STATISTICS Seminars Series 2026-11-04: Gilbert Mutungi

Speaker: Gilbert Mutungi, Makerere University, Uganda

Topic: Time Series Features and Forecasting

Abstract: Selecting an appropriate forecasting approach remains a central challenge, as no single method consistently performs best across different time series. Conventional model selection and forecast combination approaches face challenges related to accuracy, computational demands, and generalizability. Using the M4 forecasting competition dataset, we explore how statistical features relate to forecasting model performance and how these relationships can inform model selection and combination. We further investigate whether knowledge learned from M4 can be transferred to improve the forecasting of Uganda’s macroeconomic indicators. Results demonstrate the potential of archive-based forecasting knowledge transfer, with the proposed probabilistically weighted combination achieving the best overall performance among the selection and combination strategies considered.