artificial prediction markets for online prediction of continuous variables." Thesis, University of Bath, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.690730.

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In this dissertation, we propose an online machine learning technique – named Artificial Continuous Prediction Market (ACPM) – to predict the value of a continuous variable by (i) integrating a set of data streams from heterogeneous sources with time varying compositions such as changing the quality of data streams, (ii) integrating the results of several analysis models for each data source when the most suitable model for a given data source is not known a priori, (iii) dynamically weighting the prediction of each analysis model and data source to form the system prediction. We adapt the concept of prediction market, motivated by their success in forecasting accurately the outcome of many events [Nikolova and Sami, 2007]. Our proposed model instantiates a sequence of prediction markets in which artificial agents play the role of market participants. Agents participate in the markets with the objective of increasing their own utility and hence indirectly cause the markets to aggregate their knowledge. Each market is run in a number of rounds in which agents have the opportunity to send their prediction and bet to the market. At the end of each round, the aggregated prediction of the crowd is announced to all agents, which provides a signal to agents about the private information of other agents so they can adjust their beliefs accordingly. Once the true value of the record is known, agents are rewarded according to accuracy of their prediction. Using this information, agents update their models and knowledge, with the aim of improving their performance in future markets. This thesis proposes two trading strategies to be utilised by agents when participating in a market. While the first one is a naive constant strategy, the second one is an adaptive strategy based on Q-Learning technique [Watkins, 1989]. We evaluate the performance of our model in different situations using real-world and synthetic data sets. Our results suggest that ACPM: i) is either better or very close to the best performing agents, ii) is resilient to the addition of agents with low performance, iii) outperforms many well-known machine learning models, iv) is resilient to quality drop-out in the best performing agents, v) adapts to changes in quality of agents predictions.
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Books on the topic "Prediction"

1

Kanjilal, P. P. Adaptive prediction and predictive control. Stevenage, Herts., U.K: P. Peregrinus on behalf of Institution of Electrical Engineers, 1995.

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Engineers, Institution of Electrical, ed. Adaptive prediction and predictive control. Stevenage: P. Peregrinus on behalf of Institution of Electrical Engineers, 1995.

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Manski, Charles F. Interpreting the predictions of prediction markets. Cambridge, MA: National Bureau of Economic Research, 2004.

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Ma, Zongjin, Zhengxiang Fu, Yingzhen Zhang, Chengmin Wang, Guomin Zhang, and Defu Liu. Earthquake Prediction. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-61269-5.

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Peijnenburg, Willie J. G. M., and Jirí Damborský, eds. Biodegradability Prediction. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-5686-8.

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Kollmar, Martin, ed. Gene Prediction. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9173-0.

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Shimazaki, Kunihiko, and William Stuart, eds. Earthquake Prediction. Basel: Birkhäuser Basel, 1985. http://dx.doi.org/10.1007/978-3-0348-6245-5.

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Wyatt, Ray. Plan Prediction. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46430-5.

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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. Prediction Markets. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5.

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Wolfers, Justin. Prediction markets. Cambridge, MA: National Bureau of Economic Research, 2004.

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Book chapters on the topic "Prediction"

1

Pourbafrani, Mahsa, Shreya Kar, Sebastian Kaiser, and Wil M. P. van der Aalst. "Remaining Time Prediction for Processes with Inter-case Dynamics." In Lecture Notes in Business Information Processing, 140–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_11.

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AbstractProcess mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that process states, e.g., the number of running instances, influence the remaining time of a single process instance. Recent approaches improve the quality of RTMs by utilizing process context related to batching-at-end inter-case dynamics in the process, e.g., using the time to batching as a feature. We propose an approach that decreases the previous approaches’ reliance on user knowledge for discovering fine-grained process behavior. Furthermore, we enrich our RTMs with the extracted features for multiple performance patterns (caused by inter-case dynamics), which increases the interpretability of models. We assess our proposed remaining time prediction method using two real-world event logs. Incorporating the created inter-case features into RTMs results in more accurate and interpretable predictions.
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Fani Sani, Mohammadreza, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, and Wil M. P. van der Aalst. "Event Log Sampling for Predictive Monitoring." In Lecture Notes in Business Information Processing, 154–66. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_12.

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AbstractPredictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy.
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Liu, Wendi, Léan E. Garland, Jesus Ochoa, and Michael J. Pyrcz. "A Geostatistical Heterogeneity Metric for Spatial Feature Engineering." In Springer Proceedings in Earth and Environmental Sciences, 3–19. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_1.

