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Thematische Bibliographien / Neural time series / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Neural time series“

Autor: Grafiati

Veröffentlicht am 18. Mai 2024

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1

Rudenko, Oleg, Oleksandr Bezsonov und Oleksandr Romanyk. „Neural network time series prediction based on multilayer perceptron“. Development Management 17, Nr. 1 (07.05.2019): 23–34. http://dx.doi.org/10.21511/dm.5(1).2019.03.

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Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.

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2

Golovenko, A. O., und A. A. Kopyrkin. „Neural Network Forecasting of Time Series“. Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 19, Nr. 4 (2019): 124–31. http://dx.doi.org/10.14529/ctcr190412.

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3

Kolarik, Thomas, und Gottfried Rudorfer. „Time series forecasting using neural networks“. ACM SIGAPL APL Quote Quad 25, Nr. 1 (Oktober 1994): 86–94. http://dx.doi.org/10.1145/190468.190290.

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4

Xinhui, Wen, und Chen Kaizhou. „Time series neural network forecasting methods“. Journal of Electronics (China) 12, Nr. 1 (Januar 1995): 1–8. http://dx.doi.org/10.1007/bf02684561.

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5

Liu, Zhi Cheng. „Real Time Prediction Method of Sensor Output Time Series“. Advanced Materials Research 912-914 (April 2014): 1322–26. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1322.

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In order to improve the real time prediction precision of sensor output time series, the predictable inner mechanism of time series is analyzed, and a method using wavelet filtering and neural network is proposed. Sensor output time series are first handled with wavelet filtering, and then predicted by neural network method. The proposed method can eliminate effect of measurement noise on prediction precision. Simulation experiment shows a higher prediction precision by the method. A new idea is given to increase prediction precision of sensor output time series by neural network-based methods.

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6

Panigrahi, Sibarama, Yasobanta Karali und H. S. Behera. „Time Series Forecasting using Evolutionary Neural Network“. International Journal of Computer Applications 75, Nr. 10 (23.08.2013): 13–17. http://dx.doi.org/10.5120/13146-0553.

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7

Kim, JongHwa, Jong Hoo Choi und Changwan Kang. „Time Series Prediction Using Recurrent Neural Network“. Korean Data Analysis Society 21, Nr. 4 (31.08.2019): 1771–79. http://dx.doi.org/10.37727/jkdas.2019.21.4.1771.

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8

Zhang, Dongqing, und Yubing Han. „Time Series Prediction with RBF Neural Networks“. Information Technology Journal 12, Nr. 14 (01.07.2013): 2815–19. http://dx.doi.org/10.3923/itj.2013.2815.2819.

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9

Sako, Kady, Berthine Nyunga Mpinda und Paulo Canas Rodrigues. „Neural Networks for Financial Time Series Forecasting“. Entropy 24, Nr. 5 (07.05.2022): 657. http://dx.doi.org/10.3390/e24050657.

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Financial and economic time series forecasting has never been an easy task due to its sensibility to political, economic and social factors. For this reason, people who invest in financial markets and currency exchange are usually looking for robust models that can ensure them to maximize their profile and minimize their losses as much as possible. Fortunately, recently, various studies have speculated that a special type of Artificial Neural Networks (ANNs) called Recurrent Neural Networks (RNNs) could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD, using the RNNs model and its variants: the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The results show that the GRU gives the overall best results, especially for the univariate out-of-sample forecasting for the currency exchange rates and multivariate out-of-sample forecasting for the stock market indexes.

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10

Pérez-Chavarría, M. A. „Time series prediction using artificial neural networks“. Ciencias Marinas 28, Nr. 1 (01.02.2002): 67–77. http://dx.doi.org/10.7773/cm.v28i1.205.

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11

Zhao, Bendong, Huanzhang Lu, Shangfeng Chen, Junliang Liu und Dongya Wu. „Convolutional neural networks for time series classification“. Journal of Systems Engineering and Electronics 28, Nr. 1 (20.02.2017): 162–69. http://dx.doi.org/10.21629/jsee.2017.01.18.

