Deep Factors for Forecasting

Understand

Producing probabilistic forecasts for large collections of similar and/or dependent time series is a practically relevant and challenging task.

• Classical time series models fail to capture complex patterns in the data, and multivariate techniques struggle to scale to large problem sizes.
• Their reliance on strong structural assumptions makes them data-efficient, and allows them to provide uncertainty estimates.
• The converse is true for models based on deep neural networks, which can learn complex patterns and dependencies given enough data.