Data Science Practice

1. DEEP LEARNING
for signal processing (or time series)
Oleksandr Honchar
Mawi Solutions

2. • AI solutions architect @ Mawi
Solutions
• Dottore magistrale in
mathematics candidate @ UNIVR
• Calling myself AI expert in
Linkedin

8. SIGNALS IN THE WILD 🦁

9. SIGNALS IN THE WILD 🦁

10. SIGNALS IN THE WILD 🦁

11. MAWI BAND ♥️

12. CLASSICAL APPROACHES 🔬
• Classification
- time domain analysis

13. CLASSICAL APPROACHES 🔬
• Classification
- time domain analysis
- frequency domain analysis

14. CLASSICAL APPROACHES 🔬
• Classification
- time domain analysis
- frequency domain analysis
- nearest neighbors

15. CLASSICAL APPROACHES 🔬
• Classification
- time domain analysis
- frequency domain analysis
- nearest neighbors
- ML

16. CLASSICAL APPROACHES 🔬
• Classification
- time domain analysis
- frequency domain analysis
- nearest neighbors
- ML
• Regression
- ARIMA models

17. CLASSICAL APPROACHES 🔬
• Classification
- time domain analysis
- frequency domain analysis
- nearest neighbors
- ML
• Regression
- ARMA models
- smoothing / decomposition

18. CLASSICAL APPROACHES 🔬
• Classification
- time domain analysis
- frequency domain analysis
- nearest neighbors
- ML
• Regression
- ARMA models
- smoothing / decomposition
- nonlinear dynamics

19. CLASSICAL APPROACHES 🔬
• Classification
- time domain analysis
- frequency domain analysis
- nearest neighbors
- ML
• Regression
- ARMA models
- smoothing / decomposition
- nonlinear dynamics
- ML

20. DEEP LEARNING 🦁
TCE conference, 2014

21. DEEP LEARNING 🦁
• RNN

22. DEEP LEARNING 🦁
• RNN

23. DEEP LEARNING 🦁
• RNN

24. DEEP LEARNING 🦁
• RNN
1.Theoretical infinite memory
2.Multistep prediction ability
3.Truncated implementation
4.Cant train in parallel
5.Difficult to optimize
6.Slow in inference
7.Doubtful superior performance!!!

25. DEEP LEARNING 🦁
• RNN
• CNN

26. DEEP LEARNING 🦁
• RNN
• CNN

27. DEEP LEARNING 🦁
• RNN
• CNN
• RNN + CNN

28. DEEP LEARNING 🦁
• RNN
• CNN
• RNN + CNN
• Autoregressive CNN

29. DEEP LEARNING 🦁
• RNN
• CNN
• RNN + CNN
• Transformer

30. MAWI BAND ♥️

31. Other successes 🦁
Sales forecasting Wikipedia traffic

32. DEEP LEARNING 🦁
• RNN
• CNN
• RNN + CNN
• Autoregressive CNN
• Other tasks

33. DEEP LEARNING 🦁
• RNN
• CNN
• RNN + CNN
• Autoregressive CNN
• Other tasks

34. HYBRID SOLUTIONS 🐙

35. HYBRID SOLUTIONS 🐙

36. TAKEAWAYS 📚
• Signals are everywhere
• Autoregressive CNN > CNN > RNN
• Cluster in embedding space
• Use GANs not just to generate
• Combine DL and classics if you can
• It works for NLP, speech and other sequences as well!

37. TCE conference, 2014

38. Home reading
1. When Recurrent Models Don't Need To Be Recurrent
2. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
3. DEEP TEMPORAL CLUSTERING: FULLY UNSUPERVISED LEARNING OF TIME-DOMAIN FEATURES
4. REAL-VALUED (MEDICAL) TIME SERIES GENERATION WITH RECURRENT CONDITIONAL GANS
5. Time-series Extreme Event Forecasting with Neural Networks at Uber
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