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12

Sinha, M., M. M. Gupta und P. N. Nikiforuk. „HYBRID NEURAL MODELS FOR TIME-SERIES FORECASTING“. IFAC Proceedings Volumes 35, Nr. 1 (2002): 427–31. http://dx.doi.org/10.3182/20020721-6-es-1901.00724.

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13

Lesher, S., Li Guan und A. H. Cohen. „Symbolic time-series analysis of neural data“. Neurocomputing 32-33 (Juni 2000): 1073–81. http://dx.doi.org/10.1016/s0925-2312(00)00281-2.

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14

Hüsken, Michael, und Peter Stagge. „Recurrent neural networks for time series classification“. Neurocomputing 50 (Januar 2003): 223–35. http://dx.doi.org/10.1016/s0925-2312(01)00706-8.

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15

Conway, A. J., K. P. Macpherson und J. C. Brown. „Delayed time series predictions with neural networks“. Neurocomputing 18, Nr. 1-3 (Januar 1998): 81–89. http://dx.doi.org/10.1016/s0925-2312(97)00070-2.

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16

Wong, F. S. „Time series forecasting using backpropagation neural networks“. Neurocomputing 2, Nr. 4 (Juli 1991): 147–59. http://dx.doi.org/10.1016/0925-2312(91)90045-d.

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17

Macpherson, K. „Generalisation in neural network time series analysis“. Vistas in Astronomy 38 (Januar 1994): 341–49. http://dx.doi.org/10.1016/0083-6656(94)90045-0.

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18

Zhang, G. P., und D. M. Kline. „Quarterly Time-Series Forecasting With Neural Networks“. IEEE Transactions on Neural Networks 18, Nr. 6 (November 2007): 1800–1814. http://dx.doi.org/10.1109/tnn.2007.896859.

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19

Hill, Tim, Marcus O'Connor und William Remus. „Neural Network Models for Time Series Forecasts“. Management Science 42, Nr. 7 (Juli 1996): 1082–92. http://dx.doi.org/10.1287/mnsc.42.7.1082.

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20

Sokolov, Alik, Jonathan Mostovoy, Brydon Parker und Luis Seco. „Neural Embeddings of Financial Time-Series Data“. Journal of Financial Data Science 2, Nr. 4 (24.08.2020): 33–43. http://dx.doi.org/10.3905/jfds.2020.1.041.

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21

Marchenko, Olesya V. „RESEARCH OF TIME SERIES USING NEURAL NETWORKS“. Scholarly Notes of Komsomolsk-na-Amure State Technical University, Nr. 7 (2022): 77–85. http://dx.doi.org/10.17084/20764359-2022-63-77.

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22

Song, Mingli, Yan Li und Witold Pedrycz. „Time series prediction with granular neural networks“. Neurocomputing 546 (August 2023): 126328. http://dx.doi.org/10.1016/j.neucom.2023.126328.

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23

Tolstykh, Viktor N. „Neural networks for a time series extrapolation“. H&ES Research 15, Nr. 6 (2023): 4–11. http://dx.doi.org/10.36724/2409-5419-2023-15-6-4-11.

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24

Velásquez, Juan David, Fernán Alonso Villa und Reinaldo C. Souza. „Time series forecasting using cascade correlation networks“. Ingeniería e Investigación 30, Nr. 1 (01.01.2010): 157–62. http://dx.doi.org/10.15446/ing.investig.v30n1.15226.

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Artificial neural networks, especially multilayer perceptrons, have been recognised as being a powerful technique for forecasting nonlinear time series; however, cascade-correlation architecture is a strong competitor in this task due to it incorporating several advantages related to the statistical identification of multilayer perceptrons. This paper compares the accuracy of a cascade-correlation neural network to the linear approach, multilayer perceptrons and dynamic architecture for artificial neural networks (DAN2) to determine whether the cascade-correlation network was able to forecast the time series being studied with more accuracy. It was concluded that cascade-correlation was able to forecast time series with more accuracy than other approaches.

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25

Liu, Chien-Liang, Wen-Hoar Hsaio und Yao-Chung Tu. „Time Series Classification With Multivariate Convolutional Neural Network“. IEEE Transactions on Industrial Electronics 66, Nr. 6 (Juni 2019): 4788–97. http://dx.doi.org/10.1109/tie.2018.2864702.

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26

Li, Ge, Hu Jing und Chen Guangsheng. „Fusion Process Neural Networks Classifier Oriented Time Series“. Journal of Computational and Theoretical Nanoscience 16, Nr. 10 (01.10.2019): 4059–63. http://dx.doi.org/10.1166/jctn.2019.6946.

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Based on the consideration of complementary advantages, different wavelet, fractal and statistical methods are integrated to complete the classification feature extraction of time series. Combined with the advantage of process neural networks that processing time-varying information, we propose a fusion classifier with process neural network oriented time series. Be taking advantage of the multi-fractal processing nonlinear feature of time series data classification, the strong adaptability of the wavelet technique for time series data and the effect of statistical features on the classification of time series data, we can achieve the classification feature extraction of time series. Additionally, using time-varying input characteristics of process neural networks, the pattern matching of timevarying input information and space-time aggregation operation is realized. The feature extraction of time series with the above three methods is fused to the distance calculation between time-varying inputs and cluster space in process neural networks. We provide the process neural network fusion to the learning algorithm and optimize the calculation process of the time series classifier. Finally, we report the performance of our classification method using Synthetic Control Charts data from the UCI dataset and illustrate the advantage and validity of the proposed method.

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27

Marzban, Forough, Ramin Ayanzadeh und Pouria Marzban. „Discrete Time Dynamic Neural Networks for Predicting Chaotic Time Series“. Journal of Artificial Intelligence 7, Nr. 1 (15.12.2013): 24–34. http://dx.doi.org/10.3923/jai.2014.24.34.

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28

Hansen, James V., und Ray D. Nelson. „Time-series analysis with neural networks and ARIMA-neural network hybrids“. Journal of Experimental & Theoretical Artificial Intelligence 15, Nr. 3 (Januar 2003): 315–30. http://dx.doi.org/10.1080/0952813031000116488.

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29

Jia, Jia. „Financial Time Series Prediction Based on BP Neural Network“. Applied Mechanics and Materials 631-632 (September 2014): 31–34. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.31.

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BP neural network is promising methods for the prediction of financial time series because it use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies BP neural network to predicting the stock price index. In addition, this study examines the feasibility of applying BP neural network in financial forecasting. The experimental results show that BP neural network provides a promising alternative to stock market prediction.

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30

Campos, Lídio Mauro Lima, Jherson Haryson Almeida Pereira, Danilo Souza Duarte und Roberto Célio Limão Oliveira. „Evolving deep neural networks for Time Series Forecasting“. Learning and Nonlinear Models 18, Nr. 2 (30.06.2021): 40–55. http://dx.doi.org/10.21528/lnlm-vol18-no2-art4.

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The aim of this paper is to introduce a biologically inspired approach that can automatically generate Deep Neural networks with good prediction capacity, smaller error and large tolerance to noises. In order to do this, three biological paradigms were used: Genetic Algorithm (GA), Lindenmayer System and Neural Networks (DNNs). The final sections of the paper present some experiments aimed at investigating the possibilities of the method in the forecast the price of energy in the Brazilian market. The proposed model considers a multi-step ahead price prediction (12, 24, and 36 weeks ahead). The results for MLP and LSTM networks show a good ability to predict peaks and satisfactory accuracy according to error measures comparing with other methods.

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31

Rafsanjani, Marjan Kuchaki, und Meysam Samareh. „Chaotic time series prediction by artificial neural networks“. Journal of Computational Methods in Sciences and Engineering 16, Nr. 3 (13.10.2016): 599–615. http://dx.doi.org/10.3233/jcm-160643.

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32

Uribarri, Gonzalo, und Gabriel B. Mindlin. „Dynamical time series embeddings in recurrent neural networks“. Chaos, Solitons & Fractals 154 (Januar 2022): 111612. http://dx.doi.org/10.1016/j.chaos.2021.111612.

