Patent Publication Number: US-11397792-B2

Title: Anomaly detecting device, anomaly detecting method, and recording medium

Description:
This application is a National Stage Entry of PCT/JP2017/031894 filed on Sep. 5, 2017, which claims priority from Japanese Patent Application 2016-175402 filed on Sep. 8, 2016, the contents of all of which are incorporated herein by reference, in their entirety. 
     TECHNICAL FIELD 
     The present invention relates to an anomaly detecting device, an anomaly detecting method, and a recording medium which detect an anomaly from series data. 
     BACKGROUND ART 
     In the technical field described above, patent literature (PTL) 1, for example, describes the following method. The method is a method which represents, by a probability distribution, a probability that sequentially-input series data are generated, thereby models a state of a generation mechanism of series data at generation, and then detects a statistical outlier emerging in series data and a change point in a state of a generation mechanism. 
     Moreover, PTL 2 describes a method of detecting an anomaly of an observation target by threshold-processing a dissimilarity indicating a degree of difference between a statistical amount of a probability density function of each variable of sequentially-input series data and a statistical amount of a probability density function of a predetermined reference. 
     CITATION LIST 
     Patent Literature 
     
         
         [PTL 1] Japanese Unexamined Patent Application Publication No. 2004-054370 
         [PTL 2] Japanese Unexamined Patent Application Publication No. 2006-331300 
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     However, in real issues, a change in a state of a generation mechanism is not always an anomaly targeted for detection. For example, it is assumed that, from series data acquired by using such a generation mechanism as to operate while a state thereof constantly changes during a normal operation, an anomaly of the state of the generation mechanism is detected. In such a case, the method described in PTL 1 has an issue that an anomaly truly needed to detect cannot be accurately detected, because a change in an operating state in a normal state is also sensed as an outlier of series data or a change point in a state. 
     Moreover, the method which simply threshold-processes a dissimilarity as described in PTL 2 also has an issue similar to that described above. In other words, with only simple threshold-processing of a dissimilarity, since a change in an operating state in a normal state is also sensed as an outlier of series data or a change point in a state, it is not possible to accurately detect an anomaly truly needed to detect. 
     In view of the problem described above, an object of the present technique is to enable an anomaly to be accurately detected even from series data resulting from a generation mechanism involving a change in a state. 
     Solution to Problem 
     An anomaly detecting device according to one aspect of the present invention includes: 
     a memory; and 
     at least one processor coupled to the memory, 
     the processor performing operations, the operations includes: 
     when first series data are input, extracting a series feature amount being a feature amount of a signal included in the first series data; 
     calculating a series probability distribution being a probability distribution which the series feature amount follows; 
     storing a reference probability distribution being a probability distribution designated as a reference for the series feature amount in the first series data; 
     calculating a state feature amount representing a fluctuation condition of the series probability distribution with respect to the reference probability distribution; and 
     detecting an anomaly of the first series data, based on a plurality of the state feature amounts calculated from the first series data. 
     An anomaly detecting method according one aspect of the present invention includes: 
     when first series data are input, extracting a series feature amount being a feature amount of a signal included in the first series data; 
     calculating a series probability distribution being a probability distribution which the series feature amount follows; 
     calculating a state feature amount representing a fluctuation condition of the series probability distribution with respect to a reference probability distribution being a probability distribution designated as a reference for the series feature amount in the first series data; and 
     detecting an anomaly of the first series data, based on a plurality of the state feature amounts calculated from the first series data. 
     A non-transitory computer-readable recording medium according to one aspect of the present invention computer-readably records an anomaly detecting program. The anomaly detecting program causes a computer to perform a method. The method includes: 
     when first series data are input, extracting a series feature amount being a feature amount of a signal included in the first series data; 
     calculating a series probability distribution being a probability distribution which the series feature amount follows; 
     calculating a state feature amount representing a fluctuation condition of the series probability distribution with respect to a reference probability distribution being a probability distribution designated as a reference for the series feature amount in the first series data; and 
     detecting an anomaly of the first series data, based on a plurality of the state feature amounts calculated from the first series data. 
     Advantageous Effects of Invention 
     According to the present invention, it is possible to accurately detect an anomaly even from series data resulting from a generation mechanism involving a change in a state. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram illustrating a configuration example of an anomaly detecting device according to a first example embodiment. 
         FIG. 2  is a flowchart illustrating one example of an operation of the anomaly detecting device according to the first example embodiment. 
         FIG. 3  is a block diagram illustrating a configuration example of an anomaly detecting device according to a second example embodiment. 
         FIG. 4  is a block diagram illustrating an example of a reference-probability-distribution generation unit. 
         FIG. 5  is a flowchart illustrating one example of a processing flow of reference-probability-distribution generation processing. 
         FIG. 6  is a block diagram illustrating a configuration example of an anomaly detecting device according to a third example embodiment. 
         FIG. 7  is a block diagram illustrating an example of a normal-model generation unit. 
         FIG. 8  is a flowchart illustrating one example of an operation of the anomaly detecting device according to the third example embodiment. 
         FIG. 9  is a block diagram illustrating a configuration example of an anomaly detecting device according to a fourth example embodiment. 
         FIG. 10  is a flowchart illustrating one example of an operation of the anomaly detecting device according to the fourth example embodiment. 
         FIG. 11  is a block diagram illustrating a configuration example of an anomaly detecting device according to a fifth example embodiment. 
         FIG. 12  is a block diagram illustrating an example of a series data analyzing unit. 
         FIG. 13  is a flowchart illustrating one example of an operation of the anomaly detecting device according to the fifth example embodiment. 
         FIG. 14  is a block diagram illustrating a configuration example of an anomaly detecting device according to a sixth example embodiment. 
         FIG. 15  is a flowchart illustrating one example of an operation of the anomaly detecting device according to the sixth example embodiment. 
         FIG. 16  is a block diagram illustrating one example of a hardware configuration. 
     
