Decomposition and CNN based time series forecasting methods
DOI:
https://doi.org/10.54097/2sc1s074Keywords:
Time Series Forecasting, Modal Decomposition, Convolutional Neural NetworksAbstract
Time series forecasting is usually based on historical observations and is used to predict trends and values of data for a certain period of time in the future. The usefulness of time series forecasting lies in its ability to reveal the intrinsic patterns in the data, such as trend, seasonality and periodicity, which are of great significance to people's decision making, planning and risk management. In this paper, the current research status of time series forecasting is sorted out, and the feature extraction methods involved in time series forecasting as well as the related theories such as deep learning are deeply analyzed, and the advantages and disadvantages of each method are compared, and on the basis of which, the future development trend of time series forecasting methods is outlooked.
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[1] Patton A. Copula methods for forecasting multivariate time series[J]. Handbook of economic forecasting, 2013, 2: 899-960.
[2] Zhang J, Zheng Y, Qi D. Deep spatio-temporal residual networks for citywide crowd flows prediction[C]//Proceedings of the AAAI conference on artificial intelligence. 2017, 31(1).
[3] Matsubara Y, Sakurai Y, Van Panhuis W G, et al. FUNNEL: automatic mining of spatially coevolving epidemics[C]//Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014: 105-114.
[4] Angryk R A, Martens P C, Aydin B, et al. Multivariate time series dataset for space weather data analytics[J]. Scientific data, 2020, 7(1): 227.
[5] Qin H, Zhan X, Li Y, et al. Network-wide traffic states imputation using self-interested coalitional learning[C]//Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 2021: 1370-1378.
[6] Zhao Y, Norouzi H, Azarderakhsh M, et al. Global patterns of hottest, coldest, and extreme diurnal variability on earth[J]. Bulletin of the American Meteorological Society, 2021, 102(9): E1672-E1681.
[7] Liu T, Ma X, Li S, et al. A stock price prediction method based on meta-learning and variational mode decomposition[J]. Knowledge-Based Systems, 2022, 252: 109324.
[8] Ma X, Zhao T, Guo Q, et al. Fuzzy hypergraph network for recommending top-K profitable stocks[J]. Information Sciences, 2022, 613: 239-255.
[9] Ma X, Li X, Fang L, et al. U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(13): 14255-14262.
[10] Lai G, Chang W C, Yang Y, et al. Modeling long-and short-term temporal patterns with deep neural networks[C]//The 41st international ACM SIGIR conference on research & development in information retrieval. 2018: 95-104.
[11] Y.W. Wang, Shucai Ma. Time series forecasting based on hybrid ARIMA and LSTM models[J]. Computer Applications and Software, 2021, 38(2): 291-298.
[12] Faghih S, Shah A, Wang Z, et al. Taxi and mobility: Modeling taxi demand using ARMA and linear regression[J]. Procedia Computer Science, 2020, 177: 186-195.
[13] Feng Z, Niu W. Hybrid artificial neural network and cooperation search algorithm for nonlinear river flow time series forecasting in humid and semi-humid regions[J]. Knowledge-Based Systems, 2021, 211: 106580.
[14] Sasal L, Chakraborty T, Hadid A. W-transformers: a wavelet-based transformer framework for univariate time series forecasting[C]//2022 21st IEEE international conference on machine learning and applications (ICMLA). IEEE, 2022: 671-676.
[15] Taylor S J, Letham B. Forecasting at scale[J]. The American Statistician, 2018, 72(1): 37-45.
[16] Liu M, Ren S, Ma S, et al. Gated Transformer Networks for Multivariate Time Series Classification[J]. arXiv preprint arXiv: 2103.14438, 2021.
[17] Y. Zhu, W. Zhang, Y. Chen, et al.A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment[J] EURASIP J. Wirel. Commun. Netw., 2019 (2019), pp. 1-18
[18] Tanaka G, Matsumori T, Yoshida H, et al. Reservoir computing with diverse timescales for prediction of multiscale dynamics[J]. Physical Review Research, 2022, 4(3): L032014.
[19] Zhou T, Ma Z, Wen Q, et al. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting[C]//International conference on machine learning. PMLR, 2022: 27268-27286.
[20] Liu S, Yu H, Liao C, et al. Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting[C]//# PLACEHOLDER_PARENT_METADATA_VALUE#. 2022.
[21] Zeng A, Chen M, Zhang L, et al. Are transformers effective for time series forecasting? [C]//Proceedings of the AAAI conference on artificial intelligence. 2023, 37(9): 11121-11128.
[22] Oreshkin B N, Carpov D, Chapados N, et al. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting[J]. arXiv preprint arXiv:1905.10437, 2019.
[23] Taylor S J, Letham B. Forecasting at scale[J]. The American Statistician, 2018, 72(1): 37-45.
[24] Wu H, Xu J, Wang J, et al. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting[J]. Advances in neural information processing systems, 2021, 34: 22419-22430.
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