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### Is Deep learning The Final Frontier For Signal Processing and Time Series Analysis?

- Time domain analysis: this is all about "looking" how time series evolves over time. It can include analysis of width, heights of the time steps, statistical features and other "visual" characteristics.
- Frequency domain analysis: a lot of signals are better represented not by how the change over time, but what amplitudes they have in it and how they change. Fourier analysis and wavelets are what you go with.
- Nearest neighbors analysis: sometimes we just need to compare two signals or measure a distance between them and we can't do this with regular metrics like Euclidean, because signals can be of different length and the notion of similarity is a bit different as well. A great example of metrics for time series with dynamic time warping.
- (S)AR(I)MA(X) models: the very popular family of mathematical models based on linear self-dependence inside of time series (autocorrelation) that is able to explain future fluctuations.
- Decomposition: another important approach for prediction is decomposing time series into logical parts that can be summed or multiplied to obtain the initial time series: trend part, seasonal part, and residuals.
- Nonlinear dynamics: we always forget about differential equations (ordinary, partial, stochastic and others) as a tool for modeling dynamical systems that are in fact signals or time series. It's rather unconventional today, but features from DEs can be very useful forÃ¢?Â¦
- Machine learning: all things from above can get features for any machine learning model we have. But in 2018 we don't want to rely on human-biased mathematical models and feature. We want it to be done for us with AI, which today is deep learning.

- Signals are everywhere: from outer space to our bodies
- Autoregressive CNN > CNN > RNN for sequential modeling
- Do clustering in embedding space instead of DTW+K-Means
- Use GANs not just to generate
- Combine DL and mathematical modeling if you can
- It works for NLP, speech and other sequences as well!

- When Recurrent Models Don't Need To Be Recurrent
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- Deep Temporal Clustering: Fully Unsupervised Learning of Time-Domain Features
- Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
- Time-series Extreme Event Forecasting with Neural Networks at Uber