# Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units

## TL;DR

This paper introduces ARU, an adaptive recurrent unit that enables efficient streaming adaptation of deep forecasting models by combining global learning with local linear updates, improving responsiveness and reducing memory usage.

## Contribution

The paper proposes ARU, a novel adaptive recurrent unit that integrates local linear models with deep global models for streaming time-series forecasting, trained end-to-end with simple update rules.

## Key findings

- ARU outperforms recent local adaptation methods on multiple datasets.
- ARU requires only fixed-sized state and simple updates, making it memory-efficient.
- ARU effectively combines global deep models with local linear adaptation.

## Abstract

We present ARU, an Adaptive Recurrent Unit for streaming adaptation of deep globally trained time-series forecasting models. The ARU combines the advantages of learning complex data transformations across multiple time series from deep global models, with per-series localization offered by closed-form linear models. Unlike existing methods of adaptation that are either memory-intensive or non-responsive after training, ARUs require only fixed sized state and adapt to streaming data via an easy RNN-like update operation. The core principle driving ARU is simple --- maintain sufficient statistics of conditional Gaussian distributions and use them to compute local parameters in closed form. Our contribution is in embedding such local linear models in globally trained deep models while allowing end-to-end training on the one hand, and easy RNN-like updates on the other. Across several datasets we show that ARU is more effective than recently proposed local adaptation methods that tax the global network to compute local parameters.