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33

Hofert, Marius, Avinash Prasad und Mu Zhu. „Multivariate time-series modeling with generative neural networks“. Econometrics and Statistics 23 (Juli 2022): 147–64. http://dx.doi.org/10.1016/j.ecosta.2021.10.011.

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34

Gallardo Del Ángel, Roberto. „Financial time series forecasting using Artificial Neural Networks“. Revista Mexicana de Economía y Finanzas 15, Nr. 1 (17.12.2019): 105–22. http://dx.doi.org/10.21919/remef.v15i1.376.

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Este documento contiene una predicción financiera utilizando Redes Neuronales Artificiales. Hacemos nuestro análisis utilizando el algoritmo de Backpropagation tradicional y luego Backpropagation Resiliente para estimar los pesos en las redes. El uso del algorithm de Bacpropagation Resiliente permite resolver el problema de la determinación de la tasa de aprendizaje. Ambos algoritmos son bastante consistentes y arrojan predicciones similares. Analizamos seis índices principales de los mercados bursátiles de Europa, Asia y América del Norte para generar índices de aciertos que puedan compararse entre mercados. Usamos precios de cierre diarios para construir una variable de dependiente para dirigir el aprendizaje (aprendizaje supervisado) y una matriz de variables de características construidas utilizando indicadores de análisis técnico. El rango de datos de la serie de tiempo va desde Enero de 2000 a Junio de 2019, un periodo de grandes fluctuaciones debido a mejoras en la tecnología de la información y una alta movilidad de capital. En lugar de la predicción en sí misma, el objetivo científico es evaluar la importancia relativa de las variables independientes que permiten la predicción. Utilizamos dos medidas de contribución utilizadas en la literatura para evaluar la relevancia de cada variable para los seis mercados financieros analizados. Descubrimos que estas medidas no siempre son consistentes, por lo que construimos una medida de contribución simple que le da a cada peso una interpretación geométrica. Proporcionamos algunas pruebas de que la tasa de cambio (ROC) es la herramienta de predicción más útil para cuatro índices generales, con las excepciones siendo el índice Hang Sheng y EU50, en donde el fastK es el más destacado.

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35

Heng-Chao, Li, Zhang Jia-Shu und Xiao Xian-Ci. „Neural Volterra filter for chaotic time series prediction“. Chinese Physics 14, Nr. 11 (31.10.2005): 2181–88. http://dx.doi.org/10.1088/1009-1963/14/11/007.

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36

Sultan, Muzakir Hi. „Optimasi parameter neural network pada data time series“. CAUCHY 3, Nr. 2 (10.05.2014): 59. http://dx.doi.org/10.18860/ca.v3i2.2574.

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Gempa bumi merupakan suatu pergerakan tanah yang terjadi secara tiba-tiba hingga menimbulkan getaran, besarnya kekuatan gempa dapat mengakibatkan bencana baik kerusakan maupun korban jiwa. Untuk mengantisipasi bencana yang akan datang maka diperlukan suatu model khususnya untuk meramalkan besarnya kekuatan gempa. Pada penelitian ini, digunakan model ARIMA dan model kombinasi dari Neural Network-Algoritma Genetik (NN-GA) untuk memprediksi rata-rata kekuatan gempa bumi setiap bulan khususnya yang terjadi di wilayah Maluku Utara. Data yang digunakan adalah data kekuatan gempa berdasarkan skala richter yang diperoleh dari Badan Meteorologi, Klimatologi dan Geofisika (BMKG) kota Ternate. Sebagai input pada model ARIMA dan NN-GA digunakan rata-rata kekuatan gempa bumi 36 bulan dan rata-rata kekuatan gempa 36 bulan berikutnya digunakan sebagai target untuk prediksi. Untuk meng-update parameter (bobot) dari Neural Network digunakan metode Gradient Descent dan untuk mendapatkan parameter yang lebih optimal pada layer Output, maka di diterapkan Algoritma Genetik. Hasil peramalan dari kedua model kemudian dibandingkan dan model terbaik ditentukan dari nilai Mean square Error (MSE) yang terkecil. dari hasil peramalan dengan model ARIMA diperoleh MSE sebesar 1.0125, sedangkan pada model NN-GA diperoleh MSE sebesar 0.9196. Nilai tersebut, menunjukkan bahwa model NN-GA lebih baik dari model ARIMA untuk peramalan rata-rata kekuatan gempa bumi beberapa bulan ke depan

