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While many models (e.g., DeepAR, MQRNN) have focused on variants of recurrent neural networks (RNNs), recent improvements, including Transformer-based models, have used attention-based layers to enhance the selection of relevant time steps in the past beyond the inductive bias of RNNs – sequential ordered processing of information including. For example, retailers can use future sales to optimize their supply chain and promotions, investment managers are interested in forecasting the future prices of financial assets to maximize their performance, and healthcare institutions can use the number of future patient admissions to have sufficient personnel and equipment.ĭeep neural networks (DNNs) have increasingly been used in multi-horizon forecasting, demonstrating strong performance improvements over traditional time series models. Most real-world datasets have a time component, and forecasting the future can unlock great value. predicting variables-of-interest at multiple future time steps, is a crucial challenge in time series machine learning. Arik, Research Scientist and Tomas Pfister, Engineering Manager, Google Cloud