Demand forecasting using statistical and machine learning algorithms

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Vasudev, Shekhar Ramesh
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MSc in Data Analytics
Dublin Business School
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This research titled “Demand forecasting using statistical and machine learning algorithms” aims to compare the performance of traditional statistical and machine learning algorithms to forecast the demand for 50 products. Demand forecasting is a crucial part of a firm’s operations, it aims at predicting and estimating the future demand of products to aid the decision making process. The current research conducted compares the time series models with one another to identify the better model, this research focuses on implementing different algorithms to identify the variation in performance for each product. Traditional statistical models such as ARIMA and Theta method are implemented alongside Machine learning algorithms such as MLP and a new technology by H2O called Driverless AI. The accuracy of each algorithm is evaluated using the Back-testing technique by splitting the existing data into a train and test set. The models are built using the train set and the demand of the products are forecasted for the existing year, the forecasted values are compared with the values of the test set to compute the MAPE. After computing the errors of the models, the entire data is used to forecast the future demand for the products using each of these algorithms. The results show that, ARIMA accurately forecasts the demand for 10 out of the 50 products, Theta accurately forecasts the demand for 25 out of the 50 products and MLP accurately forecasts for the remaining 15 products. ARIMA couldn’t handle the products with a strong pattern and returned a generic model, Theta method and MLP are able to decompose the data and forecast for products with a strong pattern. The Driverless AI was out performed by ARIMA and Theta for all the products and the MLP for majority of the products. The conclusion of this research is that, different statistical and machine learning algorithms needs to be implemented when forecasting the demand of a set of products to identify the best performing algorithm for each product.