Presentation Template Design-7

The L-Co-R co-evolutionary algorithm: a comparative analysis in
medium-term time-series forecasting problems

Parras-Gutirrez, Rivas and Merelo

U. Jan & Granada (Spain)

http://geneura.wordpress.com

It's difficult to make predictions, especially about the future

Yogi Berra

Cropped from Image by Pensiero at http://www.flickr.com/photos/pensiero/2878055175/

Smells like a bubble

Using coevolution to predict bubble-bursting

Image by Light Knight at http://www.flickr.com/photos/lightknight/3176554248

Radial Basis Function neural nets and time lags

Coevolving!

Image by JWPotowerks at http://www.flickr.com/photos/john_whitworth_photography/3017645549 (spokes) and Eduardo Zrate at http://www.flickr.com/photos/eduardozarate/3513912756/

What are RBFNNs?

What do we mean by time lags?

Horizon is what lies between the predicted value and the first previous datum used to predict it. A consistent value of horizon was used throughout the experiments.

Trend pre-processingTrend post-processingInitializate lagsInitializate RBFNNEvaluate lagsEvolve Lags: CHCEvaluate RBFNNEvaluate LagsEvolve RBFNN: EAEvaluate RBFNNRBFNNsLagsMain loop

Lags' loop

RBFNs' loop

Final forecasting

CHC combines conservative selection strategy with disruptive recombination HUX: http://neo.lcc.uma.es/mallba/easy-mallba/html/algorithms.html#chcCHC is called also Adaptive Search AlgorithmEvRBF is an already published evolutionary algorithmTrend is removed and then added to avoid it to dominate the prediction.

Let's fight

Data sets taken from Spanish National Statistics Institute+ Time Series book by D. Pea + NN3 competitionCheck them out at https://sites.google.com/site/presetemp/datos

Airline passengers, mortgages, prices...

Comparison with other five methods:Exponential Smoothing Method.

Croston

Theta

Random Walk

ARIMA

75% for training - 25% for testing30 executions with average published.

L-Co-R predicting airline passengers

How do we measure success?

Several measures used:Mean absolute percentage error : MAPE.

Mean absolute scaled error: MASE.

Median absolute percentage error: MdAPE.

MASE is probably the most reliableLess sensitive to outliers.

Less variable on small samples.

More easily interpreted.

Imagen by tudedude at http://www.flickr.com/photos/tudedude/3516187441

Who's the best?

Differences are significant anyways

That's all

Any questions?

Check us out at @geneura@canubeproject@anyselfproject@sipesca

ANYSELF

AnyselfProject