Review of Deep Learning Applied to Time Series Prediction

Abstract

The time series is generally a set of random variables that are observed and collected at a certain frequency in the course of something??s development. The task of time series forecasting is to extract the core patterns from a large amount of data and to make accurate estimates of future data based on known factors. Due to the access of a large number of IoT data collection devices, the explosive growth of multidimensional data and the increasingly demanding requirements for prediction accuracy, it is difficult for classical parametric models and traditional machine learning algorithms to meet high efficiency and high accuracy requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Trans-former models have achieved fruitful results in time series forecasting tasks. To further promote the development of time series prediction technology, common characteristics of time series data, evaluation indexes of datasets and models are reviewed, and the characteristics, advantages and limitations of each prediction algorithm are experimentally compared and analyzed with time and algorithm architecture as the main research line. Several time series prediction methods based on Transformer model are highlighted and compared. Finally, according to the problems and challenges of deep learning applied to time series prediction tasks, this paper provides an outlook on the future research trends in this direction.

Keywords

• time series data; time series prediction; deep learning; transformer model