Time-Series Prediction Apparatus and Time-Series Prediction Method

Patent metadata

Field	Value
Publication number	US-2017300819-A1
Application number	US-201415513749-A
Country	US
Kind code	A1
Filing date	Oct 21, 2014
Priority date	Oct 21, 2014
Publication date	Oct 19, 2017
Grant date	—

Abstract

A time-series prediction apparatus 10 , which is an information processing apparatus that predicts transition of time-series data on a matter, calculates a relevance level which is an index of strength of a causal relation between a plurality of matters including a prediction target matter, based on time-series data relevant to each of the matters and on time-series data relevant to the causal relation between the matters, and predicts transition of the time-series data relevant to the matter based on the calculated relevance level. The time-series prediction apparatus 10 calculates the relevance level based on collocation frequency of terms relevant to the respective matters in the time-series data relevant to the causal relation between the matters. The time-series prediction apparatus 10 builds multiple prediction models for predicting the transition of the time-series data relevant to the prediction target matter based on time-series data relevant to a matter which is in a causal relation with the prediction target matter, and integrates prediction results of the respective prediction models while weighing each of the prediction models according to the relevance level.

First claim

1 . A time-series prediction apparatus that predicts transition of time-series data on a matter, comprising:
a relevance level calculation part that calculates a relevance level which is an index of strength of a causal relation between a plurality of matters including a prediction target matter, based on time-series data relevant to each of the matters and on time-series data relevant to the causal relation between the matters; and a transition prediction part that predicts transition of the time-series data relevant to the matter based on the relevance level.

2 . The time-series prediction apparatus according to claim 1 , wherein
the relevance level calculation part calculates the relevance level based on collocation frequency of terms relevant to the respective matters in the time-series data relevant to the causal relation between the matters.

3 . The time-series prediction apparatus according to claim 1 , wherein
based on time-series data relevant to a matter which is in a causal relation with the prediction target matter, the transition prediction part builds a plurality of prediction models for predicting the transition of the time-series data relevant to the prediction target matter, and the transition prediction part integrates prediction results of the respective prediction models while weighing each of the prediction models according to the relevance level.

4 . The time-series prediction apparatus according to claim 1 , wherein
the time-series prediction apparatus generates a graph representing temporal transition of the time-series data.

5 . The time-series prediction apparatus according to claim 4 , wherein
the time-series prediction apparatus generates a graph representing temporal transition of the relevance level.

6 . The time-series prediction apparatus according to claim 1 , wherein
the time-series prediction apparatus extracts, from time-series data relevant to the causal relation between the matters, time-series data containing both of terms relevant to the respective matters, and generates information indicating appearance frequency of the terms included in the time-series data extracted.

7 . The time-series prediction apparatus according to claim 1 , further comprising a time-series data collection part that acquires, over the Internet, the time-series data relevant to each of the plurality of matters including the prediction target matter and the time-series data relevant to the causal relation between the matters.

8 . A time-series prediction method executed using an information processing apparatus that predicts transition of time-series data on a matter, the method comprising the steps, performed by the information processing apparatus, of:
calculating a relevance level which is an index of strength of a causal relation between a plurality of matters including a prediction target matter, based on time-series data relevant to each of the matters and on time-series data relevant to the causal relation between the matters; and predicting transition of the time-series data relevant to the matter based on the relevance level.

9 . The time-series prediction method according to claim 8 , further comprising the step, performed by the time-series prediction apparatus, of:
calculating the relevance level based on collocation frequency of terms relevant to the respective matters in the time-series data relevant to the causal relation between the matters.

10 . The time-series prediction method according to claim 8 , further comprising the steps, performed by the time-series prediction apparatus, of:
based on time-series data relevant to a matter which is in a causal relation with the prediction target matter, building a plurality of prediction models for predicting the transition of the time-series data relevant to the prediction target matter; and integrating prediction results of the respective prediction models while weighing each of the prediction models according to the relevance level.

11 . The time-series prediction method according to claim 8 , further comprising the step, performed by the time-series prediction apparatus, of:
generating a graph representing temporal transition of the time-series data.

12 . The time-series prediction method according to claim 11 , further comprising the step, performed by the time-series prediction apparatus, of:
generating a graph representing temporal transition of the relevance level.

13 . The time-series prediction method according to claim 8 , further comprising the step, performed by the time-series prediction apparatus, of:
extracting, from time-series data relevant to the causal relation between the matters, time-series data containing both of terms relevant to the respective matters, and generating information indicating a frequency of appearance of the terms included in the time-series data extracted.

14 . The time-series prediction method according to claim 8 , further comprising the step, performed by the time-series prediction apparatus, of:
acquiring, over the Internet, the time-series data relevant to each of the plurality of matters including the prediction target matter and the time-series data relevant to the causal relation between the matters.

Assignees

• Hitachi Ltd

Inventors

• Hayashi Yu
• Tomita Naofumi
• Ishiguro Masao
• Hiroi Kazushige

Classifications

• G06N5/04 Primary

  Inference or reasoning models · CPC title

• G06F17/18 Primary

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06N20/00

  Machine learning · CPC title