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AbstractHeterogeneity is a vital spatial feature for subsurface resource recovery predictions, such as mining grade tonnage functions, hydrocarbon recovery factor, and water aquifer draw-down predictions. Feature engineering presents the opportunity to integrate heterogeneity information, but traditional heterogeneity engineered features like Dykstra-Parsons and Lorenz coefficients ignore the spatial context; therefore, are not sufficient to quantify the heterogeneity over multiple scales of spatial intervals to inform predictive machine learning models. We propose a novel use of dispersion variance as a spatial-engineered feature that accounts for heterogeneity within the spatial context, including spatial continuity and sample data and model volume support size to improve predictive machine-learning-based models, e.g., for pre-drill prediction and uncertainty quantification. Dispersion variance is a generalized form of variance that accounts for volume support size and can be calculated from the semivariogram-based spatial continuity model. We demonstrate dispersion variance as a useful predictor feature for the case of hydrocarbon recovery prediction, with the ability to quantify the spatial variation over the support size of the production well drainage radius, given the spatial continuity from the variogram and trajectory of the well. We include a synthetic example based on geostatistical models and flow simulation to show the sensitivity of dispersion variance to production. Then we demonstrate the dispersion variance as an informative predictor feature for production forecasting with a field case study in the Duvernay formation.
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Spenrath, Yorick, Marwan Hassani, and Boudewijn F. van Dongen. "Online Prediction of Aggregated Retailer Consumer Behaviour." In Lecture Notes in Business Information Processing, 211–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_16.

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AbstractPredicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.
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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. "Introduction." In Prediction Markets, 1–5. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_1.

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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. "Fundamentals of Prediction Markets." In Prediction Markets, 6–10. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_2.

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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. "Key Design Elements of Prediction Markets." In Prediction Markets, 11–47. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_3.

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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. "Applications of Prediction Markets." In Prediction Markets, 48–117. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_4.

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Luckner, Stefan, Jan Schröder, Christian Slamka, Markus Franke, Andreas Geyer-Schulz, Bernd Skiera, Martin Spann, and Christof Weinhardt. "Conclusion." In Prediction Markets, 118–19. Wiesbaden: Gabler Verlag, 2012. http://dx.doi.org/10.1007/978-3-8349-7085-5_5.

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Peijnenburg, W. J. G. M., and J. Damborský. "Introduction, Main Conclusions and Recommendations of The Workshop “QSAR Biodegradation II”." In Biodegradability Prediction, 1–5. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-5686-8_1.

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Conference papers on the topic "Prediction"

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Deshwal, Aryan, Janardhan Rao Doppa, and Dan Roth. "Learning and Inference for Structured Prediction: A Unifying Perspective." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/878.

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In a structured prediction problem, one needs to learn a predictor that, given a structured input, produces a structured object, such as a sequence, tree, or clustering output. Prototypical structured prediction tasks include part-of-speech tagging (predicting POS tag sequence for an input sentence) and semantic segmentation of images (predicting semantic labels for pixels of an input image). Unlike simple classification problems, here there is a need to assign values to multiple output variables accounting for the dependencies between them. Consequently, the prediction step itself (aka ``inference" or ``decoding") is computationally-expensive, and so is the learning process, that typically requires making predictions as part of it. The key learning and inference challenge is due to the exponential size of the structured output space and depend on its complexity. In this paper, we present a unifying perspective of the different frameworks that address structured prediction problems and compare them in terms of their strengths and weaknesses. We also discuss important research directions including integration of deep learning advances into structured prediction, and learning from weakly supervised signals and active querying to overcome the challenges of building structured predictors from small amount of labeled data.
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Jørgensen, Magne, and Bjørn Faugli. "Prediction of Overoptimistic Predictions." In 10th International Conference on Evaluation and Assessment in Software Engineering (EASE). BCS Learning & Development, 2006. http://dx.doi.org/10.14236/ewic/ease2006.5.

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Jakša, Rudolf, Martina Zeleňáková, Juraj Koščák, and Helena Hlavatá. "Local Prediction of Precipitation Based on Neural Network." In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.079.

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The paper is focused on analysis of local neural network model of precipitation. We use basic multilayer perceptron neural network with the time-window on input data to predict the precipitation. We predict the precipitation in the next day from the local meteorological data from past days. Data from the past 60 years were used to train the predictor. Obtained prediction model is specific for given area of Košice City in Slovakia, as the prediction is based on the statistics of the weather in given area. This precipitation predictor is multiple-input-single-output architecture with a single value per day resolution on output. Obtained results show that good local temperature prediction accuracy is possible with chosen setup, but it is worse for the precipitation prediction. Also the training requirements of precipitation predictor seem to be significantly higher then for the temperature predictor. Obtained prediction results can be used for applications based on local meteorological station data, although they are not as accurate as the state of art agency predictions based on satellite data. In the paper we will analyze design of the precipitation predictor based on existing design of the temperature predictor and provide the reader with recommended setup of such predictor for application with his/her local precipitation data.
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Dhakksinesh, A., Olivia R. Katherine, and V. S. Pooja. "Crime Analysis and Prediction Based on Machine Learning Algorithm." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-y21866.