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37

Gang, Ding, Zhong Shi-Sheng und Li Yang. „Time series prediction using wavelet process neural network“. Chinese Physics B 17, Nr. 6 (Juni 2008): 1998–2003. http://dx.doi.org/10.1088/1674-1056/17/6/011.

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38

Connor, J. T., R. D. Martin und L. E. Atlas. „Recurrent neural networks and robust time series prediction“. IEEE Transactions on Neural Networks 5, Nr. 2 (März 1994): 240–54. http://dx.doi.org/10.1109/72.279188.

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39

Warsito, Budi, Rukun Santoso, Suparti und Hasbi Yasin. „Cascade Forward Neural Network for Time Series Prediction“. Journal of Physics: Conference Series 1025 (Mai 2018): 012097. http://dx.doi.org/10.1088/1742-6596/1025/1/012097.

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40

Alzahrani, A., J. W. Kimball und C. Dagli. „Predicting Solar Irradiance Using Time Series Neural Networks“. Procedia Computer Science 36 (2014): 623–28. http://dx.doi.org/10.1016/j.procs.2014.09.065.

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41

Ke-Ping, Li, und Chen Tian-Lun. „Nonlinear Time Series Prediction Using Chaotic Neural Networks“. Communications in Theoretical Physics 35, Nr. 6 (15.06.2001): 759–62. http://dx.doi.org/10.1088/0253-6102/35/6/759.

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42

Nie, Junhong. „Nonlinear time-series forecasting: A fuzzy-neural approach“. Neurocomputing 16, Nr. 1 (Juli 1997): 63–76. http://dx.doi.org/10.1016/s0925-2312(97)00019-2.

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43

Balkin, Sandy D., und J. Keith Ord. „Automatic neural network modeling for univariate time series“. International Journal of Forecasting 16, Nr. 4 (Oktober 2000): 509–15. http://dx.doi.org/10.1016/s0169-2070(00)00072-8.

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44

Monforte, Frank A. „Predictive Modular Neural Networks – Applications to Time Series“. International Journal of Forecasting 18, Nr. 1 (Januar 2002): 157–58. http://dx.doi.org/10.1016/s0169-2070(01)00129-7.

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45

Meade, Nigel. „Neural network time series forecasting of financial markets“. International Journal of Forecasting 11, Nr. 4 (Dezember 1995): 601–2. http://dx.doi.org/10.1016/s0169-2070(95)90005-5.

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46

Teixeira, J. P., und P. O. Fernandes. „Tourism time series forecast with artificial neural networks“. Tékhne 12, Nr. 1-2 (Januar 2014): 26–36. http://dx.doi.org/10.1016/j.tekhne.2014.08.001.

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47

Fullah Kamara, Amadu, Enhong Chen, Qi Liu und Zhen Pan. „Combining contextual neural networks for time series classification“. Neurocomputing 384 (April 2020): 57–66. http://dx.doi.org/10.1016/j.neucom.2019.10.113.

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48

Kehagias, Ath, und Vas Petridis. „Predictive Modular Neural Networks for Time Series Classification“. Neural Networks 10, Nr. 1 (Januar 1997): 31–49. http://dx.doi.org/10.1016/s0893-6080(96)00040-8.

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49

Badran, F., und S. Thiria. „Neural Network Smoothing in Correlated Time Series Context“. Neural Networks 10, Nr. 8 (November 1997): 1445–53. http://dx.doi.org/10.1016/s0893-6080(97)00007-5.

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50

Liang, Faming. „Bayesian neural networks for nonlinear time series forecasting“. Statistics and Computing 15, Nr. 1 (Januar 2005): 13–29. http://dx.doi.org/10.1007/s11222-005-4786-8.

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