Computer Science ›› 2025, Vol. 52 ›› Issue (2): 91-98.doi: 10.11896/jsjkx.240400127

• Database & Big Data & Data Science • Previous Articles     Next Articles

Long-term Series Forecasting Method Based on Multi-granularity Multi-scale Deep Spatio-TemporalModeling

CHEN Jiahao1, XIE Liang1, LIAO Sihao1, WU Yuchen1, XU Haijiao2   

  1. 1 College of Science,Wuhan University of Technology,Wuhan 430070,China
    2 School of Computer Science,Guangdong University of Education,Guangzhou 510303,China
  • Received:2024-04-17 Revised:2024-10-20 Online:2025-02-15 Published:2025-02-17
  • About author:CHEN Jiahao,born in 1997.His main research interests include deep learning and data mining.
    XIE Liang,born in 1987,Ph.D,asso-ciate prefessor.His main research intere-sts include multimedia retrieval and machine learning.
  • Supported by:
    Natural Science Foundation of Guangdong Province,China(2020A1515011208),Basic and Applied Basic Research Project of Guangzhou Basic Research Teaching Program(202102080353) and Characteristic Innovation Project of Natural Science in General Colleges and Universities in Guangdong Province(2019KTSCX117).

Abstract: Time series modeling has been the focus of research in a number of fields,including finance and transportation,and spatio-temporal models have received a lot of attention from researchers because of their ability to capture the complex associations and trends in time-series data more comprehensively.In recent years,long-term series forecasting based on spatio-temporal modeling has achieved remarkable results,but the existing methods are limited by multi-granularity or multi-scale studies,which cannot fully mine the spatio-temporal information of the data.To overcome this problem,a multi-granularity multi-scale deep spatio-temporal model(MMDSTM) is proposed.The model first obtains seasonal,periodic and granularity sequences by decomposing the initial data.Then,the multi-scale isometric convolution generates scale predictions,while attention-based spatio-temporal feature layers generates multi-granularity predictions.Finally,the prediction results of multi-granularity and multi-scale predictions are merged by multi-level fusion.In experiments,MMDSTM's MSE metric decreases by 6.2%,21.5% and 1% compared to other methods on stock,traffic and battery datasets,and the introduction of multi-granularity and multi-scale significantly improves the time series forecasting performance.

Key words: Multi-granularity learning, Multi-scale learning, Time series forecasting, Financial markets, Traffic flow speed

CLC Number: 

  • TP391
[1]SALINAS D,FLUNKERT V,GASTHAUS J,et al.DeepAR:Probabilistic forecasting with autoregressive recurrent networks[J].International Journal of Forecasting,2020,36(3):1181-1191.
[2]BAHDANAU D,CHO K,BENGIO Y.Neural machine transla-tion by jointly learning to align and translate[J].arXiv:1409.0473,2014.
[3]BELGHZI M I,BARATIN A,RAJESHWAR S,et al.Mutual information neural estimation[C]//International Conference on Machine Learning.PMLR,2018:531-540.
[4]BINKOWSKI M,MARTI G,DONNAT P.Autoregressive con-volutional neural networks for asynchronous time series[C]//International Conference on Machine Learning.PMLR,2018:580-589.
[5]BOX G.BOX and JENKINS:time series analysis,forecastingand control[M]//A Very British Affair:Six Britons and the Development of Time Series Analysis During the 20th Century.London:Palgrave Macmillan UK,2013:161-215.
[6]VAN GESTEL T,SUYKENS J A K,BAESTAENS D E,et al.Financial time series prediction using least squares support vector machines within the evidence framework[J].IEEE Transactions on Neural Networks,2001,12(4):809-821.
[7]BOX G E P,PIERCE D A.Distribution of residual autocorrela-tions in autoregressive-integrated moving average time series models[J].Journal of the American statistical Association,1970,65(332):1509-1526.
[8]YOO J,SOUN Y,PARK Y,et al.Accurate multivariate stockmovement prediction via data-axis transformer with multi-level contexts[C]//Proceedings of the 27th ACM SIGKDD Confe-rence on Knowledge Discovery & Data Mining.2021:2037-2045.
[9]DING Q,WU S,SUN H,et al.Hierarchical Multi-Scale Gaus-sian Transformer for Stock Movement Prediction[C]//IJCAI.2020:4640-4646.
[10]HAN M,XU M.Laplacian echo state network for multivariate time series prediction[J].IEEE Transactions on Neural Networks and Learning Systems,2017,29(1):238-244.
[11]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[12]VARGAS M R,DE LIMA B S L P,EVSUKOFFA G.Deep learning for stock market prediction from financial news articles[C]//2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications(CIVEMSA).IEEE,2017:60-65.
[13]NIEPERT M,AHMED M,KUTZKOV K.Learning convolu-tional neural networks for graphs[C]//International Conference on Machine Learning.PMLR,2016:2014-2023.
[14]CHEN W,JIANG M,ZHANG W G,et al.A novel graph convolutional feature based convolutional neural network for stock trend prediction[J].Information Sciences,2021,556:67-94.
[15]YU B,YIN H,ZHU Z.Spatio-temporal graph convolutional networks:A deep learning framework for traffic forecasting[J].arXiv:1709.04875,2017.
[16]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.
[17]SHIN S Y,SUN F K,LEE H.Temporal pattern attention for multivariate time series forecasting[J].Machine Learning,2019,108:1421-1441.
[18]VASWANI A,SHAZEERN,PARMAR N,et al.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.2017:6000-6010.
[19]WU Z,PAN S,LONG G,et al.Connecting the dots:Multiva-riate time series forecasting with graph neural networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2020:753-763.
[20]HOU M,XU C,LIU Y,et al.Stock trend prediction with multi-granularity data:A contrastive learning approach with adaptive fusion[C]//Proceedings of the 30th ACM International Confe-rence on Information & Knowledge Management.2021:700-709.
[21]HOU M,XU C,LI Z,et al.Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction[C]//Proceedings of the ACM Web Conference 2022.2022:112-121.
[22]WANG M,WANG H,ZHANG F.FAMC-Net:Frequency Domain Parity Correction Attention and Multi-Scale Dilated Convolution for Time Series Forecasting[C]//Proceedings of the 32nd ACM International Conference on Information and Know-ledge Management.2023:2554-2563.
[23]WANG H,PENG J,HUANG F,et al.Micn:Multi-scale local and global context modeling for long-term series forecasting[C]//The Eleventh International Conference on Learning Re-presentations.2022.
[24]JOUIN M,GOURIVEAU R,HISSEL D,et al.Prognostics ofPEM fuel cell in a particle filtering framework[J].International Journal of Hydrogen Energy,2014,39(1):481-494.
[25]CHUNG H,SHIN K.Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction[J].Neural Computing and Applications,2020,32(12):7897-7914.
[26]FAN J,SHEN Y.StockMixer:A Simple Yet Strong MLP-Based Architecture for Stock Price Forecasting[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2024:8389-8397.
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