    
    
     EXAMPLE EMBODIMENT 
     Hereinafter, example embodiments of the present invention will be described with reference to the drawings. 
     A direction of an arrow in the drawing illustrates one example, and does not limit a direction of a signal between blocks. 
     Example Embodiment 1 
     An anomaly detecting device  100  according to a first example embodiment of the present invention is described with reference to the drawings. The anomaly detecting device  100  illustrated in  FIG. 1  includes a series-feature extracting unit  101 , a series-probability-distribution calculating unit  102 , a reference-probability-distribution storage unit  103 , a state-feature calculating unit  104 , and an anomaly detecting unit  105 . 
     The series-feature extracting unit  101  acquires series data (a signal in  FIG. 1 ) as an input, and outputs a feature amount extracted from the series data. More specifically, the series-feature extracting unit  101  extracts a predetermined feature amount from input series data, and then outputs the feature amount. A feature amount extracted by the series-feature extracting unit  101  may be, for example, a predetermined feature amount of a signal at a time point of each time frame defined by a predetermined time unit. Hereinafter, there is a case where a feature amount extracted from series data is referred to as a series feature amount. 
     The series-probability-distribution calculating unit  102  acquires a series feature amount as an input, and calculates and outputs a series probability distribution being a probability distribution which the series feature amount follows. Herein, a series probability distribution needs only to be something representing as to what kind of series feature amount and with what kind of probability the series data output. In other words, a series probability distribution is related to under what state of a generation mechanism the series data serving as a basis of the series feature amount are generated. 
     The reference-probability-distribution storage unit  103  stores a reference probability distribution being a probability distribution serving as a reference for a series probability distribution. A series probability distribution is related to a state set as a reference by a user, in a state of a generation mechanism of input series data. 
     The state-feature calculating unit  104  acquires, as inputs, a series probability distribution calculated by the series-probability-distribution calculating unit  102 , and a reference probability distribution stored in the reference-probability-distribution storage unit  103 , and outputs a state feature amount. More specifically, the state-feature calculating unit  104  calculates a state feature amount representing a fluctuation condition of a series probability distribution viewed from an input reference probability distribution, and then outputs the state feature amount. In the present example embodiment, the state-feature calculating unit  104  calculates a plurality of state feature amounts. The state-feature calculating unit  104  may calculate, for example, a state feature amount for each signal included in series data, as a second feature amount of a signal for which a series feature amount is calculated. Moreover, for example, the state-feature calculating unit  104  may output a calculated state feature amount, in relation to a time index of a signal included in series data. 
     The anomaly detecting unit  105  acquires, as inputs, a plurality of state feature amounts output from the state-feature calculating unit  104 , and outputs presence or absence of an anomaly of series data. More specifically, the anomaly detecting unit  105  senses presence or absence of an anomaly in a state of a generation mechanism of input series data, based on a plurality of input state feature amounts, and then outputs presence or absence of an anomaly. 
     For example, the anomaly detecting unit  105  may designate, as second series data, data in which a state feature amount, calculated from series data, for respective time frames defined by a predetermined time unit are arranged in a time-series form, statistically process the second series data, and then sense presence or absence of an anomaly. The anomaly detecting unit  105  may calculate a probability distribution of a state feature amount, as a model of a state feature amount in series data, for example, based on two or more state feature amounts, and sense presence or absence of an anomaly by using the calculated probability distribution of the state feature amount. 
