In contrast, the other non neural-net forecasting models in Darts (ARIMA, Exponential Smoothing, FFT, etc) are currently all local models - namely, they are trained on a single time series to forecast the future of this series. This means that these models can be trained on multiple series, and can forecast future values of any time series, even series that are not contained in the training set. Training a Model on Multiple SeriesĪll the deep learning forecasting models implemented in Darts as well as RegressionModel are global forecasting models. Past_covariates=,įuture_covariates=)įuture_covariates=future_covariate_series)įuture_covariates have to be known n time steps in advance at prediction time. TL DR - Summary Create a Global Forecasting Model If you are new to Darts, we recommend to start by reading our earlier short introductory blog post. In this post, we’ll show how Darts can be used to easily train state-of-the-art deep learning forecasting models on multiple and potentially multi-dimensional time series, in only a few lines of code.Ī notebook containing code and explanations related to this article is available here. That is, until Darts came around □ One of the missions of the open-source Darts Python library is to break this barrier of entry, and provide an easy and unified way to work with different kinds of forecasting models. However, one commonly-occurring drawback is that such deep learning models are typically less trivial to work with for data scientists than some of their simpler statistical counter-parts. There are many contexts where this capability can be beneficial: for instance for electricity producers observing the energy demand of many of their customers, or for retailers observing the sales of many potentially-related products. Second, these models can also potentially be trained on multiple related series. First, it allows for building more accurate models that can potentially capture more patterns and also work on multi-dimensional time series. Since a couple of years, deep learning has made its entry into the domain of time series forecasting, and it’s bringing many exciting innovations. Until recently, the most popular time series forecasting techniques were focusing on isolated time series that is, predicting the future of one time series considering the history of this series alone. Time series forecasting - the ability to predict the future evolution of time series- is thus a key capability in many domains where anticipation is important. Any quantity varying over time can be represented as a time series: sales numbers, rainfalls, stock prices, CO2 emissions, Internet clicks, network traffic, etc.
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