用于多元时间序列预测的自适应频域模型

doi:10.11896/jsjkx.210500129

摘要/Abstract

摘要： 近年来,学术和工业领域对时间序列数据的研究热潮不断增长,但其中蕴含的频率信息仍缺乏有效的建模。研究发现,时间序列预测依赖于不同的频率模式,为未来的趋势预测提供有用的线索:短期的序列预测更多依赖于高频分量,而长期预测则更多关注低频数据。为更好地挖掘时间序列的多频模式,提出了一个多特征自适应频域预测模型MAFD。该模型分为两个阶段:在第一阶段中,模型通过XGBoost算法对输入向量进行重要性度量,选择高重要性特征;在第二阶段,模型将时间序列的频率特征提取和目标序列的频域建模集成到一起,并根据时间序列对频率模式的依赖特点构建一个端到端的预测网络。MAFD的创新性体现在预测网络能够根据输入序列的动态演变自动关注不同的频率分量,从而揭示时间序列的多频模式,强化模型的学习能力。采用4种不同领域的数据集对模型进行了性能验证,实验结果表明,与现有经典的预测模型相比,MAFD具有更高的准确性和更小的滞后性。

关键词: XGBoost, 多频模式, 深度学习, 时间序列预测, 自适应建模

Abstract: In recent years,the research enthusiasm for time series data in academic and industrial fields has been increasing,but the frequency information contained in it still lacks effective modeling.The studies found that time series forecasting relies on different frequency patterns,providing useful clues for future trend forecasting:short-term series forecasting relies more on high-frequency components,while long-term forecasting focuses more on low-frequency data.In order to better mine the multi-frequency mode of time series,this paper proposes a multi-feature adaptive frequency domain prediction model (MAFD).MAFD is composed of two stages.In the first stage,it uses XGBoost algorithm to measure the importance of the input vector and selects high-importance features.In the second stage,the model integrates the frequency feature extraction of the time series and the frequency domain modeling of the target sequence,and builds an end-to-end prediction network based on the dependence of the time series on the frequency mode.The innovation of MAFD is reflected in the predictive network's ability to automatically focus on diffe-rent frequency components according to the dynamic evolution of the input sequence,thereby revealing the multi-frequency pattern of the time series and strengthening the learning ability of the model.This work uses 4 datasets from different fields to verify the performance of the model.The experimental results show that compared with the existing classic prediction models,MAFD has higher accuracy and smaller lag.

Key words: Adaptive modeling, Deep learning, Multi-frequency pattern, Time series prediction, XGBoost

中图分类号:

TP391

王晓迪, 刘鑫, 于晓. 用于多元时间序列预测的自适应频域模型[J]. 计算机科学, 2021, 48(11A): 204-210. https://doi.org/10.11896/jsjkx.210500129

WANG Xiao-di, LIU Xin, YU Xiao. Adaptive Frequency Domain Model for Multivariate Time Series Forecasting[J]. Computer Science, 2021, 48(11A): 204-210. https://doi.org/10.11896/jsjkx.210500129

