Time Series Forecasting Using Machine Learning: Case Studies with R and Iforecast

By Tsung-wu Ho

This book uses R package iForecast to conduct financial economic time series forecasting with machine learning methods, especially the generation of dynamic forecasts out-of-sample. Firstly, the machine learning methods cover, for example, enet, random forecast, gbm, autoML, and LSTM etc., including high frequency financial time series. Secondly, I will explain the problem about the generation of dynamic forecasts in machine learning framework, under which, there are no Xs, namely, input (independent) variables. This case is pretty common in real decision environment, for example, the decision-making want 6-monthforecasts in the real out-of-sample future, under with, there are no Xs available, what we can use is dynamic, or multistep, forecast.

• Format: Hardcover
• Language: English
• ISBN: 3031979451
• ISBN13: 9783031979453
• Release Date: August 2025
• Publisher: Springer
• Length: 131 Pages
• Weight: 0.84 lbs.
• Dimensions: 0.4" x 6.1" x 9.2"