Journal articles on the topic 'Réseau Long Short-Term Memory (LSTM)'
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1
HARINAIVO, A., H. HAUDUC, and I. TAKACS. "Anticiper l’impact de la météo sur l’influent des stations d’épuration grâce à l’intelligence artificielle." Techniques Sciences Méthodes 3 (March 20, 2023): 33–42. http://dx.doi.org/10.36904/202303033.
Abstract:
Le changement climatique a pour conséquence l’apparition de forts événements pluvieux de plus en plus fréquents, occasionnant de fortes variations de débit et de concentrations à l’influent des stations d’épuration. La connaissance des risques d’orage et des débits potentiels plusieurs heures ou plusieurs jours en avance permettrait d’anticiper les adaptations opérationnelles pour préparer la station et protéger les différents ouvrages des risques de défaillance. Dans cette étude, les données météorologiques (pluies, température, vents, humidités, précipitations…) et l’historique des données d’influent de la station sont utilisés pour entraîner un algorithme d’intelligence artificielle, d’apprentissage automatique et d’apprentissage profond pour prédire les débits entrants sur la station jusqu’à une semaine en avance. Trois jeux de données journalières et horaires, de 1 à 3 ans, sont utilisés pour entraîner un modèle de Forêt aléatoire à 30 arbres, un modèle LSTM (long short-term memory) et un modèle GRU (gate recurrent unit) à trois couches de 100 neurones suivis chacun d’un dropout de 20 % et une couche de sortie entièrement connectée. Les données sont préalablement nettoyées pour supprimer les valeurs aberrantes et sont réparties à 80 % pour les données pour l’apprentissage et 20 % pour les données de test afin d’obtenir des modèles avec les meilleures prédictions. Les algorithmes utilisés dans cette étude sont simples et détectent bien les pics. La durée de l’entraînement sur les données de trois ans se fait en moins de deux minutes pour la Forêt aléatoire et en moins d’une demi-heure pour les réseaux de neurones LSTM et GRU. Les résultats montrent que les données horaires et la prise en compte de l’effet de l’historique par l’utilisation des réseaux de neurones récurrents LSTM et GRU permettent d’obtenir une meilleure prédiction des débits d’influent. Les séries de données plus longues permettent également un meilleur apprentissage des algorithmes et une meilleure prédiction du modèle.
2
Hochreiter, Sepp, and Jürgen Schmidhuber. "Long Short-Term Memory." Neural Computation 9, no. 8 (November 1, 1997): 1735–80. http://dx.doi.org/10.1162/neco.1997.9.8.1735.
Abstract:
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
3
Septiadi, Jaka, Budi Warsito, and Adi Wibowo. "Human Activity Prediction using Long Short Term Memory." E3S Web of Conferences 202 (2020): 15008. http://dx.doi.org/10.1051/e3sconf/202020215008.
Abstract:
Early symptoms of dementia is one of the causes decrease in quality of life. Human activity recognition (HAR) system is proposed to recognize the daily routines which has an important role in detecting early symptoms of dementia. Long Short Term Memory (LSTM) is very useful for sequence analysis that can find the pattern of activities that carried out in daily routines. However, the LSTM model is slow to achieving convergence and take a long time during training. In this paper, we investigated the sequence of actions recorded in smart home sensors data using LSTM model, then the model will be optimized using several optimization methods. The optimization methods were Stochastic Gradient Descent (SGD), Adagrad, Adadelta, RMSProp, and Adam. The results showed that using Adam to optimized LSTM is better than other optimization methods.
4
Singh, Arjun, Shashi Kant Dargar, Amit Gupta, Ashish Kumar, Atul Kumar Srivastava, Mitali Srivastava, Pradeep Kumar Tiwari, and Mohammad Aman Ullah. "Evolving Long Short-Term Memory Network-Based Text Classification." Computational Intelligence and Neuroscience 2022 (February 21, 2022): 1–11. http://dx.doi.org/10.1155/2022/4725639.
Abstract:
Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.
5
Bhalke, D. G., Daideep Bhingarde, Siddhi Deshmukh, and Digvijay Dhere. "Stock Price Prediction Using Long Short Term Memory." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, Spl-2 issu (June 30, 2022): 271–73. http://dx.doi.org/10.18090/samriddhi.v14spli02.12.
Abstract:
Stock market price prediction is difficult and complex task. Prediction in stock market is very complex and unstable Process. Stock Price are most of the time tend to follow patterns those are more or less regular in stock price curve. Machine Learning techniques use different predictive models and algorithms to predict and automate things to reduce human effort. This research paper focuses on the use of Long Short Term Memory (LSTM) to predict the future stock market company price of stock using each day closing price analysis. LSTM is very helpful in sequential data models. In this paper LSTM algorithm has been used to train and forecast the future stock prices.
6
Zhou, Chenze. "Long Short-term Memory Applied on Amazon's Stock Prediction." Highlights in Science, Engineering and Technology 34 (February 28, 2023): 71–76. http://dx.doi.org/10.54097/hset.v34i.5380.
Abstract:
More and more investors are paying attention to how to use data mining technology into stock investing decisions as a result of the introduction of big data and the quick expansion of financial markets. Machine learning can automatically apply complex mathematical calculations to big data repeatedly and faster. The machine model can analyze all the factors and indicators affecting stock price and achieve high efficiency. Based on the Amazon stock price published on Kaggle, this paper adopts the Long Short-term Memory (LSTM) method for model training. The Keras package in the Python program is used to normalize the data. The Sequence model in Keras establishes a two-layer LSTM network and a three-layer LSTM network to compare and analyze the fitting effect of the model on stock prices. By calculating RMSE and RMPE, the study found that the stock price prediction accuracy of two-layer LSTM is similar to that of three-layer LSTM. In terms of F-measure and Accuracy, the LSTM model of the three-layer network is significantly better than the LSTM model of the two-layer network layer. In general, the LSTM model can accurately predict stock price. Therefore, investors will know the upward or downward trend of stock prices in advance according to the prediction results of the model to make corresponding decisions.
7
Mukhlis, Mukhlis, Aziz Kustiyo, and Aries Suharso. "Peramalan Produksi Pertanian Menggunakan Model Long Short-Term Memory." BINA INSANI ICT JOURNAL 8, no. 1 (June 24, 2021): 22. http://dx.doi.org/10.51211/biict.v8i1.1492.
Abstract:
Abstrak: Masalah yang timbul dalam peramalan hasil produksi pertanian antara lain adalah sulit untuk mendapatkan data yang lengkap dari variabel-variabel yang mempengaruhi hasil pertanian dalam jangka panjang. Kondisi ini akan semakin sulit ketika peramalan mencakup wilayah yang cukup luas. Akibatnya, variabel-variabel tersebut harus diinterpolasi sehingga akan menyebabkan bias terhadap hasil peramalan. (1) Mengetahui gambaran meta analisis penelitian peramalan produk pertanian menggunakan Long Short Term Memory (LSTM), (2) Mengetahui penelitian meta analisis cakupan wilayah, komoditi dan periode data terkait produk pertanian terutama gandum, kedelai jagung dan pisang, (3) Mengetahui praproses data antara lain menghilangkan data yang tidak sesuai, menangani data yang kosong, serta memilih variabel tertentu. Sebagai solusi dari masalah tersebut, peramalan hasil produksi pertanian dilakukan berdasarkan data historis hasil produksi pertanian. Salah model peramalan yang saat ini banyak dikembangkan adalah model jaringan syaraf LSTM yang merupakan pengembangan dari model jaringan syaraf recurrent (RNN). Tulisan ini merupakan hasil kajian literatur pengembangan model-model LSTM untuk peramalan hasil produksi pertanian meliputi gandum, kedelai, jagung dan pisang. Perbaikan kinerja model LSTM dilakukan mulai dari praproses, tuning hyperparameter, sampai dengan penggabungan dengan metode lain. Berdasarkan kajian tersebut, model-model LSTM memiliki kinerja yang lebih baik dibandingkan dengan model benchmark.
