Predictive Analysis of Fuel Prices Using Machine Learning

Sales forecasting is seen as one of the most important indicators of the wellbeing of a business. The ability to accurately predict the sales figures can influence the success of a business. This can be tied to the stock levels of products, however businesses experience a number of problems, such as stock shortages that stem from not being able to accurately predict customer spending in advance. If there is understocking, there will be discouraged customers and overstocking will lead to unnecessary stock-holding costs. Several concepts have been introduced to help find useful insights from Big data to predict customer spending. Some of these are Predictive Analysis and Machine Learning. This paper focuses on the real-time prediction of fuel prices. The predictive analytics model implemented in this study takes into consideration the external factors such as time, the consumer price index, exchange rates, interest rates and oil prices. Relevant data, obtained from an agriculture organization and from various other sources were integrated into a single dataset. An exploratory analysis, using an Elman neural network was carried out to understand the relationships that exist between the datasets. Predictions were generated in two modes namely, daily and monthly fuel prices. The evaluation and validation of the model indicated accurate daily sales and spike predictions of diesel fuel.