Time Series Prediction with Mixture of Experts A ECE539 Project By: Jiong Fan

Introduction Time Series Prediction can be defined as y(t+1) = f(y(0), y(1), …, y(t-L)) Times Series Prediction has a lot of applications There exists a lot of method to perform this perdiciton

Implementation The implementation consists 3 experts and gating network Each of the expert is a MLP implementation Experts are chosen from a pool of predefined configurations The Gating Network is another MLP implementation

“Time-Series Prediction Competition” Testing Methodology Test file is from the class web page “Time-Series Prediction Competition” Data file is then divided into 2 partitions The last 270 is the testing data The rest is training and tuning data AR model is used as base model

Results

AR Model

Experts 1 Result

Expert 2 Result

Expert 3 Result

Mixture of Experts Result

Table of Errors AR Expert 1 Expert 2 Expert 3 Mixture of Experts 0.016115 0.0014062 0.0059747 0.0026146 0.00070705

Conclusion The Mixture of Expert system predicts with more accuracy than any of the model tested