Kata kunci: jaringan syaraf, LSTM, peramalan, produksi pertanian, RNN.
Abstract: Problems that arise in forecasting agricultural products include the difficulty of obtaining complete data on the variables that affect agricultural yields in the long term. This condition will be more difficult when the forecast covers a large area. As a result, these variables must be interpolated so that it will cause a bias towards the forecasting results. (1) Knowing the description of research maps for forecasting agricultural products using Long short term memory (LSTM), (2) Knowing Research Coverage areas, commodities, and data periods related to agricultural products, especially Wheat, Soybeans, corn, and bananas, (3) Knowing Preprocessing data between others remove inappropriate data, handle blank data, and select certain variables. This paper is the result of a literature review on the development of LSTM models for crop yields forecasting including wheat, soybeans, corn, and bananas. The Performance Improvements of the LSTM models were carried out by preprocessing data, hyperparameter tuning, and combining LSTM with other methods. Based on this study, LSTM models have better performance compared to the benchmark model.
Keywords: neural network, LSTM, forecasting, crop yield, RNN.
8
Nguyen, Sang Thi Thanh, and Bao Duy Tran. "Long Short-Term Memory Based Movie Recommendation." Science & Technology Development Journal - Engineering and Technology 3, SI1 (September 19, 2020): SI1—SI9. http://dx.doi.org/10.32508/stdjet.v3isi1.540.
Abstract:
Recommender systems (RS) have become a fundamental tool for helping users make decisions around millions of different choices nowadays – the era of Big Data. It brings a huge benefit for many business models around the world due to their effectiveness on the target customers. A lot of recommendation models and techniques have been proposed and many accomplished incredible outcomes. Collaborative filtering and content-based filtering methods are common, but these both have some disadvantages. A critical one is that they only focus on a user's long-term static preference while ignoring his or her short-term transactional patterns, which results in missing the user's preference shift through the time. In this case, the user's intent at a certain time point may be easily submerged by his or her historical decision behaviors, which leads to unreliable recommendations. To deal with this issue, a session of user interactions with the items can be considered as a solution. In this study, Long Short-Term Memory (LSTM) networks will be analyzed to be applied to user sessions in a recommender system. The MovieLens dataset is considered as a case study of movie recommender systems. This dataset is preprocessed to extract user-movie sessions for user behavior discovery and making movie recommendations to users. Several experiments have been carried out to evaluate the LSTM-based movie recommender system. In the experiments, the LSTM networks are compared with a similar deep learning method, which is Recurrent Neural Networks (RNN), and a baseline machine learning method, which is the collaborative filtering using item-based nearest neighbors (item-KNN). It has been found that the LSTM networks are able to be improved by optimizing their hyperparameters and outperform the other methods when predicting the next movies interested by users.
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Rezza, Muhammad, M. Ismail Yusuf, and Redi Ratiandi Yacoub. "Prediksi Radiasi Surya Menggunakan Metode Long Short-Term Memory." ILKOMNIKA: Journal of Computer Science and Applied Informatics 6, no. 1 (April 30, 2024): 33–44. http://dx.doi.org/10.28926/ilkomnika.v6i1.571.
Abstract:
Penelitian ini memfokuskan pada optimalisasi pemanfaatan energi matahari di Kalimantan Barat, sebuah wilayah yang kaya akan sumber daya matahari, dengan total pembangkit listrik tenaga surya (PLTS) mencapai 1.58 MW. Untuk memprediksi potensi energi matahari, penelitian menggunakan metode jaringan syaraf tiruan Long Short-Term Memory (LSTM) dengan data yang diperoleh dari data logger yang merekam tegangan, arus, dan daya keluaran panel surya selama 57 hari dengan interval 1-2 detik, menghasilkan 4.294.273 data. Dalam pengolahan data, 80% digunakan untuk pelatihan dan sisanya untuk pengujian model LSTM. Model LSTM yang digunakan terdiri dari 2 layer LSTM, masing-masing dengan 50 node LSTM. Penggunaan Google Colaboratory sebagai platform komputasi memungkinkan pelatihan model LSTM dalam dua skenario, yaitu dengan epoch sebesar 1 dan 10. Evaluasi model dilakukan menggunakan metrik Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), dan R-squared (R2). Hasil pengujian menunjukkan bahwa model dengan epoch 10 memiliki nilai evaluasi yang lebih baik, dengan MSE sebesar 0.04444, RMSE sebesar 0.00456, MAE sebesar 0.06753, dan R2 sebesar 0.99961, menunjukkan performa prediksi energi matahari yang sangat akurat dan efisien.
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Wimbassa, Muhamad Dwirizqy, Taswiyah Marsyah Noor, Salma Yasara, Vannesha Vannesha, Tubagus Muhammad Arsyah, and Abdiansah Abdiansah. "Emotional Text Detection dengan Long Short Term Memory (LSTM)." Format : Jurnal Ilmiah Teknik Informatika 12, no. 2 (July 5, 2023): 158. http://dx.doi.org/10.22441/format.2023.v12.i2.009.
Abstract:
Emotional Text Detection is a technique in natural language processing that aims to identify the emotions contained in conversations or text messages. The LSTM (Long Short-Term Memory) method is one of the techniques used in natural language processing to model and predict sequential data. In this study, we propose the use of the LSTM method for emotion detection in conversation. The dataset used is a conversational dataset that contains positive, negative, and neutral emotions. We process datasets using data pre-processing techniques such as tokenization, data cleansing and one-hot encoding. Then, we train the LSTM model on the processed dataset and obtain evaluation results using accuracy metrics. The experimental results show that the LSTM model can be used to detect emotions in conversation with a good degree of accuracy. In addition, we also conducted an analysis on the prediction results of the model and showed that the LSTM model can correctly identify emotions. In conclusion, the LSTM method can be used to detect emotions in conversation with a good degree of accuracy. This method can be used to improve user experience in chat applications and increase the effectiveness of human and machine interactions.
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Liu, Chen. "Long short-term memory (LSTM)-based news classification model." PLOS ONE 19, no. 5 (May 30, 2024): e0301835. http://dx.doi.org/10.1371/journal.pone.0301835.
Abstract:
In this study, we used unidirectional and bidirectional long short-term memory (LSTM) deep learning networks for Chinese news classification and characterized the effects of contextual information on text classification, achieving a high level of accuracy. A Chinese glossary was created using jieba—a word segmentation tool—stop-word removal, and word frequency analysis. Next, word2vec was used to map the processed words into word vectors, creating a convenient lookup table for word vectors that could be used as feature inputs for the LSTM model. A bidirectional LSTM (BiLSTM) network was used for feature extraction from word vectors to facilitate the transfer of information in both the backward and forward directions to the hidden layer. Subsequently, an LSTM network was used to perform feature integration on all the outputs of the BiLSTM network, with the output from the last layer of the LSTM being treated as the mapping of the text into a feature vector. The output feature vectors were then connected to a fully connected layer to construct a feature classifier using the integrated features, finally classifying the news articles. The hyperparameters of the model were optimized based on the loss between the true and predicted values using the adaptive moment estimation (Adam) optimizer. Additionally, multiple dropout layers were added to the model to reduce overfitting. As text classification models for Chinese news articles, the Bi-LSTM and unidirectional LSTM models obtained f1-scores of 94.15% and 93.16%, respectively, with the former outperforming the latter in terms of feature extraction.
