An Experimental Review on Deep Learning Architectures for Time Series Forecasting

TL;DR

This paper provides an extensive review and experimental comparison of seven deep learning architectures for time series forecasting, highlighting LSTM and CNN as the most effective models in terms of accuracy and efficiency.

Contribution

It offers the most comprehensive experimental analysis of deep learning models for time series forecasting, comparing over 38,000 models across diverse datasets and configurations.

Findings

LSTM models deliver the highest forecasting accuracy.

CNN models are more efficient and have less variability.

Both LSTM and CNN outperform other architectures in the study.

Abstract

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting; and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50000 time series divided into 12 different forecasting problems. By training more than 38000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient.