Abstract:
Associating the pattern in text data with the pattern with
time series data is a novel task. In this paper, an approach that utilizes
the features of the time series data and domain knowledge is proposed
and used to identify the patterns for exchange rate modeling. A set of
rules to identify the patterns are firstly specified using domain knowledge.
The text data are then associated with the exchange rate data and preclassified according to
the trend of the time series. The rules are further
refined by the characteristics of the pre-classified data. Classification
solely based on time series data requires precise and timely data, which
are difficult to obtain from financial market reports. On the other hand,
domain knowledge is often very expensive to be acquired and often has
a modest inter-rater reliability. The proposed method combines both
methods, leading to a "grey box" approach that can handle the data
with some time delay and overcome these drawbacks.
