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Training Data Size Matters: A Multi-Metric Framework Revealing How Data Availability Affects Forecasting Method Selection

Author

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  • Tharakesvulu Vangalapat

  • Priyank Raj Sharma

  • Somnath Banerjee

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).

Suggested Citation

  • Tharakesvulu Vangalapat & Priyank Raj Sharma & Somnath Banerjee, 2026. "Training Data Size Matters: A Multi-Metric Framework Revealing How Data Availability Affects Forecasting Method Selection," International Journal of Innovative Science and Research Technology (IJISRT), IJISRT Publication, vol. 11(09), pages 76-84, September.
    • Handle: RePEc:cvr:ijisrt:2026:09:ijisrt26sep096
    DOI: https://doi.org/10.38124/ijisrt/26sep096
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