I am pleased to announce the open-source Python package PyTorch Forecasting. It makes time series forecasting with neural networks simple both for data science practitioners and researchers.

Why is accurate forecasting so important?

Forecasting time series is important in many contexts and highly relevant to machine learning practitioners. Take, for example, demand forecasting from which many use cases derive. Almost every manufacturer would benefit from better understanding demand for their products in order to optimise produced quantities. Underproduce and you will lose revenues, overproduce and you will be forced to sell excess produce at a discount. Very related is pricing, which is essentially a demand forecast with a specific focus on price elasticity. Pricing is relevant to virtually all companies.

For a large number of additional machine learning applications time is of the essence: predictive maintenance, risk scoring, fraud detection, etc. — you name it. The order of events and time between them is crucial to create a reliable forecast.

In fact, while time series forecasting might not be as shiny as image recognition or language processing, it is more common in industry. This is because image recognition and language processing are relatively new to the field and are often used to power new products, while forecasting has been around for decades and sits at the heart of many decision (support) systems. The employment of high-accuracy machine learning models such as the ones in PyTorch Forecasting can better support decision making or even automate it, often directly resulting in multi-million dollars of additional profits.

#deep-learning #machine-learning #time-series-forecasting #python #pytorch