Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Normalization for Bilinear Structures in High-Frequency Financial
  Time-series   Financial time-series analysis and forecasting have been extensively studied
over the past decades, yet still remain as a very challenging research topic.
Since the financial market is inherently noisy and stochastic, a majority of
financial time-series of interests are non-stationary, and often obtained from
different modalities. This property presents great challenges and can
significantly affect the performance of the subsequent analysis/forecasting
steps. Recently, the Temporal Attention augmented Bilinear Layer (TABL) has
shown great performances in tackling financial forecasting problems. In this
paper, by taking into account the nature of bilinear projections in TABL
networks, we propose Bilinear Normalization (BiN), a simple, yet efficient
normalization layer to be incorporated into TABL networks to tackle potential
problems posed by non-stationarity and multimodalities in the input series. Our
experiments using a large scale Limit Order Book (LOB) consisting of more than
4 million order events show that BiN-TABL outperforms TABL networks using other
state-of-the-arts normalization schemes by a large margin.

12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc