A tool to ease and reproduce the univariate time series forecast/prediction analysis

An introduction to ForecastTB, an R package

Background

It is very important to make computational research reproducible and easy to analyze. There are several benefits in doing so and some of them are discussed here. In the field of Data Science, prediction and forecasting methods are very crucial processes. The ultimate objectives of data science projects are majorly affected by the performance of forecasting/prediction methods, therefore an accurate selection of these methods is a crucial task. Besides, nowadays, a large number of such methods are available and it is becoming a tedious task to choose a more appropriate model among the pool of them. Surely, if there is a tool that can automate this procedure with minimum efforts, it can be handy stuff for Data Science Researchers, Data Scientists, Data Analysts, and Academicians. This post is an introduction and demonstration to such a handy tool, an R package, named ForecastTB.

Decision making is one of the most crucial tasks in many domains and often decisions are based on the most accurate forecast available in the respective domains. A large number of areas, such as energy, economics, infrastructure, health, agriculture, defense, education, technology, geoscience, climate, and structural engineering among several others, are looking forward to benefits that can be achieved with time series forecasting.