深度学习应用于时序预测研究综述

Review of Deep Learning Applied to Time Series Prediction

时间序列一般是指对某种事物发展变化过程进行观测并按照一定频率采集得出的一组随机变量。时间序列预测的任务就是从众多数据中挖掘出其蕴含的核心规律并且依据已知的因素对未来的数据做出准确的估计。由于大量物联网数据采集设备的接入、多维数据的爆炸增长和对预测精度的要求愈发苛刻,经典的参数模型以及传统机器学习算法难以满足预测任务的高效率和高精度需求。近年来,以卷积神经网络、循环神经网络和Transformer模型为代表的深度学习算法在时间序列预测任务中取得了丰硕的成果。为进一步促进时间序列预测技术的发展,综述了时间序列数据的常见特性、数据集和模型的评价指标,并以时间和算法架构为研究主线,实验对比分析了各预测算法的特点、优势和局限;着重介绍对比了多个基于Transformer模型的时间序列预测方法;最后结合深度学习应用于时间序列预测任务存在的问题与挑战,对未来该方向的研究趋势进行了展望。

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.