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Gui, Tao, Qi Zhang, Lujun Zhao, Yaosong Lin, Minlong Peng, Jingjing Gong, and Xuanjing Huang. "Long Short-Term Memory with Dynamic Skip Connections." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6481–88. http://dx.doi.org/10.1609/aaai.v33i01.33016481.
Abstract:
In recent years, long short-term memory (LSTM) has been successfully used to model sequential data of variable length. However, LSTM can still experience difficulty in capturing long-term dependencies. In this work, we tried to alleviate this problem by introducing a dynamic skip connection, which can learn to directly connect two dependent words. Since there is no dependency information in the training data, we propose a novel reinforcement learning-based method to model the dependency relationship and connect dependent words. The proposed model computes the recurrent transition functions based on the skip connections, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially. Our experimental results on three natural language processing tasks demonstrate that the proposed method can achieve better performance than existing methods. In the number prediction experiment, the proposed model outperformed LSTM with respect to accuracy by nearly 20%.
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He, Jialuo. "Stock price prediction with long short-term memory." Applied and Computational Engineering 4, no. 1 (June 14, 2023): 127–33. http://dx.doi.org/10.54254/2755-2721/4/20230428.
Abstract:
Stock forecasting aims to predict future stock prices based on past price changes in the market, playing an essential role in the field of financial transactions. However, since the stock market is highly uncertain, stock prediction is complex and challenging. This paper uses the long short-term memory (LSTM) model to predict the stock market and compares it with the current stock prediction algorithm. Firstly, we preprocessed the raw dataset and normalized data into the range from 0 to 1. Secondly, we introduced the LSTM model and improved its performance by tuning four parameters: learning rate, number of hidden layers, number of epochs, and batch size. Finally, we use four evaluation metrics to evaluate models: mean average error (MAE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute error percentage (MAPE). Our LSTM model performs better than the previous model in experiments in terms of MAE, RMSE, R2, and MAPE.
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Muneer, Amgad, Rao Faizan Ali, Ahmed Almaghthawi, Shakirah Mohd Taib, Amal Alghamdi, and Ebrahim Abdulwasea Abdullah Ghaleb. "Short term residential load forecasting using long short-term memory recurrent neural network." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 5589. http://dx.doi.org/10.11591/ijece.v12i5.pp5589-5599.
Abstract:
<span>Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.</span>
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Hu, Sile, Wenbin Cai, Jun Liu, Hao Shi, and Jiawei Yu. "Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network." Volume 31, Issue 3 31, no. 3 (April 4, 2024): 151–66. http://dx.doi.org/10.20532/cit.2023.1005730.
Abstract:
Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid. Therefore, how to predict power load quickly and accurately has become one of the urgent problems to be solved. Based on the optimization parameter selection and data preprocessing of the improved Long Short-Term Memory Network, the study first integrated particle swarm optimization algorithm to achieve parameter optimization. Then, combined with convolutional neural network, the power load data were processed to optimize the data and reduce noise, thereby enhancing model performance. Finally, simulation experiments were conducted. The PSO-CNN-LSTM model was tested on the GEFC dataset and demonstrated stability of up to 90%. This was 22% higher than the competing CNN-LSTM model and at least 30% higher than the LSTM model. The PSO-CNN-LSTM model was trained with a step size of 1.9×10^4, the relative mean square error was 0.2345×10^-4. However, when the CNN-LSTM and LSTM models were trained for more than 2.0×10^4 steps, they still did not achieve the target effect. In addition, the fitting error of the PSOCNN-LSTM model in the GEFC dataset was less than 1.0×10^-7. In power load forecasting, the PSOCNN- LSTM model's predicted results had an average absolute error of less than 1.0% when compared to actual data. This was an improvement of at least 0.8% compared to the average absolute error of the CNNLSTM prediction model. These experiments confirmed that the prediction model that combined two methods had further improved the speed and accuracy of power load prediction compared to traditional prediction models, providing more guarantees for safe and stable operation of the power system.
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Kumar, Naresh, Jatin Bindra, Rajat Sharma, and Deepali Gupta. "Air Pollution Prediction Using Recurrent Neural Network, Long Short-Term Memory and Hybrid of Convolutional Neural Network and Long Short-Term Memory Models." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4580–84. http://dx.doi.org/10.1166/jctn.2020.9283.
Abstract:
Air pollution prediction was not an easy task few years back. With the increasing computation power and wide availability of the datasets, air pollution prediction problem is solved to some extend. Inspired by the deep learning models, in this paper three techniques for air pollution prediction have been proposed. The models used includes recurrent neural network (RNN), Long short-term memory (LSTM) and a hybrid combination of Convolutional neural network (CNN) and LSTM models. These models are tested by comparing MSE loss on air pollution test of Belgium. The validation loss on RNN is 0.0045, LSTM is 0.00441 and CNN and LSTM is 0.0049. The loss on testing dataset for these models are 0.00088, 0.00441 and 0.0049 respectively.
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Shankar, Sonali, P. Vigneswara Ilavarasan, Sushil Punia, and Surya Prakash Singh. "Forecasting container throughput with long short-term memory networks." Industrial Management & Data Systems 120, no. 3 (December 4, 2019): 425–41. http://dx.doi.org/10.1108/imds-07-2019-0370.
Abstract:
Purpose Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty. Findings The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods. Originality/value The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.
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Son, Hojae, Anand Paul, and Gwanggil Jeon. "Country Information Based on Long-Term Short-Term Memory (LSTM)." International Journal of Engineering & Technology 7, no. 4.44 (December 1, 2018): 47. http://dx.doi.org/10.14419/ijet.v7i4.44.26861.
Abstract:
Social platform such as Facebook, Twitter and Instagram generates tremendous data these days. Researchers make use of these data to extract meaningful information and predict future. Especially twitter is the platform people can share their thought briefly on a certain topic and it provides real-time streaming data API (Application Programming Interface) for filtering data for a purpose. Over time a country has changed its interest in other countries. People can get a benefit to see a tendency of interest as well as prediction result from twitter streaming data. Capturing twitter data flow is connected to how people think and have an interest on the topic. We believe real-time twitter data reflect this change. Long-term Short-term Memory Unit (LSTM) is the widely used deep learning unit from recurrent neural network to learn the sequence. The purpose of this work is building prediction model “Country Interest Analysis based on LSTM (CIAL)” to forecast next interval of tweet counts when it comes to referring country on the tweet post. Additionally it’s necessary to cluster for analyzing multiple countries twitter data over the remote nodes. This paper presents how country attention tendency can be captured over twitter streaming data with LSTM algorithm.
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Izzadiana, Helma Syifa, Herlina Napitupulu, and Firdaniza Firdaniza. "Peramalan Data Univariat Menggunakan Metode Long Short Term Memory." SisInfo : Jurnal Sistem Informasi dan Informatika 5, no. 2 (August 18, 2023): 29–39. http://dx.doi.org/10.37278/sisinfo.v5i2.669.
