The document describes a study comparing the L-Co-R co-evolutionary algorithm to other forecasting methods for medium-term time-series problems. The L-Co-R algorithm uses coevolving radial basis function neural networks (RBFNNs) and time lags to make predictions. The study uses several Spanish economic and transportation datasets and evaluates the accuracy of L-Co-R versus exponential smoothing, Croston's method, Theta, random walk, and ARIMA models based on mean absolute percentage error, mean absolute scaled error, and median absolute percentage error. Preliminary results show that L-Co-R performs well in predicting airline passenger numbers compared to other methods.

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

4. Parras et al. #lcor 4
Using
coevolution
to predict
bubble-
bursting

5. Parras et al. #lcor 5
Radial Basis Function neural nets and time lags
Coevolving!

6. Parras et al. #lcor 6
What are RBFNNs?

7. Parras et al. #lcor 7
What do we mean by time lags?

8. Parras et al. #lcor 8
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

9. Parras et al. #lcor 9
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

10. Parras et al. #lcor 10
L-Co-R predicting airline passengers

11. Parras et al. #lcor 11
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.

12. Parras et al. #lcor 12
Who's the best?

13. Parras et al. #lcor 13
That's all
Any questions?Any questions?
Check us out atCheck us out at
@geneura@geneura
@canubeproject@canubeproject
@anyselfproject@anyselfproject
@sipesca@sipesca
ANYSELF
AnyselfProject