The Evolution of Forecasting Techniques: Traditional Versus Machine Learning Methods

Title

The Evolution of Forecasting Techniques: Traditional Versus Machine Learning Methods

Creator

Thomas A.; Johnson A.; Manoj M.J.

Description

Forecasting is used effectively and efficiently to support decision-making for the future. Over time, several methods have been created to conduct forecasting. Finding a forecasting technique with the ability to provide the best estimate of the system being modeled has always been a challenge. The selection and comparison criteria for forecasting methodologies can be organized in a variety of ways. Accurate forecasting has a great demand for various fields like weather prediction, economic condition, business forecasting, demand and supply forecasts and many more. When deciding whether to utilize a certain model to predict future events, accuracy is very important. In every field, machine learning (ML) algorithms are being used to forecast future events. These algorithms can handle more complex data and make predictions that are more accurate. Based on the least values of forecasting errors, forecasters create a model to determine the best strategy for prediction. For centuries, forecasting has been used to assist individuals in making future-related decisions. In the past, forecasts were based on intuition and experience, but as technology has advanced, so have forecasting methods. Currently, advanced ML models and methods for data analysis are used to provide forecasts. To forecast the future, these models incorporate a range of inputs, including historical data, present trends, and economic indicators. Forecasting is a vital tool for businesses to employ when making future plans. It is used in a wide range of industries, from finance to weather prediction. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.

Source

Artificial Intelligence in Forecasting: Tools and Techniques, pp. 73-90.

Date

2024-01-01

Publisher

CRC Press

Coverage

Thomas A., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bengaluru, India; Johnson A., Department of Data Science, CHRIST (Deemed to be University), Pune, Lavasa, India; Manoj M.J., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 978-104005150-4; 978-103250615-9

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1201/9781003399292-5

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