Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extracting Predictive Information from Heterogeneous Data Streams using
  Gaussian Processes   Financial markets are notoriously complex environments, presenting vast
amounts of noisy, yet potentially informative data. We consider the problem of
forecasting financial time series from a wide range of information sources
using online Gaussian Processes with Automatic Relevance Determination (ARD)
kernels. We measure the performance gain, quantified in terms of Normalised
Root Mean Square Error (NRMSE), Median Absolute Deviation (MAD) and Pearson
correlation, from fusing each of four separate data domains: time series
technicals, sentiment analysis, options market data and broker recommendations.
We show evidence that ARD kernels produce meaningful feature rankings that help
retain salient inputs and reduce input dimensionality, providing a framework
for sifting through financial complexity. We measure the performance gain from
fusing each domain's heterogeneous data streams into a single probabilistic
model. In particular our findings highlight the critical value of options data
in mapping out the curvature of price space and inspire an intuitive, novel
direction for research in financial prediction.

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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: cs
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: math
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: chem-ph
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The categories of this paper are: physics
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The categories of this paper are: eess