| Summary: | Predictive analytics is the use of data, mathematical models and statistical algorithms to make predictions of the likelihood of future events. With the recent trend in technology and the ever-growing database of information, predictive analytics has become a catalyst to drive strategic decision making in most businesses today.
This project explores the prediction of the prices of different raw material using time series- based analysis. Each raw material is composed of different chemicals, referred to as feedstocks. We investigate univariate and multivariate time series modelling techniques to create a generalised prediction pipeline using autocorrelation and cross-correlation analysis.
We show through experiments and with the help of metrics (i.e., Mean Absolute Error and Mean Absolute Prediction Error) that how a statistical machine learning model outperforms the existing rule-based prediction model by identifying the underlying trends through correlation and time lag analysis.
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