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Multi-Model Fusion for Robust Time-Series Forecasting

Multi-Model Fusion for Robust Time-Series Forecasting. Weizhong Yan. Industrial Artificial Intelligence Lab GE Global Research Center Niskayuna, NY 12309. Outline. Problem Description Datasets Challenges and modeling strategies Our Approach The Results Final Remarks.

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Multi-Model Fusion for Robust Time-Series Forecasting

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  1. Multi-Model Fusion for Robust Time-Series Forecasting Weizhong Yan Industrial Artificial Intelligence Lab GE Global Research Center Niskayuna, NY 12309 W. Yan

  2. Outline • Problem Description • Datasets • Challenges and modeling strategies • Our Approach • The Results • Final Remarks W. Yan

  3. Dataset characteristics Time series with seasonality, trend, and outlier Non-stationary W. Yan

  4. Challenges and modeling strategies A large number of time series with different features. Manual, ad-hoc modeling strategies are not working A model-building strategy that can automatically identify features (i.e., trend, seasonality, etc) of time series and arrives in a forecast model with robust & accurate performance for a large number of time series W. Yan

  5. Our Approach(1) - Preprocessing automatically Feature identification Feature treatment Outliers Trend W. Yan

  6. Our Approach(2) - Modeling Generalized Regression NN W. Yan

  7. Our Approach(3) - Why GRNN? It’s a variation of “nearest neighbor” approach Forecast for an input is a weighted average of the outputs in the training examples. The closer an input to the training example, the larger the weight of its corresponding output. • Advantages • It’s a universal approximator • It’s fast in training (one-pass learning) • It’s good for sparse data • Disadvantages • It requires large amount of online computation • It almost does not have any extrapolation capability (forecast is bounded by min & max of the observations) W. Yan

  8. Results(1) W. Yan

  9. Results(2) W. Yan

  10. Results(3) W. Yan

  11. Results(4) W. Yan

  12. Results(5) W. Yan

  13. Results(6) W. Yan

  14. Final remarks • Developing a robust time series forecasting model is a challenging task. • Developing an automatic model building process that can be reliably applied to a large number of time series with varying features is even more challenging. • When the number of historical data points is small, fusion of multiple simple models seems to work better than a single complex model does Future work • Using more GRNNs • Optimally determining the tunable parameter, spread, for GRNNs • … W. Yan

  15. Thank you W. Yan

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