This is the second article on time-series with Deep Learning and DeepDetect. It shows how to use a type of deep neural network architecture named NBEATS dedicated to time-series. In our earlier post on time-series with recurrent networks and DeepDetect, we did use LSTMs with DeepDetect, and here we thus focus on more appropriate architectures.
This blog post shows how to obtain much more configurable and accurate time-series forecasting models than with other methods.
This article is the first of an ongoing serie on forecasting time series with Deep Learning and DeepDetect.
DeepDetect allows for quick and very powerful modeling of time series for a variety of applications, including forecasting and anomaly detection.
This serie of posts describes reproducible results with powerful deep network advances such as LSTMs, NBEATS and Transformer architectures.
The DeepDetect Open Source Server & DeepDetect Platform do come with the following features with application to time series: