G 08-17-2007 W. Yan 1 Multi-Model Fusion for Robust Time-Series Forecasting Weizhong Yan Industrial Artificial Intelligence Lab GE Global Research Center.

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Presentation transcript:

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

g W. Yan 2 Outline 1.Problem Description Datasets Challenges and modeling strategies 2.Our Approach 3.The Results 4.Final Remarks

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

g W. Yan 4 Challenges and modeling strategies 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 A large number of time series with different features. Manual, ad-hoc modeling strategies are not working

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

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

g W. Yan 7 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 1.It’s a universal approximator 2.It’s fast in training (one-pass learning) 3.It’s good for sparse data Disadvantages 1.It requires large amount of online computation 2.It almost does not have any extrapolation capability (forecast is bounded by min & max of the observations) Our Approach(3) - Why GRNN?

g W. Yan 8 Results(1)

g W. Yan 9 Results(2)

g W. Yan 10 Results(3)

g W. Yan 11 Results(4)

g W. Yan 12 Results(5)

g W. Yan 13 Results(6)

g 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  …

g W. Yan 15 Thank you