Temporal Tensor Transformation Network for Multivariate Time Series Prediction

--Multivariate time series prediction has applications in a wide variety of domains and is considered to be a very challenging task, especially when the variables have correlations and exhibit complex temporal patterns, such as seasonality and trend. Many existing methods suffer from strong statistical assumptions, numerical issues with high dimensionality, manual feature engineering efforts, and scalability. In this work, we present a novel deep learning architecture, known as T emporal T ensor Transformation Network, which transforms the original multivariate time series into a higher order of tensor through the proposed T emporal-Slicing Stack Transformation. This yields a new representation of the original multivariate time series, which enables the convolution kernel to extract complex and nonlinear features as well as variable interactional signals from a relatively large temporal region. Experimental results show that T emporal T ensor Transformation Network outperforms several state-of-the-art methods on window-based predictions across various tasks. The proposed architecture also demonstrates robust prediction performance through an extensive sensitivity analysis. Index T erms--multivariate time series, prediction, convolution, deep learning, tensor transformation I. I NTRODUCTION Multivariate time series analysis has gained wide spread applications in many fields, e.g., financial market prediction, weather forecasting, and energy consumption prediction. It is used to model and explain the underlying temporal patterns among a group of time series variables in dynamical systems. V arious methods have been proposed to predict multivariate time series based on statistical modeling and deep neural networks. Classical statistical models assume that the time series is stationary, i.e., the summary statistics of data points are consistent over time. Preprocessing procedures are usually needed to remove trend, seasonality, and other time-dependent structures from the raw series in order to make the data stationary. In addition, these models also assume the independence condition in the underlying linear regression problem, i.e., the random errors in the model are not correlated over time.