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US 11,635,454 B2Granted
Model structure selection apparatus, method, disaggregation system and program
AssigneeNEC Corporation
InventorsMurtuza Petladwala, Ryota Suzuki, Shigeru Koumoto
Life of this patent
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Abstract
Provided an apparatus that receives time series data from a data storage unit storing time series of sample data or feature values calculated from the sample data, computes a measure indicating change and repetition characteristics of the time series data, based on sample value distribution thereof, selects a state model structure to be used for model learning and estimation, from state models including a fully connected state model and a one way direction state model, based on the measure and stores the selected state model in a model storage unit.
Claims11
A state model structure selection apparatus comprising:
- a processor;
- and
- a memory storing program instructions executable by the processor is configured to execute the program instructions stored in the memory to
- receive time series data from a data storage unit that stores time series of sample data or feature values calculated from the sample data;
- compute a measure indicating change and repetition characteristics of the time series data, based on sample value distribution thereof;
- and
- select a state model structure to be used for model learning and estimation, from state models including a fully connected state model and a one way direction state model, based on the measure and stores the selected state model in a storage unit that stores the state model selected, wherein the processor is configured to compute:
- a first probability of number of cycles for each magnitude value in the time series data, by dividing number of cycles of the magnitude value by total number of cycles in the time series data;
- and
- a second probability of number of occurrences for each magnitude value in the time series data, by dividing number of occurrences of the magnitude value by a length of the time series data;
- compute a correlation coefficient between the first probability and the second probability, and
- select either the fully connected state model or the one way direction state model, based on the correlation coefficient, as the measure.
9 more elements
A computer based method for selecting a state model structure, comprising:
- receiving time series data from a data storage unit that stores time series of sample data or feature values calculated from the sample data;
- computing a measure indicating change and repetition characteristics of the time series data, based on sample value distribution thereof received;
- selecting a state model structure to be used for model learning and estimation, from state models including a fully connected state model and a one way direction state model, based on the measure;
- and
- storing the state model selected in a model storage unit,
- the method further comprising:
- computing a first probability of number of cycles for each magnitude value in the time series data, by dividing number of cycles of the magnitude value by total number of cycles in the time series data;
- computing a second probability of number of occurrences for each magnitude value in the time series data, by dividing number of occurrences of the value by a length of the time series data;
- computing a correlation coefficient between the first probability and the second probability;
- and
- selecting either the fully connected state model or the one way direction state model, based on the correlation coefficient, as the measure.
8 more elements
A non-transitory computer-readable medium storing a program causing a computer to execute processing comprising:
- receiving time series data from a data storage unit that stores time series of sample data or feature values calculated from the sample data;
- computing a measure indicating change and repetition characteristics of the time series data, based on sample value distribution thereof received;
- selecting a state model structure to be used for model learning and estimation, from state models including a fully connected state model and a one way direction state model, based on the measure;
- and
- storing the state model selected in a model storage unit, the processing further comprising:
- computing a first probability of number of cycles for each magnitude value in the time series data, by dividing number of cycles of the magnitude value by total number of cycles in the time series data;
- computing a second probability of number of occurrences for each magnitude value in the time series data, by dividing number of occurrences of the value by a length of the time series data;
- computing a correlation coefficient between the first probability and the second probability;
- and
- selecting either the fully connected state model or the one way direction state model, based on the correlation coefficient, as the measure.
7 more elements
Dependencies are inferred from the claim text.