Abstract
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.
