Deep Learning for Time Series and Signal Processing

We researched the topic of using deep learning to replace forecasting time series applications in the enterprise,  for faster and more accurate results, but were never successful.    The need to segment many small  time steps and solve the resulting large neural nets was too difficult.   Here a useful piece on the current related approaches with machine learning.   Reading and will comment further.  From DSC.   Join the group

Deep learning: the final frontier for signal processing and time series analysis?
Posted by Andrea Manero-Bastin  
This article was written by Alexandr Honchar.

People use deep learning almost for everything today, and the “sexiest” areas of applications are computer vision, natural language processing, speech and audio analysis, recommender systems and predictive analytics. But there is also one field that is unfairly forgotten in terms of machine learning — signal processing (and, of course, time series analysis). In this article, I want to show several areas where signals or time series are vital, after I will briefly review classical approaches and will move on to my experience with applying deep learning for biosignal analysis in Mawi Solutions and for algorithmic trading. I already gave a couple of talks on this topic in Barcelona and Lviv, but I would like to make the materials a bit more accessible.

I am sure, that not only people working with time series data will benefit from this article. Computer vision specialists will learn how similar their domain expertise is to signal processing, NLP people will get some insights about sequential modeling and other professionals can have interesting takeaways as well. Enjoy!  ... '