Abstract:
Peramalan data univariat mengacu pada kegiatan meramalkan nilai pada data dengan satu variabel independen yang mungkin muncul di masa depan berdasarkan nilai-nilai yang ada di masa lalu. Penelitian ini bertujuan untuk memperoleh model yang dibangun menggunakan pendekatan deep learning jenis supervised learning yaitu metode Long Short Term Memory (LSTM) yang diterapkan pada data univariat. Metode LSTM merupakan pengembangan dari metode Recurrent Neural Network (RNN) dengan menambahkan 3 gate yang mampu memilih informasi yang dibutuhkan untuk pelatihan sel sehingga mampu mengurangi kemungkinan exploding gradients dan vanishing gradients. Model dibangun dengan input layer LSTM dengan unit sel dan output dense layer dengan tambahan hyperparameter tuning yang diset menggunakan optimizer, fungsi aktivasi dan , dan nilai epoch. Performa model peramalan diuji menggunakan mean absolute percentage error (MAPE).
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Arifin, Samsul, AndyanKalmer Wijaya, Rinda Nariswari, I. Gusti Agung Anom Yudistira, Suwarno Suwarno, Faisal Faisal, and Diah Wihardini. "Long Short-Term Memory (LSTM): Trends and Future Research Potential." International Journal of Emerging Technology and Advanced Engineering 13, no. 5 (May 13, 2023): 24–34. http://dx.doi.org/10.46338/ijetae0523_04.
Abstract:
-One of the most widely used machine learning methods, Long Short-Term Memory (LSTM), is particularly useful for time series prediction. In this study, we carried out a bibliometric analysis against publications about LSTMs to identify trends and contributions of researchers in the development of machine learning technology. We collect bibliometric data from the Scopus database and use the bibliometric analysis method to analyze trends and contributions of researchers in publications about LSTM. Results of the bibliometric analysis show that LSTM is a lot used in related machine learning applications with time series data and is one the most popular technique for use in predictions. In addition, the use of LSTM is often combined with other deep learning methods, such as neural networks, to improve accuracy prediction. In addition, the results of the bibliometric analysis also show that the use of LSTM has spread to various fields, such as in handwriting recognition, processing Language experience, and recognition of a face. Implications from the results of this study are that the use of LSTM can provide solutions that are accurate and effective in solving prediction problems in various fields, especially in practical applications such as business, health, and transportation. The results of the LSTM bibliometric analysis can provide a broader view of trends and the contributions of researchers to the development of machine learning technology, as well as identify potential research areas for further development. Therefore, this research provides an important contribution to strengthening the results of previous research and showing that the use of LSTM has great potential in the development of future machine learning technology
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Awad, Asmaa Ahmed, Ahmed Fouad Ali, and Tarek Gaber. "An improved long short term memory network for intrusion detection." PLOS ONE 18, no. 8 (August 1, 2023): e0284795. http://dx.doi.org/10.1371/journal.pone.0284795.
Abstract:
Over the years, intrusion detection system has played a crucial role in network security by discovering attacks from network traffics and generating an alarm signal to be sent to the security team. Machine learning methods, e.g., Support Vector Machine, K Nearest Neighbour, have been used in building intrusion detection systems but such systems still suffer from low accuracy and high false alarm rate. Deep learning models (e.g., Long Short-Term Memory, LSTM) have been employed in designing intrusion detection systems to address this issue. However, LSTM needs a high number of iterations to achieve high performance. In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. The performance of ILSTM and the intrusion detection system were evaluated using two public datasets (NSL-KDD dataset and LITNET-2020) under nine performance metrics. The results showed that the proposed ILSTM algorithm outperformed the original LSTM and other related deep-learning algorithms regarding accuracy and precision. The ILSTM achieved an accuracy of 93.09% and a precision of 96.86% while LSTM gave an accuracy of 82.74% and a precision of 76.49%. Also, the ILSTM performed better than LSTM in both datasets. In addition, the statistical analysis showed that ILSTM is more statistically significant than LSTM. Further, the proposed ISTLM gave better results of multiclassification of intrusion types such as DoS, Prob, and U2R attacks.
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Song, Tianyu, Wei Ding, Jian Wu, Haixing Liu, Huicheng Zhou, and Jinggang Chu. "Flash Flood Forecasting Based on Long Short-Term Memory Networks." Water 12, no. 1 (December 29, 2019): 109. http://dx.doi.org/10.3390/w12010109.
Abstract:
Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory (LSTM) networks for flash flood forecasting in mountainous catchments. The proposed LSTM flood forecasting (LSTM-FF) model is composed of T multivariate single-step LSTM networks and takes spatial and temporal dynamics information of observed and forecast rainfall and early discharge as inputs. The case study in Anhe revealed that the proposed models can effectively predict flash floods, especially the qualified rates (the ratio of the number of qualified events to the total number of flood events) of large flood events are above 94.7% at 1–5 h lead time and range from 84.2% to 89.5% at 6–10 h lead-time. For the large flood simulation, the small flood events can help the LSTM-FF model to explore a better rainfall-runoff relationship. The impact analysis of weights in the LSTM network structures shows that the discharge input plays a more obvious role in the 1-h LSTM network and the effect decreases with the lead-time. Meanwhile, in the adjacent lead-time, the LSTM networks explored a similar relationship between input and output. The study provides a new approach for flash flood forecasting and the highly accurate forecast contributes to prepare for and mitigate disasters.
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Salman, Umar, Shafiqur Rehman, Basit Alawode, and Luai Alhems. "Short term prediction of wind speed based on long-short term memory networks." FME Transactions 49, no. 3 (2021): 643–52. http://dx.doi.org/10.5937/fme2103643s.
Abstract:
Power utilities, developers, and investors are pushing towards larger penetrations of wind and solar energy-based power generation in their existing energy mix. This study, specifically, looks towards wind power deployment in Saudi Arabia. For profitable development of wind power, accurate knowledge of wind speed both in spatial and time domains is critical. The wind speed is the most fluctuating and intermittent parameter in nature compared to all the meteorological variables. This uncertain nature of wind speed makes wind power more difficult to predict ahead of time. Wind speed is dependent on meteorological factors such as pressure, temperature, and relative humidity and can be predicted using these meteorological parameters. The forecasting of wind speed is critical for grid management, cost of energy, and quality power supply. This study proposes a short-term, multi-dimensional prediction of wind speed based on Long-Short Term Memory Networks (LSTM). Five models are developed by training the networks with measured hourly mean wind speed values from1980 to 2019 including exogenous inputs (temperature and pressure). The study found that LSTM is a powerful tool for a short-term prediction of wind speed. However, the accuracy of LSTM may be compromised with the inclusion of exogenous features in the training sets and the duration of prediction ahead.
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Xu, Wei, Yanan Jiang, Xiaoli Zhang, Yi Li, Run Zhang, and Guangtao Fu. "Using long short-term memory networks for river flow prediction." Hydrology Research 51, no. 6 (October 5, 2020): 1358–76. http://dx.doi.org/10.2166/nh.2020.026.
