A Deep Structural Model for Analyzing Correlated Multivariate Time Series

TL;DR

This paper introduces a deep structural model for analyzing correlated multivariate time series, effectively capturing trend, seasonality, and event components to improve forecasting accuracy across diverse real-world datasets.

Contribution

The proposed model uniquely combines CNN, LSTM, and Fourier-based seasonality components to handle correlated multivariate time series and explicitly extract different temporal features.

Findings

Outperforms state-of-the-art methods on multiple datasets

Effectively captures trend, seasonality, and event components

Demonstrates improved forecasting accuracy in real-world applications

Abstract

Multivariate time series are routinely encountered in real-world applications, and in many cases, these time series are strongly correlated. In this paper, we present a deep learning structural time series model which can (i) handle correlated multivariate time series input, and (ii) forecast the targeted temporal sequence by explicitly learning/extracting the trend, seasonality, and event components. The trend is learned via a 1D and 2D temporal CNN and LSTM hierarchical neural net. The CNN-LSTM architecture can (i) seamlessly leverage the dependency among multiple correlated time series in a natural way, (ii) extract the weighted differencing feature for better trend learning, and (iii) memorize the long-term sequential pattern. The seasonality component is approximated via a non-liner function of a set of Fourier terms, and the event components are learned by a simple linear function of regressor encoding the event dates. We compare our model with several state-of-the-art methods through a comprehensive set of experiments on a variety of time series data sets, such as forecasts of Amazon AWS Simple Storage Service (S3) and Elastic Compute Cloud (EC2) billings, and the closing prices for corporate stocks in the same category.