Regression Modelling for Predictions in Time Series

Description

Forecasting is perhaps the most common application of machine learning in the real world. Businesses forecast product demand, governments forecast economic and population growth, meteorologists forecast the weather. Likewise, Time series regression is a statistical method for predicting a future response based on the response history and the transfer of dynamics from relevant predictors. Time series regression can help you understand and predict the behavior of dynamic systems from experimental or observational data. Common uses of time series regression include modeling and forecasting of economic, financial, biological, and engineering systems. This book “Regression Modelling for Predictions in Time Series” focuses on the application of modern machine learning methods to time series data with the goal of producing the most accurate predictions. The aim of this book is to give an unified approach to the solution of statistical problems for such time series models, and mainly to problems of the estimation of unknown parameters of models and to problems of the prediction of time series modeled by regression models.