Abstract:
Abstract Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow. The batch size and the number of LSTM cells are sensitive parameters and should be carefully tuned to achieve a balance between learning efficiency and stability. Compared with several hydrological models, the LSTM network achieves good performance in terms of three evaluation criteria, i.e., coefficient of determination, Nash–Sutcliffe Efficiency and relative error, which demonstrates its powerful capacity in learning non-linear and complex processes in hydrological modelling.
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Bhandarkar, Tanvi, Vardaan K, Nikhil Satish, S. Sridhar, R. Sivakumar, and Snehasish Ghosh. "Earthquake trend prediction using long short-term memory RNN." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 1304. http://dx.doi.org/10.11591/ijece.v9i2.pp1304-1312.
Abstract:
<p>The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.</p>
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Yang, Tianyi, Quanming Zhao, and Yifan Meng. "Ultra-short-term Photovoltaic Power Prediction Based on Multi-head ProbSparse Self-attention and Long Short-term Memory." Journal of Physics: Conference Series 2558, no. 1 (August 1, 2023): 012007. http://dx.doi.org/10.1088/1742-6596/2558/1/012007.
Abstract:
Abstract To provide accurate predictions of photovoltaic (PV) power generation, an MHPSA-LSTM ultra-short-term multipoint PV power prediction model combining Multi-head ProbSparse self-attention (MHPSA) and long short-term memory (LSTM) network is posited. The MHPSA is first used to capture information dependencies at a distance. Secondly, the LSTM is used to enhance the local correlation. At last, a pooling layer is added after LSTM to reduce the parameters of the fully-connected layer and alleviate overfitting, thus improving the prediction accuracy. The MHPSA-LSTM model is validated on a PV plant at the Desert Knowledge Australia Solar Centre as an example, and the RMSE, MAE, and R2 of MHPSA-LSTM are 0.527, 0.264, and 0.917, respectively. MHPSA-LSTM has higher prediction accuracy compared with BP, LSTM, GRU, and CNN-LSTM.
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Wang, Jianyong, Lei Zhang, Yuanyuan Chen, and Zhang Yi. "A New Delay Connection for Long Short-Term Memory Networks." International Journal of Neural Systems 28, no. 06 (June 24, 2018): 1750061. http://dx.doi.org/10.1142/s0129065717500617.
Abstract:
Connections play a crucial role in neural network (NN) learning because they determine how information flows in NNs. Suitable connection mechanisms may extensively enlarge the learning capability and reduce the negative effect of gradient problems. In this paper, a new delay connection is proposed for Long Short-Term Memory (LSTM) unit to develop a more sophisticated recurrent unit, called Delay Connected LSTM (DCLSTM). The proposed delay connection brings two main merits to DCLSTM with introducing no extra parameters. First, it allows the output of the DCLSTM unit to maintain LSTM, which is absent in the LSTM unit. Second, the proposed delay connection helps to bridge the error signals to previous time steps and allows it to be back-propagated across several layers without vanishing too quickly. To evaluate the performance of the proposed delay connections, the DCLSTM model with and without peephole connections was compared with four state-of-the-art recurrent model on two sequence classification tasks. DCLSTM model outperformed the other models with higher accuracy and F1[Formula: see text]score. Furthermore, the networks with multiple stacked DCLSTM layers and the standard LSTM layer were evaluated on Penn Treebank (PTB) language modeling. The DCLSTM model achieved lower perplexity (PPL)/bit-per-character (BPC) than the standard LSTM model. The experiments demonstrate that the learning of the DCLSTM models is more stable and efficient.
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Lv, Liujia, Weijian Kong, Jie Qi, and Jue Zhang. "An improved long short-term memory neural network for stock forecast." MATEC Web of Conferences 232 (2018): 01024. http://dx.doi.org/10.1051/matecconf/201823201024.
Abstract:
This paper presents an improved long short-term memory (LSTM) neural network based on particle swarm optimization (PSO), which is applied to predict the closing price of the stock. PSO is introduced to optimize the weights of the LSTM neural network, which reduces the prediction error. After preprocessing the historical data of the stock, including opening price, closing price, highest price, lowest price, and daily volume these five attributes, we train the LSTM by employing time series of the historical data. Finally, we apply the proposed LSTM to predict the closing price of the stock in the last two years. Compared with typical algorithms by simulation, we find the LSTM has better performance in reliability and adaptability, and the improved PSO-LSTM algorithm has better accuracy.
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Zhang, Xiaoyu, Yongqing Li, Song Gao, and Peng Ren. "Ocean Wave Height Series Prediction with Numerical Long Short-Term Memory." Journal of Marine Science and Engineering 9, no. 5 (May 10, 2021): 514. http://dx.doi.org/10.3390/jmse9050514.
Abstract:
This paper investigates the possibility of using machine learning technology to correct wave height series numerical predictions. This is done by incorporating numerical predictions into long short-term memory (LSTM). Specifically, a novel ocean wave height series prediction framework, referred to as numerical long short-term memory (N-LSTM), is introduced. The N-LSTM takes a combined wave height representation, which is formed of a current wave height measurement and a subsequent Simulating Waves Nearshore (SWAN) numerical prediction, as the input and generates the corrected numerical prediction as the output. The correction is achieved by two modules in cascade, i.e., the LSTM module and the Gaussian approximation module. The LSTM module characterizes the correlation between measurement and numerical prediction. The Gaussian approximation module models the conditional probabilistic distribution of the wave height given the learned LSTM. The corrected numerical prediction is obtained by sampling the conditional probabilistic distribution and the corrected numerical prediction series is obtained by iterating the N-LSTM. Experimental results validate that our N-LSTM effectively lifts the accuracy of wave height numerical prediction from SWAN for the Bohai Sea and Xiaomaidao. Furthermore, compared with the state-of-the-art machine learning based prediction methods (e.g., residual learning), the N-LSTM achieves better prediction accuracy by 10% to 20% for the prediction time varying from 3 to 72 h.
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Sugiartawan, Putu, Agus Aan Jiwa Permana, and Paholo Iman Prakoso. "Forecasting Kunjungan Wisatawan Dengan Long Short Term Memory (LSTM)." Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) 1, no. 1 (September 30, 2018): 43–52. http://dx.doi.org/10.33173/jsikti.5.
Abstract:
Bali is one of the favorite tourist attractions in Indonesia, where the number of foreign tourists visiting Bali is around 4 million over 2015 (Dispar Bali). The number of tourists visiting is spread in various regions and tourist attractions that are located in Bali. Although tourist visits to Bali can be said to be large, the visit was not evenly distributed, there were significant fluctuations in tourist visits. Forecasting or forecasting techniques can find out the pattern of tourist visits.
Forecasting technique aims to predict the previous data pattern so that the next data pattern can be known. In this study using the technique of recurrent neural network in predicting the level of tourist visits. One of the techniques for a recurrent neural network (RNN) used in this study is Long Short-Term Memory (LSTM). This model is better than a simple RNN model. In this study predicting the level of tourist visits using the LSTM algorithm, the data used is data on tourist visits to one of the attractions in Bali. The results obtained using the LSTM model amounted to 15,962. The measured value is an error value, with the MAPE technique. The LSTM architecture used consists of 16 units of neuron units in the hidden layer, a learning rate of 0.01, windows size of 3, and the number of hidden layers is 1.
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Santra, Arpita Samanta, and Jun-Lin Lin. "Integrating Long Short-Term Memory and Genetic Algorithm for Short-Term Load Forecasting." Energies 12, no. 11 (May 28, 2019): 2040. http://dx.doi.org/10.3390/en12112040.
