Food Index Forecasting

Kalyani Dacha (), Ramya Cherukupalli () and Abir Sinha ()
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Kalyani Dacha: Deloitte Consulting
Ramya Cherukupalli: Deloitte Consulting
Abir Sinha: Deloitte Consulting

A chapter in Applied Advanced Analytics, 2021, pp 125-134 from Springer

Abstract: Abstract Designing efficient and robust algorithms for accurate forecast of price index is one of the most prevalent challenges in the food market business. With the exponential rate of development, evolution of sophisticated algorithms and the availability of fast computing platforms, it has now become possible to effectively and efficiently extract, store, process and analyze food price index data with diversity in its contents. One of the leading food processing companies in the USA approached us to use the price index data for over six food categories and forecast for the upcoming 18 months so that they can get an idea about the upcoming price trends, which will help in making more informed decisions in their business. In this paper, all the data used for analyzing purpose was external (freely available) data. We used the monthly price index data for USA for raw/processed food categories, published by United States Department of Labor for the period January 2010 till May 2018. Different univariate and multivariate time series modeling approaches were used to model the price data, and best-fitted model was chosen for each of the individual categories.

Keywords: Time series analysis; Multicollinearity; Correlation (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-981-33-6656-5_11

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DOI: 10.1007/978-981-33-6656-5_11

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