Time Series Forecasting using Sequence-to-Sequence Deep Learning Framework

Time series forecasting has been regarded as a key research problem in various fields. such as financial forecasting, traffic flow forecasting, medical monitoring, intrusion detection, anomaly detection, and air quality forecasting etc. In this paper, we propose a sequence-to-sequence deep learning framework for multivariate time series forecasting, which addresses the dynamic, spatial-temporal and nonlinear characteristics of multivariate time series data by LSTM based encoder-decoder architecture. Through the air quality multivariate time series forecasting experiments, we show that the proposed model has better forecasting performance than classic shallow learning and baseline deep learning models. And the predicted PM2.5 value can be well matched with the ground truth value under single timestep and multi-timestep forward forecasting conditions. The experiment results show that our model is capable of dealing with multivariate time series forecasting with satisfied accuracy.