The L-Co-R co-evolutionary
algorithm: a comparative
analysis in
medium-term time-series
forecasting problems
Parras-Gutiér...
Parras et al. #lcor 2
It's difficult to
make
predictions,
especially about
the future
Yogi Berra
Parras et al. #lcor 3
Smells like a bubble
Parras et al. #lcor 4
Using
coevolution
to predict
bubble-
bursting
Parras et al. #lcor 5
Radial Basis Function neural nets and time lags
Coevolving!
Parras et al. #lcor 6
What are RBFNNs?
Parras et al. #lcor 7
What do we mean by time lags?
Parras et al. #lcor 8
Trend pre-processing
Trend post-processing
Initializate lags
Initializate RBFNN
Evaluate lags
Evolve...
Parras et al. #lcor 9
Let's fight
●
Data sets taken from Spanish National Statistics Institute+ Time
Series book by D. Peñ...
Parras et al. #lcor 10
L-Co-R predicting airline passengers
Parras et al. #lcor 11
How do we measure success?
●
Several measures used:
– Mean absolute percentage error : MAPE.
– Mean...
Parras et al. #lcor 12
Who's the best?
Parras et al. #lcor 13
That's all
Any questions?Any questions?
Check us out atCheck us out at
@geneura@geneura
@canubeproj...
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The L-Co-R co-evolutionary algorithm: a comparative analysis in medium-term time-series forecasting problems

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

Publicado en: Tecnología, Empresariales
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The L-Co-R co-evolutionary algorithm: a comparative analysis in medium-term time-series forecasting problems

  1. 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. 2. Parras et al. #lcor 2 It's difficult to make predictions, especially about the future Yogi Berra
  3. 3. Parras et al. #lcor 3 Smells like a bubble
  4. 4. Parras et al. #lcor 4 Using coevolution to predict bubble- bursting
  5. 5. Parras et al. #lcor 5 Radial Basis Function neural nets and time lags Coevolving!
  6. 6. Parras et al. #lcor 6 What are RBFNNs?
  7. 7. Parras et al. #lcor 7 What do we mean by time lags?
  8. 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. 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. 10. Parras et al. #lcor 10 L-Co-R predicting airline passengers
  11. 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. 12. Parras et al. #lcor 12 Who's the best?
  13. 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

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