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Forecast Uncertainty and Monte Carlo Simulation

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  • Sam Sugiyama

Abstract

Sam Sugiyama has written a primer on the use of Monte Carlo Simulation to assess forecast error. His simple illustrative example and description of the steps in the MCS procedure provide a non-technical overview of this fascinating approach to the evaluation of uncertainty in forecasts. For regression modelers specifically, Sam shows how MCS can be used to develop more realistic prediction intervals than the theoretical PIs found in books and software. Copyright International Institute of Forecasters, 2007

Suggested Citation

  • Sam Sugiyama, 2007. "Forecast Uncertainty and Monte Carlo Simulation," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 29-37, Spring.
    • Handle: RePEc:for:ijafaa:y:2007:i:6:p:29-37
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    Cited by:

    1. Gu, Bo & Zhang, Tianren & Meng, Hang & Zhang, Jinhua, 2021. "Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation," Renewable Energy, Elsevier, vol. 164(C), pages 687-708.
    2. Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).
    3. Bo Gu & Xi Li & Fengliang Xu & Xiaopeng Yang & Fayi Wang & Pengzhan Wang, 2023. "Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    4. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.

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