Prediction for Time Series with CNN and LSTM

Time series data exist in various systems and affect the following management and control, in which real time series data sets are often composed of multiple variables. For predicting the future of data, not only the historical value of the variable but also other implicit influence factors should be considered. Therefore, we propose a prediction method based on the convolutional neural network (CNN) and Bi-directional long short term memory (Bi-LSTM) networks with the multidimensional variable. CNN is used to learn the horizontal relationship between variables of multivariate raw data, and Bi-LSTM is used to extract temporal relationships. Experiments are carried out with Beijing meteorological data, and the results show the high prediction accuracy of wind speed and temperature data. It is indicated that the proposed model can explore effectively the features of multivariable non-stationary time series data.

[1]  Guizhong Liu,et al.  Multivariate time series prediction based on neural networks applied to stock market , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[2]  Lianhong Cai,et al.  Question detection from acoustic features using recurrent neural network with gated recurrent unit , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[4]  Ravi Sankar,et al.  Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.

[5]  Lutgarde M. C. Buydens,et al.  Using support vector machines for time series prediction , 2003 .

[6]  Yan Liu,et al.  Recurrent Neural Networks for Multivariate Time Series with Missing Values , 2016, Scientific Reports.

[7]  Shi-Jinn Horng,et al.  Deep Air Quality Forecasting Using Hybrid Deep Learning Framework , 2018, IEEE Transactions on Knowledge and Data Engineering.

[8]  Stephan K. Chalup,et al.  Incremental training of first order recurrent neural networks to predict a context-sensitive language , 2003, Neural Networks.

[9]  Jun Suzuki,et al.  Convolution Kernels with Feature Selection for Natural Language Processing Tasks , 2004, ACL.

[10]  Philip Constantinou,et al.  ANN Prediction Models for Outdoor Environment , 2006, 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications.

[11]  Daniel F. Leite,et al.  Ensemble of evolving data clouds and fuzzy models for weather time series prediction , 2018, Appl. Soft Comput..

[12]  Soumaya Yacout,et al.  Bidirectional handshaking LSTM for remaining useful life prediction , 2019, Neurocomputing.