Authors : Tharakesvulu Vangalapat; Priyank Raj Sharma; Somnath Banerjee

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/3w48fxsc

DOI : https://doi.org/10.38124/ijisrt/26sep096

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Background: Practitioners receive contradictory guidance on forecasting method selection because existing comparisons use fixed train-test splits without examining how training data size affects rankings.  Methods: We evaluate 11 methods across 11 datasets (2,515–48,204 observations) using two protocols (60/20/20 and 70/15/15 splits) with five metrics (MAPE, SMAPE, RMSE, MAE, MASE).  Results: With 60% training data, Exponential Smoothing ranks best (4.80). With 70% training data, Prophet dominates (4.47), followed by LightGBM (4.75) and ETS (5.02). ETS improves 1.75 positions and LightGBM improves 1.56 positions, whereas Exponential Smoothing declines 1.24 positions. ElasticNet ranks last under both protocols (7.69 and 8.31).

Keywords : Time Series Forecasting, Method Selection, Training Data Requirements, Empirical Evaluation, Comparative Study.

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