Machine Learning in Demand Forecasting - A Review

9 Pages Posted: 25 Nov 2020

See all articles by Sumaiya Farzana G

Sumaiya Farzana G

aB.S Abdur Rahman Crescent Institute of Science and Technology

Prakash N

B.S Abdur Rahman Crescent Institute of Science and Technology

Date Written: November 19, 2020

Abstract

Demand forecasting is of great importance in many industries such as agriculture, electric power, tourism, retail sales and manufacturing companies, etc. It plays a vital role in the decision making of every business. This paper survey various state of art methods on demand forecasting with a focus on machine learning. The machine learning techniques have been categorized into three categories namely time series analysis, regression-based methods and supervised/unsupervised models. The pros and cons of the various machine learning techniques are discussed and their performance measures are compared. The comparison conceals that LSTM has a notable result, but its computation time is higher than any other method. Another field of future research includes regression-based methods, hybrid models and ensemble models. This study gives the reader an idea of demand forecasting in the field of machine learning.

Keywords: Demand Forecasting; Machine Learning; LSTM; Regression-Based Methods; Hybrid Model; Ensemble Model

Suggested Citation

G, Sumaiya Farzana and N, Prakash, Machine Learning in Demand Forecasting - A Review (November 19, 2020). Proceedings of the 2nd International Conference on IoT, Social, Mobile, Analytics & Cloud in Computational Vision & Bio-Engineering (ISMAC-CVB 2020), Available at SSRN: https://ssrn.com/abstract=3733548 or http://dx.doi.org/10.2139/ssrn.3733548

Sumaiya Farzana G (Contact Author)

aB.S Abdur Rahman Crescent Institute of Science and Technology ( email )

Prakash N

B.S Abdur Rahman Crescent Institute of Science and Technology ( email )

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