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commodity-price-forecasting

Compare efficency of machine learning prediction methods (e.g. LTSM, Transfomer) to classic ARIMA/GARCH approach, based commodity pricing problem. Analyzed on daily data from last ~20yrs on cocoa,milk,wheat,palm_oil,sugar.

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ML pipeline for commodity price forecasting — comparing statistical, machine learning and deep learning models across 5 commodities and 3 time horizons. Experimenting with hybrid approach (statistical-ML).

Topics

arimacommodity-macommodity-pricesgarchhydraltsmmaster-thesismlflowoptunapythonpytorchrandom-forestrolling-windowstcntime-seriestime-series-forecastingtransformerxgboost

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