Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent
  Units   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.

31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg