Time Series Analysis: yesterday, today, tomorrow
Computers and Society
2024-06-11 v1
Abstract
Forecasts of various processes have always been a sophisticated problem for statistics and data science. Over the past decades the solution procedures were updated by deep learning and kernel methods. According to many specialists, these approaches are much more precise, stable, and suitable compared to the classical statistical linear time series methods. Here we investigate how true this point of view is.
Cite
@article{arxiv.2406.06453,
title = {Time Series Analysis: yesterday, today, tomorrow},
author = {Igor Mackarov},
journal= {arXiv preprint arXiv:2406.06453},
year = {2024}
}
Comments
Keywords: ARMA, ARIMA, SARIMA; time series sampling rate; recurrent neural networks; time series cross-validation; kernel methods for time series (Support Vector Regression, Kernel Ridge Regression). 21 pages, 13 figures