Analyzing time series data that exhibits seasonal effects
First Claim
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1. A method executed by a computer to analyze a time series of data that exhibits seasonal effects associated with an enterprise, comprising:
- processing, by the computer, the time series to obtain a representation in a frequency domain;
according to the representation in the frequency domain, identifying, by the computer, plural cycle lengths as representing different seasonal effects of the data in the time series, wherein a first of the plural cycle lengths is greater than a second of the plural cycle lengths; and
building, by the computer, a model that is calibrated for the identified plural cycle lengths,wherein building the model comprises;
dividing the time series into a training sample part and a test sample part;
building an initial model based on the training sample part;
verifying the initial model using the test sample part; and
in response to successful verification, extending the model to cover the entire time series.
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Abstract
To analyze a time series of data that exhibits seasonal effects, the time series is processed to obtain a representation in the frequency domain. According to the representation, plural cycle lengths are identified as representing different seasonal effects of the data in the time series, where a first of the plural cycle lengths is greater than a second of the plural cycle lengths.
26 Citations
19 Claims
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1. A method executed by a computer to analyze a time series of data that exhibits seasonal effects associated with an enterprise, comprising:
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processing, by the computer, the time series to obtain a representation in a frequency domain; according to the representation in the frequency domain, identifying, by the computer, plural cycle lengths as representing different seasonal effects of the data in the time series, wherein a first of the plural cycle lengths is greater than a second of the plural cycle lengths; and building, by the computer, a model that is calibrated for the identified plural cycle lengths, wherein building the model comprises; dividing the time series into a training sample part and a test sample part; building an initial model based on the training sample part; verifying the initial model using the test sample part; and in response to successful verification, extending the model to cover the entire time series. - View Dependent Claims (2, 3, 4, 5, 6, 9)
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7. A method executed by a computer to analyze a time series of data that exhibits seasonal effects associated with an enterprise, comprising:
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processing, by the computer, the time series to obtain a representation in a frequency domain; according to the representation in the frequency domain, identifying, by the computer, plural cycle lengths as representing different seasonal effects of the data in the time series, wherein a first of the plural cycle lengths is greater than a second of the plural cycle lengths; and confirming at least one of the plural cycle lengths, wherein confirming the at least one of the plural cycle lengths comprises; removing an effect of a cycle length more significant than the at least one of the plural cycle lengths from the time series, wherein removing the effect causes production of a modified time series; and processing the modified time series to obtain a second representation in the frequency domain; and according to the second representation, identifying a most significant cycle length to compare to the at least one of the plural cycle lengths for confirming the at least one of the plural cycle lengths. - View Dependent Claims (8)
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10. A method executed by a computer to analyze a time series of data that exhibits a seasonal effect associated with an enterprise, comprising:
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producing, by the computer, a spectral density of the time series; normalizing, by the computer, values in the spectral density with respect to an aggregate of the values of the spectral density at corresponding frequencies to produce a normalized spectral density; determining, by the computer, a cycle length corresponding to the seasonal effect based on identifying a peak in the normalized values of the normalized spectral density; and computing the aggregate of the values of the spectral density by computing a sum of the values of the spectral density, and wherein normalizing the values in the spectral density comprises one of;
(1) subtracting the sum from each value of the spectral density; and
(2) dividing each value of the spectral density by the sum.
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11. A method executed by a computer to analyze a time series of data that exhibits a seasonal effect associated with an enterprise, comprising:
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producing, by the computer, a spectral density of the time series; normalizing, by the computer, values in the spectral density with respect to an aggregate of the values of the spectral density at corresponding frequencies to produce a normalized spectral density; determining, by the computer, a cycle length corresponding to the seasonal effect based on identifying a peak in the normalized values of the normalized spectral density; and performing outlier analysis of the time series to replace one or more outlier data values in the time series prior to producing the spectral density. - View Dependent Claims (12, 13, 14, 15)
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16. A computer-readable storage medium storing instructions that when executed cause a computer to:
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process a time series to obtain a first representation in a frequency domain; according to the first representation, identify plural cycle lengths as representing different seasonal effects of data of the time series, wherein the seasonal effects are associated with an enterprise, and wherein a first of the plural cycle lengths is greater than a second of the plural cycle lengths; and confirm a first one of the plural cycle lengths by; removing an effect of a second one of the plural cycle lengths from the time series, wherein removing the effect causes production of a modified time series; processing the modified time series to obtain a second representation in the frequency domain; and according to the second representation, identifying a cycle length to compare to the first cycle length for confirming the first cycle length. - View Dependent Claims (17, 18, 19)
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Specification