参考文献

[1]KAYACAN E,ULUTAS B,KAYNAK O.Grey system theory-based models in time series prediction[J].Expert Systems with Applications,2010,37(2):1784-1789.
[2]KROGH A,LARSSON B,VON H G,et al.Predicting transmembrane protein topology with a hidden Markov model:application to complete genomes[J].Journal of Molecular Biology,2001,305(3):567-580.
[3]SINGH S N,MOHAPATRA A.Repeated wavelet transformbased ARIMA model for very short-term wind speed forecasting[J].Renewable Energy,2019,136:758-768.
[4]WEINSTEIN S,EBERT P.Data transmission by frequency-division multiplexing using the discrete Fourier transform[J].IEEE Transactions on Communication Technology,1971,19(5):628-634.
[5]DAMERVAL C,MEIGNEN S,PERRIER V.A fast algorithm for bidimensional EMD[J].IEEE Signal Processing Letters,2005,12(10):701-704.
[6]XUAN Z Y,YANG G X.Application of EMD in the Atmos-phere Time Series Prediction[J].Acta Automatica Sinica,2008,34(1):97-101.
[7]AMOR L B,LAHYANI I,JMAIEL M.Recursive and Rolling Windows for Medical Time Series Forecasting:A Comparative Study[C]// Recursive and Rolling Windows for Medical Time Series Forecasting:A Comparative Study.IEEE Computer So-ciety,2016.
[8]ZHONG X,DAVID E.Forecasting daily stock market returnusing dimensionality reduction[J].Expert Systems with Applications,2017,67:126-139.
[9]QING X Y,YU G N.Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM[J].Energy,2018,148:461-468.
[10]WANG D C,LU Y Y,ZOU Y J.Multi-output IntuitionisticFuzzy Least Squares Support Vector Regression Algorithm[J].Computer Science,2019,46(5):163-168.
[11]TANG J,LIU F,ZHANG W,et al.Lane-changes predictionbased on adaptive fuzzy neural network[J].Expert Systems with Applications,2018,91:452-463.
[12]HU Y,FENG B,ZHANG X,et al.Stock trading rule discovery with an evolutionary trend following model[J].Expert Systems with Applications,2015,42(1):212-222.
[13]ROUNAGHI M M,ZADEH F N.Investigation of market efficiency and financial stability between S&P 500 and London Stock Exchange:Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model[J].Physica A:Statistical Mechanics and its Applications,2016,456:10-21.
[14]JIDONG W,PENG L,RAN R,et al.A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree[J].Applied Sciences,2018,8(5):689.
[15]LV M Q,HONG Z X,CHEN T M.Traffic Flow ForecastingMethod Combining Spatio-Temporal Correlations and Social Events[J].Computer Science,2021,48(2):264-270.
[16]CONNOR J T,MARTIN R D,Atlas L E.Recurrent neural networks and robust time series prediction[J].IEEE Transactions on Neural Networks,1994,5(2):240-254.
[17]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[18]PETERSEN N C,RODRIGUES F,PEREIRA F C.Multi-output bus travel time prediction with convolutional LSTM neural network[J].Expert Systems with Applications,2019,120:426-435.
[19]HU H,GUO J Q.State-Frequency Memory Recurrent NeuralNetworks[C]//International Conference on Machine Learning.New York:ACM,2017:1568-1577.
[20]MOUATADID S,ADAMOWSKI J F,TIWARI M K,et al.Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting[J].Agricultural Water Management,2019,219:72-85.
[21]ZHANG L H,AGGARWAL C,QI G J.Stock price prediction via discovering multi-frequency trading patterns[C]//Procee-dings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2017:2141-2149.
[22]OUYANG L,ZHU F,XIONG G,et al.Short-term traffic flow forecasting based on wavelet transform and neural network[C]//2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).NJ:IEEE,2017:1-6.
[23]CHEN T Q,GUESTRIN C.Xgboost:A scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2016:785-794.
[24]ZHENG H,YUAN J,CHEN L.Short-term load forecasting using EMD-LSTM neural networks with a XGBoost algorithm for feature importance evaluation[J].Energies,2017,10(8):1168.
[25]HSIEH C P,CHEN Y T,BEH W K,et al.Feature SelectionFramework for XGBoost based on Electrodermal Activity in Stress Detection[C]//2019 IEEE International Workshop on Signal Processing Systems (SiPS).NJ:IEEE,2019:330-335.
[26]KE J,ZHENG H,YANG H,et al.Short-term forecasting of passenger demand under on-demand ride services:A spatio-temporal deep learning approach[J].Transportation Research,2017,85c(dec):591-608.
[27]LAI G,CHANG W C,YANG Y,et al.Modeling Long- andShort-Term Temporal Patterns with Deep Neural Networks [C]//The 41st International ACM SIGIR Conference.New York:ACM,2018.
[28]CHU B,LI Z S,ZHANG M L,et al.Improvement Research on Feature Selection Algorithm Based on Forest Optimization[J].Journal of Software,2018,29(9):2547-2558.
[29]DASH M,LIU H.Feature selection for classification[J].Intelligent Data Analysis,1997,1(3):131-156.
[30]MA X,SHA J,WANG D,et al.Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning[J].Electronic Commerce Research and Applications,2018,31:24-39.
[31]TORLAY L,PERRONE B M,THOMAS E.Machine learning-XGBoost analysis of language networks to classify patients with epilepsy[J].Brain Informatics,2017,4(3):159-169.
[32]DAUBECHIES I.The wavelet transform,time frequency localization and signal analysis[J].IEEE Transactions on Information Theory,1990,36(5):961-1005.
[33] HUANG G B.Learning capability and storage capacity of two-hidden-layer feed forward networks[J].IEEE Transactions on Neural Networks,2003,14(2):274-281.
[34]LI Y F,XIE M G,THONG N.A study of project selection and feature weighting for analogy based software cost estimation[J].Journal of Systems and Software,2009,82(2):241-252.
[35]FAMA E F.Random walks in stock market prices[J].Financial Analysts Journal,1995,51(1):75-80.
[36]AL-SHUBIRI F N.Analysis the determinants of market stockprice movements:An empirical study of Jordanian commercial banks[J].International Journal of Business and Management,2010,5(10):137.

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