Artificial Intelligence Applications in Engineering (MUH-920), week 12 of 14: interactive lab
Prof. Dr. Utku Kose, Süleyman Demirel University
Part A generates a monthly series with adjustable trend, seasonal amplitude, noise and an optional level shift, and compares naive, seasonal naive, moving average, trend-and-season regression and Holt-Winters exponential smoothing on a held-out final period [1]. Part B repeats the comparison over rolling origins and shows how the error grows with the horizon.
Twelve origins, each followed by a 12-month horizon. The chart shows the mean absolute error for every horizon step and method.
Six questions with instant feedback. Rate your confidence before checking each answer.
Answers are saved in this browser only. The export creates a Markdown learning log for your portfolio.
After the export, continue with the discipline challenge and the weekly task in the week overview.
[1] Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts. https://otexts.com/fpp3/
[2] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735