TIME-SERIES DATA ANALYZING APPARATUS, TIME-SERIES DATA ANALYZING METHOD, AND COMPUTER PROGRAM PRODUCT

• US 20090292662A1
• Filed: 05/22/2009
• Published: 11/26/2009
• Est. Priority Date: 05/26/2008
• Status: Abandoned Application

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Ueno, Ken, Orihara, Ryohei
• Application Number:
  US12/470,950
• Publication Number:
  US 20090292662A1
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06F 17/18 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 Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

Sets of integrated data including history data and time-invariant data grouped for each analysis target are classified based on an inclusion between an amount of change of a time-varying item included in sets of integrated data and a numerical range expressed by an event sequence and also based on a common time-invariant item, to generate a prediction model in which a prediction-target event sequence expressing an amount of change of the event item included in each set of integrated data after being classified and an amount of time required for reaching the amount of change is associated with the event sequence together with a classification condition related to the classification.

Claims

1. A time-series data analyzing apparatus comprising:

a first storage unit that stores integrated data obtained by associating time-series data and time-invariant data with respect to a common analysis target for each of a plurality of analysis targets, the time-series data recording an event item quantitatively indicating a predetermined event occurred with a lapse of time, a time-varying item indicating a numerical value of an element related to occurrence of a corresponding event, and date and time of occurrence of the event, and the time-invariant data including one or a plurality of time-invariant items indicating a time-invariant setting content relating to the analysis target;

a first generating unit that expands a numerical range of the time-varying item included in a specific set of integrated data to be analyzed, among sets of grouped integrated data for each of the analysis targets, and generates an event sequence expressing the numerical range including an amount of change of the time-varying item included in the set of grouped integrated data for each of other analysis targets;

a second generating unit that classifies respective sets of the grouped integrated data based on an inclusion between the amount of change of the time-varying item included in the sets of grouped integrated data and the numerical range expressed by the event sequence and also based on the time-invariant item common to respective sets, and generates a prediction model obtained by associating a prediction-target event sequence with the event sequence together with a classification condition related to the classification, the prediction-target event sequence expressing an amount of change of the event item included in each set of integrated data after being classified and an amount of time required for reaching the amount of change of the event item; and

a second storage unit that stores the prediction model.

2. The apparatus according to claim 1, wherein the first generating unit selects a plurality of the integrated data of a corresponding analysis target for each of common analysis targets from the integrated data stored in the first storage unit, and rearranges the integrated data in order of date and time of occurrence to group the integrated data.

3. The apparatus according to claim 1, wherein the first generating unit gradually expands the numerical range until number of grouped analysis targets satisfying a condition of the numerical range becomes equal to or larger than a predetermined number.

4. The apparatus according to claim 1, wherein the second generating unit classifies the prediction-target event sequences by using a decision tree classification model in which the event sequence is set as a route.

5. The apparatus according to claim 4, wherein the second generating unit repeats to classify the prediction-target event sequences until number of prediction-target event sequences, which are leaf nodes, becomes a predetermined number.

6. The apparatus according to claim 1, wherein the second generating unit calculates a difference in date and time of occurrence between the event items included in the sets of grouped integrated data as an amount of time required for each set of the classified integrated data, and designates a statistic of the amount of time required in each set of the integrated data as the amount of time required.

7. The apparatus according to claim 1, wherein

the time-series data includes the time-varying item of a plurality of elements different from each other,

the first generating unit generates the event sequence for each element of the time-varying item, and

the second generating unit generates the prediction model for each of the event sequence.

8. The apparatus according to claim 1, further comprising a display unit that displays a prediction model stored in the second storage unit.

9. The apparatus according to claim 1, further comprising a predicting unit that compares the time-series data and the time-invariant data of the prediction target with the classification condition in the prediction model, and derives an amount of change and an amount of time required of the event item expressed by the prediction-target event sequence, which is reached finally, as a prediction result, wherein the display unit displays a derived prediction result.

10. The apparatus according to claim 1, further comprising:

a third storage unit that stores the time-series data;

a fourth storage unit that stores the time-invariant data; and

an integrating unit that integrates the time-series data stored in the third storage unit and the time-invariant data stored in the fourth storage unit with respect to a common analysis target included in the time-series data and the time-invariant data, wherein

the first storage unit stores data integrated by the integrating unit.

11. A time-series data analyzing method comprising:

storing integrated data obtained by associating time-series data and time-invariant data with respect to a common analysis target for each of a plurality of analysis targets, the time-series data recording an event item quantitatively indicating a predetermined event occurred with a lapse of time, a time-varying item indicating a numerical value of an element related to occurrence of a corresponding event, and date and time of occurrence of the event, and the time-invariant data including one or a plurality of time-invariant items indicating a time-invariant setting content relating to the analysis target;

expanding a numerical range of the time-varying item included in a specific set of integrated data to be analyzed, among sets of grouped the integrated data grouped for each of the analysis targets, and generating an event sequence expressing the numerical range including an amount of change of the time-varying item included in the sets of grouped integrated data for each of other analysis targets; and

classifying respective sets of the grouped integrated data based on an inclusion between the amount of change of the time-varying item included in the sets of grouped integrated data and the numerical range expressed by the event sequence and also based on the time-invariant item common to respective sets, and generating a prediction model obtained by associating a prediction-target event sequence with the event sequence together with a classification condition related to the classification, the prediction-target event sequence expressing an amount of change of the event item included in each set of grouped integrated data after being classified and an amount of time required for reaching the amount of change of the event item.

12. A computer program product having a computer readable medium including programmed instructions for analyzing time-series data, wherein the instructions, when executed by a computer, cause the computer to perform:

storing integrated data obtained by associating time-series data and time-invariant data with respect to a common analysis target for each of a plurality of analysis targets, the time-series data recording an event item quantitatively indicating a predetermined event occurred with a lapse of time, a time-varying item indicating a numerical value of an element related to occurrence of a corresponding event, and date and time of occurrence of the event, and the time-invariant data including one or a plurality of time-invariant items indicating a time-invariant setting content relating to the analysis target;

expanding a numerical range of the time-varying item included in a specific set of integrated data to be analyzed, among sets of grouped integrated data for each of the analysis targets, and generating an event sequence expressing the numerical range including an amount of change of the time-varying item included in the sets of grouped integrated data for each of other analysis targets; and

classifying respective sets of the grouped integrated data based on an inclusion between the amount of change of the time-varying item included in the sets of grouped integrated data and the numerical range expressed by the event sequence and also based on the time-invariant item common to respective sets, and generating a prediction model obtained by associating a prediction-target event sequence with the event sequence together with a classification condition related to the classification, the prediction-target event sequence expressing an amount of change of the event item included in each set of grouped integrated data after being classified and an amount of time required for reaching the amount of change of the event item.