Time Series and Deep Learning

A Considerable look at Deep learning and time series problems.   We did many of these kinds of problems in the enterprise.  And we did not consider DL because it seemed inefficient.   In O'Reilly.

3 reasons to add deep learning to your time series toolkit
The most promising area in the application of deep learning methods to time series forecasting is in the use of CNNs, LSTMs, and hybrid models.

By Francesca Lazzeri:

The ability to accurately forecast a sequence into the future is critical in many industries: finance, supply chain, and manufacturing are just a few examples. Classical time series techniques have served this task for decades, but now deep learning methods—similar to those used in computer vision and automatic translation—have the potential to revolutionize time series forecasting as well.

Due to their applicability to many real-life problems—such as fraud detection, spam email filtering, finance, and medical diagnosis—and their ability to produce actionable results, deep learning neural networks have gained a lot of attention in recent years. Generally, deep learning methods have been developed and applied to univariate time series forecasting scenarios, where the time series consists of single observations recorded sequentially over equal time increments. For this reason, they have often performed worse than naïve and classical forecasting methods, such as exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA). This has led to a general misconception that deep learning models are inefficient in time series forecasting scenarios, and many data scientists wonder whether it’s really necessary to add another class of methods—such as convolutional neural networks or recurrent neural networks—to their time series toolkit.

In this post, I'll discuss some of the practical reasons why data scientists may still want to think about deep learning when they build time series forecasting solutions. ... "