Applying unprocessed companydata to time series forecasting : An investigative pilot study

Abstract: Demand forecasting for sales is a widely researched topic that is essential for a business to prepare for market changes and increase profits. Existing research primarily focus on data that is more suitable for machine learning applications compared to the data accessible to companies lacking prior machine learning experience. This thesis performs demand forecasting on a known sales dataset and a dataset accessed directly from such a company, in the hopes of gaining insights that can help similar companies better utilize machine learning in their business model. LigthGBM, Linear Regression and Random Forest models are used along with several regression error metrics and plots to compare the performance of the two datasets. Both data sets are preprocessed into the same structure based on equivalent features found in each set. The company dataset is determined to be unfit for machine learning forecasting even after preprocessing measures and multiple possible reasons are established. The main contributors are a lack of observations per article and uniformity through the time series.