Abstract:
Electricity load forecasting is an important task for enhancing energy efficiency and operation reliability of the power system. Forecasting the hourly electricity load of the next day assists in optimizing the resources and minimizing the energy wastage. The main motivation of this study was to improve the robustness of short-term load forecasting (STLF) by utilizing long short- term memory (LSTM) and genetic algorithm (GA). The proposed method is novel: LSTM networks are designed to avoid the problem of long-term dependencies, and GA is used to obtain the optimal LSTM’s parameters, which are then applied to predict the hourly electricity load for the next day. The proposed method was trained using actual load and weather data, and the performance results showed that it yielded small mean absolute percentage error on the test data.
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Wang, Ying, Bo Feng, Qing-Song Hua, and Li Sun. "Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method." Sustainability 13, no. 7 (March 25, 2021): 3665. http://dx.doi.org/10.3390/su13073665.
Abstract:
Solar power is considered a promising power generation candidate in dealing with climate change. Because of the strong randomness, volatility, and intermittence, its safe integration into the smart grid requires accurate short-term forecasting with the required accuracy. The use of solar power should meet requirements proscribed by environmental law and safety standards applied for consumer protection. First, time-series-based solar power forecasting (SPF) model is developed with the time element and predicted weather information from the local meteorological station. Considering the data correlation, long short-term memory (LSTM) algorithm is utilized for short-term SPF. However, the point prediction provided by LSTM fails in revealing the underlying uncertainty range of the solar power output, which is generally needed in some stochastic optimization frameworks. A novel hybrid strategy combining LSTM and Gaussian process regression (GPR), namely LSTM-GPR, is proposed to obtain a highly accurate point prediction with a reliable interval estimation. The hybrid model is evaluated in comparison with other algorithms in terms of two aspects: Point prediction accuracy and interval forecasting reliability. Numerical investigations confirm the superiority of LSTM algorithm over the conventional neural networks. Furthermore, the performance of the proposed hybrid model is demonstrated to be slightly better than the individual LSTM model and significantly superior to the individual GPR model in both point prediction and interval forecasting, indicating a promising prospect for future SPF applications.
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Fajar Abdillah, Moh, and Kusnawi Kusnawi. "Comparative Analysis of Long Short-Term Memory Architecture for Text Classification." ILKOM Jurnal Ilmiah 15, no. 3 (December 20, 2023): 455–64. http://dx.doi.org/10.33096/ilkom.v15i3.1906.455-464.
Abstract:
Text classification which is a part of NLP is a grouping of objects in the form of text based on certain characteristics that show similarities between one document and another. One of methods used in text classification is LSTM. The performance of the LSTM method itself is influenced by several things such as datasets, architecture, and tools used to classify text. On this occasion, researchers analyse the effect of the number of layers in the LSTM architecture on the performance generated by the LSTM method. This research uses IMDB movie reviews data with a total of 50,000 data. The data consists of positive, negative data and there is data that does not yet have a label. IMDB Movie Reviews data go through several stages as follows: Data collection, data pre-processing, conversion to numerical format, text embedding using the pre-trained word embedding model: Fastext, train and test classification model using LSTM, finally validate and test the model so that the results are obtained from the stages of this research. The results of this study show that the one-layer LSTM architecture has the best accuracy compared to two-layer and three-layer LSTM with training accuracy and testing accuracy of one-layer LSTM which are 0.856 and 0.867. While the training accuracy and testing accuracy on two-layer LSTM are 0.846 and 0.854, the training accuracy and testing accuracy on three layers are 0.848 and 864.
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Lees, Thomas, Steven Reece, Frederik Kratzert, Daniel Klotz, Martin Gauch, Jens De Bruijn, Reetik Kumar Sahu, Peter Greve, Louise Slater, and Simon J. Dadson. "Hydrological concept formation inside long short-term memory (LSTM) networks." Hydrology and Earth System Sciences 26, no. 12 (June 20, 2022): 3079–101. http://dx.doi.org/10.5194/hess-26-3079-2022.
Abstract:
Abstract. Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains: what have these models learned? Is it possible to extract information about the learned relationships that map inputs to outputs, and do these mappings represent known hydrological concepts? Small-scale experiments have demonstrated that the internal states of long short-term memory networks (LSTMs), a particular neural network architecture predisposed to hydrological modelling, can be interpreted. By extracting the tensors which represent the learned translation from inputs (precipitation, temperature, and potential evapotranspiration) to outputs (discharge), this research seeks to understand what information the LSTM captures about the hydrological system. We assess the hypothesis that the LSTM replicates real-world processes and that we can extract information about these processes from the internal states of the LSTM. We examine the cell-state vector, which represents the memory of the LSTM, and explore the ways in which the LSTM learns to reproduce stores of water, such as soil moisture and snow cover. We use a simple regression approach to map the LSTM state vector to our target stores (soil moisture and snow). Good correlations (R2>0.8) between the probe outputs and the target variables of interest provide evidence that the LSTM contains information that reflects known hydrological processes comparable with the concept of variable-capacity soil moisture stores. The implications of this study are threefold: (1) LSTMs reproduce known hydrological processes. (2) While conceptual models have theoretical assumptions embedded in the model a priori, the LSTM derives these from the data. These learned representations are interpretable by scientists. (3) LSTMs can be used to gain an estimate of intermediate stores of water such as soil moisture. While machine learning interpretability is still a nascent field and our approach reflects a simple technique for exploring what the model has learned, the results are robust to different initial conditions and to a variety of benchmarking experiments. We therefore argue that deep learning approaches can be used to advance our scientific goals as well as our predictive goals.
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Kratzert, Frederik, Daniel Klotz, Claire Brenner, Karsten Schulz, and Mathew Herrnegger. "Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks." Hydrology and Earth System Sciences 22, no. 11 (November 22, 2018): 6005–22. http://dx.doi.org/10.5194/hess-22-6005-2018.
Abstract:
Abstract. Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data-driven models. In this paper, we propose a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA + Snow-17, which underlines the potential of the LSTM for hydrological modelling applications.
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Moradzadeh, Arash, Sahar Zakeri, Maryam Shoaran, Behnam Mohammadi-Ivatloo, and Fazel Mohammadi. "Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms." Sustainability 12, no. 17 (August 30, 2020): 7076. http://dx.doi.org/10.3390/su12177076.
Abstract:
Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.
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Min, Huasong, Ziming Chen, Bin Fang, Ziwei Xia, Yixu Song, Zongtao Wang, Quan Zhou, Fuchun Sun, and Chunfang Liu. "Cross-Individual Gesture Recognition Based on Long Short-Term Memory Networks." Scientific Programming 2021 (July 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/6680417.
Abstract:
Gestures recognition based on surface electromyography (sEMG) has been widely used for human-computer interaction. However, there are few research studies on overcoming the influence of physiological factors among different individuals. In this paper, a cross-individual gesture recognition method based on long short-term memory (LSTM) networks is proposed, named cross-individual LSTM (CI-LSTM). CI-LSTM has a dual-network structure, including a gesture recognition module and an individual recognition module. By designing the loss function, the individual information recognition module assists the gesture recognition module to train, which tends to orthogonalize the gesture features and individual features to minimize the impact of individual information differences on gesture recognition. Through cross-individual gesture recognition experiments, it is verified that compared with other selected algorithm models, the recognition accuracy obtained by using the CI-LSTM model can be improved by an average of 9.15%. Compared with other models, CI-LSTM can overcome the influence of individual characteristics and complete the task of cross-individual hand gestures recognition. Based on the proposed model, online control of the prosthetic hand is realized.
