APPARATUS AND METHOD FOR CLASSIFYING TIME-SERIES DATA AND TIME-SERIES DATA PROCESSING APPARATUS
First Claim
1. A time-series data classifying apparatus, comprising:
- a first database configured to store a plurality of cases each includingtime-series data in which an observed value obtained by observing an observation object is sequentially recorded in associated with an observed time anda classification label that represents a state or type of the observation object as when the observation object is observed;
a peak feature extracting unit configured to, for each of the cases,expand the time-series data in a coordinate system which is made up of a time axis and a value axis representing the observed value,set along the time axis a reference line that intersects expanded time-series data,detect intersection points of the expanded time-series data and the reference line, anddetect a peak point of the expanded time-series data in each of sections each formed between two intersection points being adjacent to generate a peak feature sequence that contains the peak point detected in each of the sections;
a second database configured to store the peak feature sequence generated for each of the cases in association with a classification label of each of the cases;
a data input unit configured to input target time-series data; and
a predicting unit configured to predict a classification label to be assigned to the target time-series data, based on the second database.
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Abstract
A time-series data classifying apparatus may include a first database, a peak feature extracting unit, a second database, a data input unit, and a predicting unit. The first database stores a plurality of cases each including time-series data a classification label. The peak feature extracting unit may, for each of the cases, calculate intersection points of time-series data expanded in a coordinate system and each reference line, detect a peak point in each of sections formed between two intersection points being adjacent to generate a peak feature sequence that contains a sequence of detected peak points. The second database may store each peak feature sequence in association with a classification label of each of the cases. The data input unit may input target time-series data. The predicting unit may predict a classification label to be assigned to the target time-series data based on the second database.
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Citations
25 Claims
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1. A time-series data classifying apparatus, comprising:
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a first database configured to store a plurality of cases each including time-series data in which an observed value obtained by observing an observation object is sequentially recorded in associated with an observed time and a classification label that represents a state or type of the observation object as when the observation object is observed; a peak feature extracting unit configured to, for each of the cases, expand the time-series data in a coordinate system which is made up of a time axis and a value axis representing the observed value, set along the time axis a reference line that intersects expanded time-series data, detect intersection points of the expanded time-series data and the reference line, and detect a peak point of the expanded time-series data in each of sections each formed between two intersection points being adjacent to generate a peak feature sequence that contains the peak point detected in each of the sections; a second database configured to store the peak feature sequence generated for each of the cases in association with a classification label of each of the cases; a data input unit configured to input target time-series data; and a predicting unit configured to predict a classification label to be assigned to the target time-series data, based on the second database. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A time-series data classifying apparatus, comprising:
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a first database configured to store a plurality of cases each including time-series data in which an observed value obtained by observing an observation object is sequentially recorded in associated with an observed time and a classification label that represents a state or type of the observation object as when the observation object is observed; a peak feature extracting unit configured to, for each of the cases, expand the time-series data in a coordinate system which is made up of a time axis and a value axis representing the observed value, set along the time axis a reference line that intersects expanded time-series data, detect intersection points of the expanded time-series data and the reference line, and detect a peak point of the expanded time-series data in each of sections each formed between two intersection points being adjacent to generate a peak feature sequence that contains the peak point detected in each of the sections; a second database configured to store the peak feature sequence generated for each of the cases in association with a classification label of each of the cases. - View Dependent Claims (21, 22, 23, 24)
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25. A time-series data classifying method, comprising:
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providing a first database which stores a plurality of cases each including time-series data in which an observed value obtained by observing an observation object is sequentially recorded in associated with an observed time and a classification label that represents a state or type of the observation object as when the observation object is observed; for each of the cases, expanding the time-series data in a coordinate system which is made up of a time axis and a value axis representing the observed value, setting along the time axis a reference line that intersects expanded time-series data, detecting intersection points of the expanded time-series data and the reference line, and detecting a peak point of the expanded time-series data in each of sections each formed between two intersection points being adjacent to generate a peak feature sequence that contains the peak point detected in each of the sections; storing the peak feature sequence generated for each of the cases in association with a classification label of each of the cases, in a second database; inputting target time-series data; and predicting a classification label to be assigned to the target time-series data based on the second database.
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Specification