Adaptive wavelet transform model for time series data prediction

With the development of cloud computing and big data, stock prediction has become a hot topic of research. In the stock market, the daily trading activities of stocks are carried out at different frequencies and cycles, resulting in a multi-frequency trading mode of stocks , which provides useful clues for future price trends: short-term stock forecasting relies on high-frequency trading data, while long-term forecasting pays more attention to low-frequency data. In addition, stock series have strong volatility and nonlinearity, so stock forecasting is very challenging. In order to explore the multi-frequency mode of the stock , this paper proposes an adaptive wavelet transform model (AWTM). AWTM integrates the advantages of XGboost algorithm, wavelet transform, LSTM and adaptive layer in feature selection, time–frequency decomposition, data prediction and dynamic weighting. More importantly, AWTM can automatically focus on different frequency components according to the dynamic evolution of the input sequence, solving the difficult problem of stock prediction. This paper verifies the performance of the model using S&P500 stock dataset. Compared with other advanced models, real market data experiments show that AWTM has higher prediction accuracy and less hysteresis.

[1]  Takashi Matsubara,et al.  Deep learning for stock prediction using numerical and textual information , 2016, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).

[2]  Sanjay P. Ahuja,et al.  On the Performance Evaluation of IaaS Cloud Services With System-Level Benchmarks , 2018, Int. J. Cloud Appl. Comput..

[3]  Leandro dos Santos Coelho,et al.  Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting , 2019, Eng. Appl. Artif. Intell..

[4]  Uzay Kaymak,et al.  An Automated Framework for Incorporating News into Stock Trading Strategies , 2014, IEEE Transactions on Knowledge and Data Engineering.

[5]  Hongbin Pu,et al.  Application of Wavelet Analysis to Spectral Data for Categorization of Lamb Muscles , 2014, Food and Bioprocess Technology.

[6]  Kai Chen,et al.  A LSTM-based method for stock returns prediction: A case study of China stock market , 2015, 2015 IEEE International Conference on Big Data (Big Data).

[7]  Reza Pulungan,et al.  Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network , 2017 .

[8]  Jiguo Yu,et al.  Research on direct anonymous attestation mechanism in enterprise information management , 2019, Enterp. Inf. Syst..

[9]  Xinchun Cui,et al.  Secure computation protocols under asymmetric scenarios in enterprise information system , 2019, Enterp. Inf. Syst..

[10]  Luca Di Persio,et al.  Artificial Neural Networks architectures for stock price prediction: comparisons and applications , 2016 .

[11]  Lorraine Morgan,et al.  The Impact of Cloud-Based Digital Transformation on IT Service Providers: Evidence From Focus Groups , 2017, Int. J. Cloud Appl. Comput..

[12]  Dharma P. Agrawal,et al.  Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security , 2016 .

[13]  Layla Albdour,et al.  A Heuristic Approach for Service Allocation in Cloud Computing , 2017, Int. J. Cloud Appl. Comput..

[14]  Charu C. Aggarwal,et al.  Stock Price Prediction via Discovering Multi-Frequency Trading Patterns , 2017, KDD.

[15]  Héctor Pomares,et al.  Soft-computing techniques and ARMA model for time series prediction , 2008, Neurocomputing.

[16]  Jürgen Schmidhuber,et al.  Learning to forget: continual prediction with LSTM , 1999 .

[17]  Hao Wang,et al.  Social rational secure multi‐party computation , 2014, Concurr. Comput. Pract. Exp..

[18]  Hao Hu,et al.  State-Frequency Memory Recurrent Neural Networks , 2017, ICML.

[19]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[20]  WangHao,et al.  Social rational secure multi-party computation , 2014 .

[21]  Arun Agarwal,et al.  Recurrent neural network and a hybrid model for prediction of stock returns , 2015, Expert Syst. Appl..

[22]  Basit Qureshi.  An Affordable Hybrid Cloud Based Cluster for Secure Health Informatics Research , 2018, Int. J. Cloud Appl. Comput..

[23]  Garrison W. Cottrell,et al.  A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction , 2017, IJCAI.

[24]  Khaled Day,et al.  Context-Aware Prediction Model for Offloading Mobile Application Tasks to Mobile Cloud Environments , 2019, Int. J. Cloud Appl. Comput..

[25]  Hui Liu,et al.  Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks , 2013 .

[26]  Junjie Wu,et al.  Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis , 2018, KDD.

[27]  Mohammad Mahdi Rounaghi,et al.  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 , 2016 .

[28]  Fengyin Li,et al.  A game-theoretic method based on Q-learning to invalidate criminal smart contracts , 2019, Inf. Sci..

[29]  Yong Hu,et al.  Stock trading rule discovery with an evolutionary trend following model , 2015, Expert Syst. Appl..

[30]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[31]  Zhe Liu,et al.  Fair Secure Computation with Reputation Assumptions in the Mobile Social Networks , 2015, Mob. Inf. Syst..

[32]  Long Chen,et al.  Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation , 2017 .

[33]  Saman Haratizadeh,et al.  CNNpred: CNN-based stock market prediction using a diverse set of variables , 2019, Expert Syst. Appl..

[34]  Qinghua Zhang,et al.  Wavelet networks , 1992, IEEE Trans. Neural Networks.

[35]  Adriano C. M. Pereira,et al.  Stock market's price movement prediction with LSTM neural networks , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[36]  Tao Li,et al.  Randomness invalidates criminal smart contracts , 2019, Inf. Sci..

[37]  Francisco C. Pereira,et al.  Multi-output bus travel time prediction with convolutional LSTM neural network , 2019, Expert Syst. Appl..