25,984 research outputs found
Detecting and Explaining Causes From Text For a Time Series Event
Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.Comment: Accepted at EMNLP 201
Tangled String for Multi-Scale Explanation of Contextual Shifts in Stock Market
The original research question here is given by marketers in general, i.e.,
how to explain the changes in the desired timescale of the market. Tangled
String, a sequence visualization tool based on the metaphor where contexts in a
sequence are compared to tangled pills in a string, is here extended and
diverted to detecting stocks that trigger changes in the market and to
explaining the scenario of contextual shifts in the market. Here, the
sequential data on the stocks of top 10 weekly increase rates in the First
Section of the Tokyo Stock Exchange for 12 years are visualized by Tangled
String. The changing in the prices of stocks is a mixture of various timescales
and can be explained in the time-scale set as desired by using TS. Also, it is
found that the change points found by TS coincided by high precision with the
real changes in each stock price. As TS has been created from the data-driven
innovation platform called Innovators Marketplace on Data Jackets and is
extended to satisfy data users, this paper is as evidence of the contribution
of the market of data to data-driven innovations.Comment: 16 pages and 7 figures. The author started to write this paper as an
extension of the paper [20] in the reference list, but the content came to be
changed substantially, not by only minor extension but to a new pape
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