Forecast Methods for Time Series Data: A Survey

被引:71
|
作者
Liu, Zhenyu [1 ]
Zhu, Zhengtong [1 ]
Gao, Jing [1 ]
Xu, Cheng [1 ]
机构
[1] Inner Mongolia Agr Univ, Coll Comp & Informat Engn, Lab Big Data Res & Applicat Agr & Anim Husb, Inner Mongolia Autonomous Reg, Hohhot 010018, Peoples R China
关键词
Time series analysis; Predictive models; Data models; Forecasting; Autoregressive processes; Atmospheric modeling; Adaptation models; forecasting; modeling; GAUSSIAN PROCESS REGRESSION; AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY; RECURRENT NEURAL-NETWORK; INFORMATION GRANULES; MODEL SELECTION; PREDICTION; ENROLLMENTS; HETEROSKEDASTICITY; EFFICIENCY; RETURNS;
D O I
10.1109/ACCESS.2021.3091162
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Research on forecasting methods of time series data has become one of the hot spots. More and more time series data are produced in various fields. It provides data for the research of time series analysis method, and promotes the development of time series research. Due to the generation of highly complex and large-scale time series data, the construction of forecasting models for time series data brings greater challenges. The main challenges of time series modeling are high complexity of time series data, low accuracy and poor generalization ability of prediction model. This paper attempts to cover the existing modeling methods for time series data and classify them. In addition, we make comparisons between different methods and list some potential directions for time series forecasting.
引用
收藏
页码:91896 / 91912
页数:17
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