Commodity Markets: Machine Learning Techniques

Chandrasekar Vuppalapati ()
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Chandrasekar Vuppalapati: San Jose State University

Chapter Chapter 4 in Machine Learning and Artificial Intelligence for Agricultural Economics, 2021, pp 219-327 from Springer

Abstract: Abstract In this chapter, machine learning techniques are introduced to perform historical commodity analysis to derive leading economic macro and micro indicators that would provide a comprehensive market view to small farmers. As part of the chapter, demand and supply framework is introduced. Next, commodity stocks to use ratio indictor are introduced and the impact of stocks to use ratio on the pricing model developed. Time series techniques are introduced to validate trend, seasonality, and stationarity of the time series. Next, the time series feature causation and correlation models and vector autoregression (VAR) are introduced. Finally, the chapter presents two time series-based use cases: predicting gold commodity prices and worldwide study of fertilizer price predict.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-77485-1_4

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DOI: 10.1007/978-3-030-77485-1_4

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