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Yudi Widhiyasana, Transmissia Semiawan, Ilham Gibran Achmad Mudzakir, and Muhammad Randi Noor. "Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 10, no. 4 (November 29, 2021): 354–61. http://dx.doi.org/10.22146/jnteti.v10i4.2438.
Abstract:
Klasifikasi teks saat ini telah menjadi sebuah bidang yang banyak diteliti, khususnya terkait Natural Language Processing (NLP). Terdapat banyak metode yang dapat dimanfaatkan untuk melakukan klasifikasi teks, salah satunya adalah metode deep learning. RNN, CNN, dan LSTM merupakan beberapa metode deep learning yang umum digunakan untuk mengklasifikasikan teks. Makalah ini bertujuan menganalisis penerapan kombinasi dua buah metode deep learning, yaitu CNN dan LSTM (C-LSTM). Kombinasi kedua metode tersebut dimanfaatkan untuk melakukan klasifikasi teks berita bahasa Indonesia. Data yang digunakan adalah teks berita bahasa Indonesia yang dikumpulkan dari portal-portal berita berbahasa Indonesia. Data yang dikumpulkan dikelompokkan menjadi tiga kategori berita berdasarkan lingkupnya, yaitu “Nasional”, “Internasional”, dan “Regional”. Dalam makalah ini dilakukan eksperimen pada tiga buah variabel penelitian, yaitu jumlah dokumen, ukuran batch, dan nilai learning rate dari C-LSTM yang dibangun. Hasil eksperimen menunjukkan bahwa nilai F1-score yang diperoleh dari hasil klasifikasi menggunakan metode C-LSTM adalah sebesar 93,27%. Nilai F1-score yang dihasilkan oleh metode C-LSTM lebih besar dibandingkan dengan CNN, dengan nilai 89,85%, dan LSTM, dengan nilai 90,87%. Dengan demikian, dapat disimpulkan bahwa kombinasi dua metode deep learning, yaitu CNN dan LSTM (C-LSTM),memiliki kinerja yang lebih baik dibandingkan dengan CNN dan LSTM.
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Li, Siyao, Rui Qin, and Zijian Zhou. "Movie sentiment analysis based on Long Short-Term Memory Network." Applied and Computational Engineering 38, no. 1 (January 22, 2024): 16–25. http://dx.doi.org/10.54254/2755-2721/38/20230524.
Abstract:
An important task in the study of Natural Language Processing (NLP) is the analysis of movie reviews. It finishes the task of classifying movie review texts into sentiment, such as positive, negative or neutral sentiment. Previous works mainly follow the pipeline of LSTM (Long Short-Term Memory Network). The network model is a variant of Recurrent Neural Network (RNN) and particularly suitable for processing natural language texts. Though existing LSTM-based works have improved the performance significantly, we argue that most of them deal with the problem of analyzing the sentiment of movie reviews while ignore analyze the model performance in different application scenarios, such as different lengths of the reviews and the frequency of sentiment adverbs in the reviews. To alleviate the above issue, in this paper, we constructed a simple LSTM model containing an embedding layer, a batch normalization layer, a dropout layer, a one-dimensional convolutional layer, a maximal pooling layer, a bi-directional LSTM layer and a fully connected layer. We used the existing IMDB movie review dataset to train the model, and selected two research scenarios of movie review length and frequency of occurrence of sentiment adverbs to test the model, respectively. From the experimental results, we proposed a model for the scenarios in which the LSTM model handles the problem of sentiment analysis with respect to the dataset construction, model stability and generalization ability, text fragment processing, data preprocessing and feature extraction, model optimization and improvement.
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Han, Mingchong, Aiguo Tan, and Jianwei Zhong. "Application of Particle Swarm Optimization Combined with Long and Short-term Memory Networks for Short-term Load Forecasting." Journal of Physics: Conference Series 2203, no. 1 (February 1, 2022): 012047. http://dx.doi.org/10.1088/1742-6596/2203/1/012047.
Abstract:
Abstract In this paper, we apply the Long Short-Term Memory (LSTM) network to short-term load forecasting, and use the TensorFlow deep learning framework to build a Particle Swarm Optimization (PSO) model to optimize the parameters of the LSTM. Optimization (PSO) model to optimize the parameters of LSTM. In this paper, we use the meteorological data and historical load data of a certain place as the input of LSTM before and after optimization, and compare the model with the BP Neural Network before and after optimization, and the results show that the PSO-LSTM model has higher reliability and prediction accuracy.
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Cruz-Victoria, Juan Crescenciano, Alma Rosa Netzahuatl-Muñoz, and Eliseo Cristiani-Urbina. "Long Short-Term Memory and Bidirectional Long Short-Term Memory Modeling and Prediction of Hexavalent and Total Chromium Removal Capacity Kinetics of Cupressus lusitanica Bark." Sustainability 16, no. 7 (March 29, 2024): 2874. http://dx.doi.org/10.3390/su16072874.
Abstract:
Hexavalent chromium [Cr(VI)] is a high-priority environmental pollutant because of its toxicity and potential to contaminate water sources. Biosorption, using low-cost biomaterials, is an emerging technology for removing pollutants from water. In this study, Long Short-Term Memory (LSTM) and bidirectional LSTM (Bi-LSTM) neural networks were used to model and predict the kinetics of the removal capacity of Cr(VI) and total chromium [Cr(T)] using Cupressus lusitanica bark (CLB) particles. The models were developed using 34 experimental kinetics datasets under various temperature, pH, particle size, and initial Cr(VI) concentration conditions. Data preprocessing via interpolation was implemented to augment the sparse time-series data. Early stopping regularization prevented overfitting, and dropout techniques enhanced model robustness. The Bi-LSTM models demonstrated a superior performance compared to the LSTM models. The inherent complexities of the process and data limitations resulted in a heavy-tailed and left-skewed residual distribution, indicating occasional deviations in the predictions of capacities obtained under extreme conditions. K-fold cross-validation demonstrated the stability of Bi-LSTM models 38 and 43, while response surfaces and validation with unseen datasets assessed their predictive accuracy and generalization capabilities. Shapley additive explanations analysis (SHAP) identified the initial Cr(VI) concentration and time as the most influential input features for the models. This study highlights the capabilities of deep recurrent neural networks in comprehending and predicting complex pollutant removal kinetic phenomena for environmental applications.
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Bi, Ruoxue. "Long-term and short-term memory network based movie comment sentiment analysis." Applied and Computational Engineering 36, no. 1 (January 22, 2024): 150–55. http://dx.doi.org/10.54254/2755-2721/36/20230437.
Abstract:
This paper proposes an emotional analysis method of movie reviews based on Long-term and Short-term Memory(LSTM) Network model. Emotional analysis is widely used in movie recommendation system, which can recommend and judge movies by understanding the audiences emotional response to movies. However, due to the characteristics of movie text and the complexity of emotional expression, traditional methods such as machine learning have limitations and shortcomings in emotional analysis. However, the LSTM models better memory is utilized by the method proposed in this paper and the ability to capture the long-term correlation in movie texts, which obviously improves the accuracy and reliability of emotional analysis, and demonstrates the advantages of the LSTM model in emotional analysis compared to the traditional model. Future research can further explore other deep learning models and algorithms, so as to make emotional analysis more accurate and provide users with reliable movie recommendation information.