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Crime prediction is a unique approach to identify and to find pattern trends of crime. Prediction means, using analysis and learning techniques, to find predictive actions of a specific activity and this is found to be effective in doing predictive analysis for various tasks such as crime prediction. The aim of this paper is to implement an approach for the problem in predicting the number of cases of crime happening in different parts of India. During the research we considered the machine learning model Random Forest and used the same for the prediction for crime. The prediction metrics used in this model are taken from feature selection technique. This technique increases the efficiency and accuracy of the prediction and also to avoid the model from over fitting. This model was tested on the crime data of India.
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Vaisband, Inna, and Eby G. Friedman. "Power Network-on-Chip for Scalable Power Delivery." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633949.

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Zhang, Xiang, Jingwei Lu, Yang Liu, and Chung-Kuan Cheng. "Worst-Case Noise Area Prediction of On-Chip Power Distribution Network." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633950.

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Zhou, Nancy Y., Phillip Restle, Joseph Palumbo, Joseph Kozhaya, Haifeng Qian, Zhuo Li, Charles J. Alpert, and Cliff Sze. "PACMAN." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633951.

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Huang, Tsung-Wei, Pei-Ci Wu, and Martin D. F. Wong. "UI-Route." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633952.

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Kemmerer, Julian, and Baris Taskin. "Range-based Dynamic Routing of Hierarchical On Chip Network Traffic." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633953.

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Chan, Wei-Ting Jonas, Andrew B. Kahng, and Siddhartha Nath. "Methodology for Electromigration Signoff in the Presence of Adaptive Voltage Scaling." In SLIP (System Level Interconnect Prediction). New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2633948.2633954.

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Reports on the topic "Prediction"

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Manski, Charles. Interpreting the Predictions of Prediction Markets. Cambridge, MA: National Bureau of Economic Research, March 2004. http://dx.doi.org/10.3386/w10359.

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, December 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered probit model (OPM) were evaluated as statistical models, while random forest (RF) and XGBoost were evaluated as ML models. For DL, multi-layer perceptron (MLP) and TabNet were evaluated. The performance of these models varied across severity levels, with property damage only (PDO) predictions performing the best and severe injury predictions performing the worst. The TabNet model performed best in predicting severe injury and PDO crashes, while RF was the most effective in predicting moderate injury crashes. However, all models struggled with severe injury classification, indicating the potential need for model refinement and exploration of other techniques. Hence, the choice of model depends on the specific application and the relative costs of false negatives and false positives. This conclusion underscores the need for further research in this area to improve the prediction accuracy of severe and moderate injury incidents, ultimately improving available data that can be used to increase road safety.
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Wolfers, Justin, and Eric Zitzewitz. Prediction Markets. Cambridge, MA: National Bureau of Economic Research, May 2004. http://dx.doi.org/10.3386/w10504.

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Rumelhart, D. E., P. G. Skokowski, and B. O. Martin. Word prediction. Office of Scientific and Technical Information (OSTI), May 1995. http://dx.doi.org/10.2172/123254.

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Cerulli, Giovanni. Non-Parametric Regression for Prediction and Scenario Analysis. Instats Inc., 2024. http://dx.doi.org/10.61700/h03w8dvg3h26b767.

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This one-day workshop, led by Giovanni Cerulli from the Research Institute on Sustainable Economic Growth, provides a comprehensive understanding of non-parametric regression for prediction and 'scenario analysis' to project the results of policies and interventions. Participants, ranging from PhD students to professional researchers across various disciplines, will gain practical skills in applying non-parametric regression using Stata, enabling them to make accurate predictions and develop scenarios in their own research.
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Buchanan, Randy, Christina Rinaudo, George Gallarno, and M. Lagarde. Early life-cycle prediction of reliability. Engineer Research and Development Center (U.S.), April 2023. http://dx.doi.org/10.21079/11681/46919.

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The intent of this project is to investigate a variety of approaches for the development of a basic model for the early life-cycle prediction of reliability (pre-Milestone A). The United States Department of Defense (DoD) currently utilizes an acquisition framework in which system development advances through a series of checkpoints known as milestones. Each milestone represents a decision point, with Milestone A being the earliest in the life cycle. At Milestone A, also known as the risk-reduction decision, the DoD evaluates design concepts while also committing funds to the maturation of technologies in an effort to mitigate future risks. Typically, little is known about the particular system to be developed at this point in the acquisition life cycle, but DoD regulations require program man-agers to submit system reliability information (OUSD[A&S] 2015). Traditional reliability predictions, however, require extensive knowledge of the system of interest to produce accurate results. This level of knowledge is unavailable at or before Milestone A, there-fore, there is a need to create models and methodologies for the prediction of system reliability. This report provides an overview of a variety of methods investigated to improve the prediction of early life cycle reliability.
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McKay, M. D. Evaluating prediction uncertainty. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/29432.

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Henney, Carl J., Richard Radick, Donald C. Norquist, Stephen Kahler, Edward Cliver, Richard Altrock, C. N. Arge, Karatholuvu S. Balasubramaniam, and Stephen M. White. Space Weather Prediction. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada612376.

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Bust, Gary S. Mesoscale Ionospheric Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada631417.

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Gates, R. K., G. J. Gibson, and K. K. McLain. Reliability Growth Prediction. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada176128.

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