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One of the factors that often result in an unforeseen shortage or expiry of medication is the absence of, or continued use of ineffective, inventory forecasting mechanisms. Unforeseen shortage of perhaps lifesaving medication potentially translates to a loss of lives, while overstocking can affect both medical budgeting as well as healthcare provision. Evidence from literature indicates that forecasting techniques can be a robust approach to address this inventory management challenge. The purpose of this study is to propose an inventory forecasting solution based on time series data mining techniques applied to transactional data of medical consumptions. Four different machine learning algorithms for time series analysis were explored and their forecasting accuracy estimates were compared. Results reveal that Gaussian Processes (GP) produced better results compared to other explored techniques …