Paper by Parras, Rivas and Merelo for the ECTA-IJCCI conference.

1. The L-Co-R co-evolutionary
algorithm: a comparative
analysis in
medium-term time-series
forecasting problems
Parras-Gutiérrez, Rivas and Merelo
U. Jaén & Granada (Spain)
http://geneura.wordpress.com

2. Parras et al. #lcor 2
It's difficult to
make
predictions,
especially about
the future
Yogi Berra

3. Parras et al. #lcor 3
Smells like a bubble

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Using
coevolution
to predict
bubble-
bursting

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Radial Basis Function neural nets and time lags
Coevolving!

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What are RBFNNs?

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What do we mean by time lags?

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Trend pre-processing
Trend post-processing
Initializate lags
Initializate RBFNN
Evaluate lags
Evolve Lags: CHC
Evaluate RBFNN
Evaluate Lags
Evolve RBFNN: EA
Evaluate RBFNN
RBFNNs
Lags
Main loop
Lags' loop
RBFNs' loop
Final forecasting

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Let's fight
●
Data sets taken from Spanish National Statistics Institute+ Time
Series book by D. Peña + NN3 competition
– Check 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

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L-Co-R predicting airline passengers

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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 reliable
– Less sensitive to outliers.
– Less variable on small samples.
– More easily interpreted.

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Who's the best?

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That's all
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