Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

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

3,160 views

Published on

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

Published in: Technology, Business
  • Be the first to comment

  • Be the first to like this

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

×