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Lasijan, Tania Giovani, Rukun Santoso, and Arief Rachman Hakim. "PREDIKSI HARGA EMAS DUNIA MENGGUNAKAN METODE LONG-SHORT TERM MEMORY." Jurnal Gaussian 12, no. 2 (May 14, 2023): 287–95. http://dx.doi.org/10.14710/j.gauss.12.2.287-295.
Abstract:
Gold investment is one of the investments that is quite lot of interest by the public and also is considered safer because it has relatively low risk and tends to be stable compared to other investment instruments, especially amid the uncertainty of global economic conditions caused by the COVID-19 pandemic. Awareness about gold price predictions can provide information to people who want to invest in gold so they have higher opportunity to earn profits and minimize the risks obtained. The gold prices prediction method used in this study is Long-Short Term Memory (LSTM) using RStudio. LSTM is one of the method that is widely used to predict time series data. LSTM is a variation of the Recurrent Neural Network (RNN) that is used as a solution to overcome the occurrence of exploding gradient or vanishing gradient in RNN when processing long sequential data. The best LSTM model in this study for predicting gold prices is the model with MAPE value 2,70601, which is a model with a training data and testing data comparison 70% : 30% and hyperparameters batch size 1, units 1, AdaGrad optimizer, and learning rate 0,1 with 500 epochs.
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Zhang, Suqin. "Stock price prediction based on the long short-term memory network." Applied and Computational Engineering 18, no. 1 (October 23, 2023): 28–32. http://dx.doi.org/10.54254/2755-2721/18/20230958.
Abstract:
Stock analysis is a challenging task that involves modelling complex and nonlinear dynamics of stock prices and volumes. Long Short-Term Memory (LSTM) is a type of recurrent neural network that can capture long-term dependencies and temporal patterns in time series data. In this paper, a stock analysis method based on LSTM is proposed that can predict future stock prices and transactions using historical data. Yfinance is used to obtain stock data of four technology companies (i.e. Apple, Google, Microsoft, and Amazon) and apply LSTM to extract features and forecast trends. Various techniques are also used such as moving average, correlation analysis, and risk assessment to evaluate the performance and risk of different stocks. When compare the method in this paper with other neural network models such as RNN and GRU, the result show that LSTM achieves better accuracy and stability in stock prediction. This paper demonstrates the effectiveness and applicability of LSTM method through experiments on real-world data sets.
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Tra, Nguyen Ngoc, Ho Phuoc Tien, Nguyen Thanh Dat, and Nguyen Ngoc Vu. "VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS." Journal of Science and Technology: Issue on Information and Communications Technology 17, no. 12.2 (December 9, 2019): 61. http://dx.doi.org/10.31130/ict-ud.2019.94.
Abstract:
The paper attemps to forecast the future trend of Vietnam index (VN-index) by using long-short term memory (LSTM) networks. In particular, an LSTM-based neural network is employed to study the temporal dependence in time-series data of past and present VN index values. Empirical forecasting results show that LSTM-based stock trend prediction offers an accuracy of about 60% which outperforms moving-average-based prediction.
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Zhang, Pei, Chunping Li, Chunhua Peng, and Jiangang Tian. "Ultra-Short-Term Prediction of Wind Power Based on Error Following Forget Gate-Based Long Short-Term Memory." Energies 13, no. 20 (October 16, 2020): 5400. http://dx.doi.org/10.3390/en13205400.
Abstract:
To improve the accuracy of ultra-short-term wind power prediction, this paper proposed a model using modified long short-term memory (LSTM) to predict ultra-short-term wind power. Because the forget gate of standard LSTM cannot reflect the correction effect of prediction errors on model prediction in ultra-short-term, this paper develops the error following forget gate (EFFG)-based LSTM model for ultra-short-term wind power prediction. The proposed EFFG-based LSTM model updates the output of the forget gate using the difference between the predicted value and the actual value, thereby reducing the impact of the prediction error at the previous moment on the prediction accuracy of wind power at this time, and improving the rolling prediction accuracy of wind power. A case study is performed using historical wind power data and numerical prediction meteorological data of an actual wind farm. Study results indicate that the root mean square error of the wind power prediction model based on EFFG-based LSTM is less than 3%, while the accuracy rate and qualified rate are more than 90%. The EFFG-based LSTM model provides better performance than the support vector machine (SVM) and standard LSTM model.
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D.S., Sunil Kumar, Mamatha Mallesh, Bharath K.N., Susmitha B.C., and Kiran Gowda. "Long Short-Term Memory (LSTM) based Epileptic Seizure Recognition." International Journal of Computer Applications 184, no. 22 (July 19, 2022): 23–29. http://dx.doi.org/10.5120/ijca2022922260.
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Kratzert, Frederik, Martin Gauch, Grey Nearing, Sepp Hochreiter, and Daniel Klotz. "Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM)." Österreichische Wasser- und Abfallwirtschaft 73, no. 7-8 (May 17, 2021): 270–80. http://dx.doi.org/10.1007/s00506-021-00767-z.
Abstract:
ZusammenfassungMethoden der künstlichen Intelligenz haben sich in den letzten Jahren zu essenziellen Bestandteilen fast aller Bereiche von Wissenschaft und Technik entwickelt. Dies gilt auch für die Hydrologie: Vielschichtige neuronale Netzwerke – auch bekannt als Modelle des Deep Learning – ermöglichen hier Vorhersagen von Niederschlagsabflussmengen in zuvor unerreichter Präzision.Dieser Beitrag beleuchtet das Potenzial von Deep Learning für wasserwirtschaftliche Anwendungen. Der erste Teil des Artikels zeigt, wie sogenannte Long Short-Term Memory-Netzwerke – eine spezifisch für Zeitreihen entwickelte Methode des Deep Learnings – für die Niederschlags-Abfluss-Modellierung verwendet werden, und wie diese für eine Reihe hydrologischer Probleme bessere Ergebnisse als jedes andere bekannte hydrologische Modell erzielen. Der zweite Teil demonstriert wesentliche Eigenschaften der Long Short-Term Memory-Netzwerke. Zum einen zeigen wir, dass diese Netzwerke beliebige Daten verarbeiten können. Dies erlaubt es, mögliche synergetische Effekte aus unterschiedlichen meteorologischen Datensätzen zu extrahieren und damit die Modellgüte zu verbessern. Zum anderen stellen wir dar, wie relevante hydrologische Prozesse (wie z. B. das Akkumulieren und Schmelzen von Schnee) innerhalb der Modelle abgebildet werden, ohne dass diese spezifisch darauf trainiert wurden.
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Vinayakumar, R., K. P. Soman, Prabaharan Poornachandran, and S. Sachin Kumar. "Detecting Android malware using Long Short-term Memory (LSTM)." Journal of Intelligent & Fuzzy Systems 34, no. 3 (March 22, 2018): 1277–88. http://dx.doi.org/10.3233/jifs-169424.
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Xiaoqun, Cao, Guo Yanan, Liu Bainian, Peng Kecheng, Wang Guangjie, and Gao Mei. "ENSO prediction based on Long Short-Term Memory (LSTM)." IOP Conference Series: Materials Science and Engineering 799 (May 19, 2020): 012035. http://dx.doi.org/10.1088/1757-899x/799/1/012035.