Feng Zhou
ABSTRACT
Time series processing is a vital issue that is encountered in various fields. However, such data are mostly non-stationary on account of the fact that they are affected by a variety of factors. In this paper, we present a supervised strategy by integrating the iterative filtering (IF) method and convolution neural network (CNN) to automatically extract features of time series, where the IF technique can decompose the raw time series into intrinsic mode functions (IMFs), and then the CNN aims to extract the features from the images constructed by the IMFs under specific task. To illustrate the performance of the proposed strategy, we apply it in one-step and multi-step predictive tasks on the national association of securities dealers automated quotations (NASDAQ) data. Furthermore, we compute the importance of the extracted and raw features by the combined decision trees, such as random forest (RF) and gradient boosted decision trees (GBDT). The results indicate the significant improvement of the proposed strategy.
- Oh, K. J., & Kim, K. J. 2002. Analyzing stock market tick data using piecewise nonlinear model. Expert Systems with Applications, 22, 249--255.Google Scholar
Cross Ref
- Wang, Y. F. 2003. Mining stock price using fuzzy rough set system. Expert Systems with Applications, 24, 13--23.Google ScholarCross Ref
- Sarantis, N. 2001. Nonlinearities, cyclical behaviour and predictability in stock markets: international evidence. International Journal of Forecasting, 17, 459--482.Google ScholarCross Ref
- Franses, P. H., & Ghijsels, H. 1999. Additive outliers, garch and forecasting volatility. International Journal of Forecasting, 15, 1--9.Google ScholarCross Ref
- Kim, K. J. 2003. Financial time series forecasting using support vector machines. Neurocomputing, 55, 307--319.Google ScholarCross Ref
- Chen, T., & Guestrin, C. 2016. Xgboost: A scalable tree boosting system. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785--794. Google ScholarDigital Library
- Chen, A. S., Leung, M. T., & Daouk, H. 2003. Application of neural networks to an emerging financial market: Forecasting and trading the taiwan stock index. Computers and Operations Research, 30, 901--923. Google ScholarDigital Library
- Rather, A. M., Agarwal, A., & Sastry, V. N. 2015. Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42, 3234--3241. Google ScholarDigital Library
- Lecun, Y., & Bengio, Y. 1998. Convolutional networks for images, speech, and time series. MIT Press.Google Scholar
- Gamboa, J. C. B. 2017. Deep learning for time-series analysis. arXiv preprint arXiv:1701.01887.Google Scholar
- Borovykh, A., Bohte, S., & Oosterlee, C. W. 2017. Conditional time series forecasting with convolutional neural networks. arXiv:1703.04691.Google Scholar
- Mittelman, R. 2015. Time-series modeling with undecimated fully convolutional neural networks. arXiv:1508.00317Google Scholar
- Cicone, A., Liu, J., & Zhou, H. 2016. Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis. Applied and Computational Harmonic Analysis, 41, 384--411.Google ScholarCross Ref
- Huang N. E., Zheng S., Long S. R., et al. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings Mathematical Physical and Engineering Sciences, 454(1971):903--995.Google ScholarCross Ref
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. International Conference on Neural Information Processing System, 1097--1105. Google ScholarDigital Library
- Ioffe, S., & Szegedy, C. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. International Conference on Machine Learning, 37, 448--456. Google ScholarDigital Library
Index Terms
- A Strategy Integrating Iterative Filtering and Convolution Neural Network for Time Series Feature Extraction
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