Deep Learning Models for Time Series Forecasting: A Review

Abstract

Time series forecasting involves justifying assertions scientifically regarding potential states or predicting future trends of an event based on historical data recorded at various time intervals. The field of time series forecasting, supported by diverse deep learning models, has made significant advancements, rendering it a prominent research area. The broad spectra of available time series datasets serve as valuable resources for conducting extensive studies in time series analysis with varied objectives. However, the complexity and scale of time series data present challenges in constructing reliable prediction models. In this paper, our objectives are to introduce and review methodologies for modeling time series data, outline the commonly used time series forecasting datasets and different evaluation metrics. We delve into the essential architectures for trending an input dataset and offer a comprehensive assessment of the recently developed deep learning prediction models. In general, different models likely serve different design goals. We boldly examine the performance of these models under the same time series input dataset with an identical hardware computing system. The measured performance may reflect the design flexibility among all the ranked models. And through our experiments, the SCINet model performs the best in accuracy with the ETT energy input dataset. The results we obtain could give a glimpse in understanding the model design and performance relationship. Upon concluding the paper, we shall provide further discussion on future deep learning research directions in the realm of time series forecasting.