Global deep learning for joint time series forecasting

Machine Learning is a notoriously intricate field practised by academics and industries alike, constantly improving on its benchmarks and spawning interesting ideas and problem-solving approaches. It has been deployed successfully in countless practical applications in many different fields before even a proper theory has been developed explaining why it works. For this reason, it can sometimes be a bit hard to keep up with the latest architectures; in this article, we are exploring the most recent successes in the field of time series forecasting, a class of prediction problems with its own particular status due to the time dimension. More precisely, we'll take into consideration the so-called global models: architectures that are built to detect patterns across many related Time Series at once, learning a single representation which is capable of explaining and forecasting each series individually. A predictive model is called global when it is trained on many different datasets, each being the random outcome of its own stochastic process.