Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147902
Title: Predictive analytics of chemical material pricing
Authors: Saini, Aditi
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Saini, A. (2021). Predictive analytics of chemical material pricing. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/147902
Abstract: 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.
URI: https://hdl.handle.net/10356/147902
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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