Time Series Forecasting (TSF) Using Various Deep Learning Models

TL;DR

This paper compares deep learning models including RNN, LSTM, GRU, and Transformer for time series forecasting using the Beijing Air Quality dataset, analyzing how window size and prediction horizon affect performance.

Contribution

It introduces a comprehensive comparison of deep learning models, especially Transformer, for TSF and explores the impact of look-back window sizes and forecast horizons.

Findings

Transformers outperform other models in MAE and RMSE.

Optimal look-back window is about one day for 1-hour ahead prediction.

Performance varies with forecast horizon and window size.

Abstract

Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as an explicit input. In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future. We also consider the performance of the recent attention-based Transformer models, which has had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.