Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: IDEA: Interpretable Dynamic Ensemble Architecture for Time Series
  Prediction   We enhance the accuracy and generalization of univariate time series point
prediction by an explainable ensemble on the fly. We propose an Interpretable
Dynamic Ensemble Architecture (IDEA), in which interpretable base learners give
predictions independently with sparse communication as a group. The model is
composed of several sequentially stacked groups connected by group backcast
residuals and recurrent input competition. Ensemble driven by end-to-end
training both horizontally and vertically brings state-of-the-art (SOTA)
performances. Forecast accuracy improves by 2.6% over the best statistical
benchmark on the TOURISM dataset and 2% over the best deep learning benchmark
on the M4 dataset. The architecture enjoys several advantages, being applicable
to time series from various domains, explainable to users with specialized
modular structure and robust to changes in task distribution.

24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math