     Next, an operation in the present example embodiment is described.  FIG. 2  is a flowchart illustrating one example of an operation of the anomaly detecting device  100  according to the present example embodiment. 
     In the example illustrated in  FIG. 2 , first, series data are input to the anomaly detecting device  100  (step S 101 ). Next, the series-feature extracting unit  101  calculates a series feature amount from the input series data (step S 102 ). 
     Next, the series-probability-distribution calculating unit  102  calculates a series probability distribution, based on the calculated series feature amount (step S 103 ). 
     Next, the state-feature calculating unit  104  calculates a state feature amount, based on the calculated series probability distribution, and a reference probability distribution stored in the reference probability distribution storage unit  103  (step S 104 ). 
     Finally, the anomaly detecting unit  105  detects an anomaly of the series data, based on the two or more state feature amounts calculated from the series data (step S 105 ). 
     As described above, based on the present example embodiment, an anomaly is detected by using a plurality of state feature amounts representing fluctuation conditions of states of a generation mechanism of input series data with respect to a reference state, and therefore, it is possible to statistically treat the fluctuation conditions of the generation mechanism which changes from moment to moment. In other words, in the present example embodiment, an anomaly is detected by using two or more state feature amounts, on a presumption that a state feature amount changes from moment to moment. Thus, even when a state of a generation mechanism of input series data changes, an anomaly, for example an anomalous outlier or change, can be appropriately detected from the series data. 
     Example Embodiment 2 
     Next, an anomaly detecting device  200  according to a second example embodiment of the present invention is described.  FIG. 3  is a configuration diagram illustrating an example of the anomaly detecting device  200  according to the present example embodiment. The anomaly detecting device  200  illustrated in  FIG. 3  includes a series-data analyzing unit  210  and an anomaly detecting unit  205 . 
     The series-data analyzing unit  210  is a processing unit which acquires series data (a signal in  FIG. 3 ) as an input, and outputs a state feature amount, and includes a series-feature extracting unit  201 , a probability-distribution calculating unit  202 , a reference-probability-distribution storage unit  203 , and a state-feature calculating unit  204 . 
     Note that the series-feature extracting unit  201 , the probability-distribution calculating unit  202 , the reference-probability-distribution storage unit  203 , and the state-feature calculating unit  204  relate to the series-feature extracting unit  101 , the series-probability-distribution calculating unit  102 , the reference-probability-distribution storage unit  103 , and the state-feature calculating unit  104  according to the first example embodiment. As illustrated in  FIG. 3 , the anomaly detecting device  200  may be implemented with these processing units as one combined unit (the series-data analyzing unit  210  in the present example embodiment), or may be individually implemented with the processing units as in the first example embodiment. 
     In the present example embodiment as well, the series-feature extracting unit  201  extracts a predetermined feature amount (series feature amount) from input series data, and then outputs the feature amount. The probability-distribution calculating unit  202  acquires the series feature amount as an input, and calculates and outputs a probability distribution (series probability distribution) which the input series feature amount follows. The reference-probability-distribution storage unit  203  stores a reference probability distribution. The state-feature calculating unit  204  acquires, as inputs, the series probability distribution and the reference probability distribution, and calculates and outputs a state feature amount, based on the input series probability distribution and the reference probability distribution. 
     Furthermore, the anomaly detecting unit  205  relates to the anomaly detecting unit  105  according to the first example embodiment. The anomaly detecting unit  205  acquires, as an input, the state feature amount output from the series-data analyzing unit  210 , and detects an anomaly. 
     Hereinafter, processing in each processing unit of the anomaly detecting device  200  is more specifically described, by using a case where input series data are a time-series acoustic signal x(t) as an example. 
     Herein, t is an index representing time, and is a time index of an acoustic signal sequentially input with a predetermined time (e.g., a time when the device is activated) as an origin, i.e., t=0. Moreover, x(t) is, for example, a digital signal series acquired by analog to digital conversion (AD conversion) of an analog signal recorded with a sensor such as a microphone. 
     The present example embodiment assumes the following case. A microphone is placed in an environment where an anomaly needs to be detected (hereinafter, referred to as a target environment). Then, an acoustic signal recorded with the microphone is sequentially input to the anomaly detecting device  200 . Then, an anomalous change is detected in states of a target environment which changes from moment to moment. In such a case, activity of a person existing in a target environment, operation of an apparatus driven in a target environment, a state of a peripheral environment of a target environment, or the like corresponds to a state of a generation mechanism of series data. The present example embodiment is intended to detect an anomalous change in status of a target environment which changes from moment to moment (more specifically, a state of a generation mechanism of series data). 
     The series-data analyzing unit  210  acquires series data x(t) as an input, and outputs a state feature amount d(i). Hereinafter, operations of the series-feature extracting unit  201 , the probability-distribution calculating unit  202 , the reference-probability-distribution storage unit  203 , and the state-feature calculating unit  204  constituting the series-data analyzing unit  210  are described. 
     The series-feature-extracting unit  201  acquires series data x(t) as an input, and extracts and outputs a predetermined series feature amount y(i). Herein, i represents a time frame index. Moreover, y(i) is a vector which stores K-dimensional feature amount at a time frame i. A time frame is a unit of time width for extracting the series feature amount y(i) from the series data x(t). A time width represented by i represents may be determined in accordance with what series feature amount is extracted. For example, when series data are acoustic signals, i is generally set to approximately 10 milliseconds (ms). In this case, t=0 and i=0 are considered as references, and thereby i has such a correlation that t=10 ms when i=1, t=20 ms when i=2, . . . . 
     A user may select an expression form of a feature amount of a signal designated as a series feature amount, depending on a kind of signal. As a feature amount of an acoustic signal, a publicly known mel-frequency cepstrum coefficients (MFCC) feature amount or the like is widely used in general. When the MFCC feature amount is used, approximately 20 is used as a dimensional number K of a feature amount. Hereinafter, a case where the MFCC feature amount is used for y(i) is described as an example, but a series feature amount is not limited to the MFCC feature amount, and may be any feature amount expressing a frequency and/or power of sound. 
     The probability-distribution calculating unit  202  calculates and outputs a series probability distribution p i (y) being a probability distribution which the input series feature amount y(i) follows. In an example of an acoustic signal, the series probability distribution p i (y) represents what and how much sound is included in a target environment, at a time point of the time frame i. For example, a Gaussian mixture distribution, a hidden Markov model, or the like is used as an expression form of the series probability distribution p i (y). 
     When the Gaussian mixture distribution is used, the series probability distribution p i (y) is represented as in [Equation 1] below. 
     
       
         
           
             
               
                 
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     Herein, N(y|μ r,i ,Σ r,i ) represents a K-dimensional Gaussian distribution characterized by a K-dimensional mean vector μ r,i  and a covariance matrix Σ r,i  of K×K. R represents a total number of Gaussian distributions, r represents an index of each Gaussian distribution, and π r,i  represents weight of an r-th Gaussian distribution. 
     Note that, when the hidden Markov model is used, it is only necessary to designate, as a latent state, a state of a mechanism for generating series data, designate, as observation data, a series feature amount being a feature amount of series data generated therefrom, and then calculate, from the series data, a probability that the series data are observed, and a transition probability of the state of the mechanism. 
     A reference probability distribution p s (y) is stored in the reference-probability-distribution storage unit  203 . For example, a probability distribution in an expression form similar to the series probability distribution p i (y) is used for the reference probability distribution p s (y). When the Gaussian mixture distribution is used, the reference probability distribution p s (y) is represented as in [Equation 2] below. 
     
       
         
           
             
               
                 
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     The state-feature calculating unit  204  acquires the series probability distribution p i (y) and the reference probability distribution p s (y) as inputs, and extracts the state feature amount d(i) representing a fluctuation condition of the series probability distribution p i (y) viewed from the reference probability distribution p s (y). 
     In the present example embodiment, the state-feature calculating unit  204  uses, as the state feature amount d(i), a distance, between input probability distributions, calculated by a predetermined method. The state-feature calculating unit  204  may use, for example, Kullback-Leibler divergence (KL divergence) between p s (y) and p i (y) or the like, as the state feature amount d(i). In addition, it is conceivable that, when the Gaussian mixture distribution is used for a probability distribution as an example of the state feature amount d(i), the state-feature calculating unit  204  uses the following distance. The distance is a vector in which R numbers of KL divergences of respective r-th Gaussian distributions are arranged, a norm of the vector, an R-dimensional vector in which R numbers of square distances of mean vectors of respective r-th Gaussian distributions are arranged, a norm of the R-dimensional vector, or the like. 
     For example, when an R-dimensional vector in which R numbers of square distances of mean vectors of respective r-th Gaussian distributions are arranged is used, the state feature amount d(i) becomes a vector value represented by [Equation 3] below.
 
[Equation 3]
 
 d ( i )=[(μ 1,i −μ 1,s ) 2 , . . . ,(μ r,i −μ r,s ) 2 , . . . ,(μ R,i −μ R,s ) 2 ]  (3)
 
     Furthermore, for example, when a norm of an R-dimensional vector in which R numbers of square distances of mean vectors of respective r-th Gaussian distributions are arranged is used as the state feature amount d(i), the state feature amount d(i) becomes a scalar value represented by [Equation 4] below.
 
[Equation 4]
 
 d ( i )=(μ 1,i −μ 1,s ) 2 + . . . +(μ r,i −μ r,s ) 2 + . . . +(μ R,i −μ R,s ) 2   (4)
 
     In the case of a vector value, the state feature amount d(i) becomes a feature amount representing a direction of a change from the reference probability distribution p s (y) to the series probability distribution p i (y). Furthermore, in the case of a scalar value, the state feature amount d(i) becomes a feature amount representing magnitude of a change from p s (y) to p i (y). 
     Hereinafter, the state feature amount d(i) is described as a scalar value. However, in the case of a vector value, the anomaly detecting device  200  may alter the anomaly detecting unit  205  at a subsequent stage into a format related to a vector value such as to change to an anomaly detecting method in which a vector value is acquired as an input. 
     The reference-probability-distribution storage unit  203  may hold, as the reference probability distribution p s (y), for example, a probability distribution previously calculated by using, as series data for calculating a reference probability distribution, series data when a generation mechanism of series data is in a predetermined state. 
     Note that, although illustration is omitted, the anomaly detecting device  200  may include, at a stage prior to the reference-probability-distribution storage unit  203 , a reference-probability-distribution generation unit that calculates a reference probability distribution from series data for calculating a reference probability distribution and then stores the reference probability distribution in the reference-probability-distribution storage unit  203 . 
     The reference-probability-distribution storage unit  203  may hold, as series data for calculating a reference probability distribution, for example, a probability distribution calculated by using an acoustic signal recorded in a silent late-night situation. In this case, the state feature amount d(i) becomes a feature amount representing a fluctuation condition of how a state of a target environment in a time frame i changes as compared with a state of a silent target environment. Otherwise, a probability distribution calculated by using all acoustic signals recorded for one day, or a probability distribution calculated by using an acoustic signal in a particular time interval of interest may be used as a reference probability distribution. Moreover, for example, when a state feature amount is calculated from the series probability distribution p i (y), the reference-probability-distribution storage unit  203  may use a series probability distribution p i-1 (y) as a reference probability distribution. 
     Herein, a reference probability distribution calculation method is described with reference to  FIG. 4 .  FIG. 4  is a block diagram illustrating an example of the above-described reference-probability-distribution generation unit. In the reference probability distribution calculation method, first, series data for calculating a reference probability distribution (a signal for calculating a reference probability distribution in  FIG. 4 ) are input to a series-feature extracting unit  221 . The series-feature extracting unit  221  extracts and outputs a series feature amount from input series data for calculating a reference probability distribution. Next, a reference-probability-distribution calculating unit  222  acquires the calculated series feature amount as an input, calculates a probability distribution thereof, and stores the probability distribution in the reference-probability-distribution storage unit  203 . Note that operations of the series-feature extracting unit  221  and the reference-probability-distribution calculating unit  222  may be similar to those of the series-feature extracting unit  201  and the probability-distribution calculating unit  202 . Moreover, prepared data, or past data (particularly, series data which are not determined to be anomalous) acquired during an operation of the anomaly detecting device  200  may be used for series data for calculating a reference probability distribution. In the latter case, the reference-probability-distribution generation unit may successively calculate a reference probability distribution. 
     Note that the operations of the series-feature extracting unit  221  and the reference-probability-distribution calculating unit  222  in the reference-probability-distribution generation unit may be performed by the series-feature extracting unit  201  and the probability-distribution calculating unit  202  of the series-data analyzing unit  210 . 
     The anomaly detecting unit  205  acquires the state feature amount d(i) as an input, and senses an anomalous state of a generation mechanism of series data x(t). The anomaly detecting unit  205  may designate, as second series data, for example, data in which state feature amounts d(i) are arranged in a time-series form, statistically process the second series data, and then detect an anomaly (a statistical outlier or a change point in a state of a generation mechanism). The anomaly detecting unit  205  can use a method described in above-described PTL 1 as statistical processing. In this case, the anomaly detecting unit  205  sequentially inputs the state feature amount d(i) for each time frame as series data, and models the probability generated by such series data (a series including prior state feature amount), by representing a probability. Then, the anomaly detecting unit  205  may detect a statistical outlier or a change point in a state of a generation mechanism, based on an outlier score calculated based on a modeled probability distribution and input series data (a latest state feature amount d(i)). 
     Note that the operation in the present example embodiment is only different in that generation processing for reference probability distribution is separately performed (as preprocessing of anomaly detecting processing, or in parallel with anomaly detecting processing), and is basically similar to that in the first example embodiment. Note that the generation processing for reference probability distribution may be performed by a device other than the anomaly detecting device  200 . Moreover, in the following present example embodiment, there is a case where operations in the step S 102  to the step S 104  in the first example embodiment illustrated in  FIG. 2  are referred to as series data analyzing processing. 
       FIG. 5  is a flowchart illustrating an example of a processing flow of reference-probability-distribution generation processing. In the example illustrated in  FIG. 5 , first, when series data for generating a reference probability distribution are input (step S 211 ), the series-feature extracting unit  221  extracts a series feature amount from the input series data for generating a reference probability distribution (step S 212 ). 
     Next, the reference-probability-distribution calculating unit  222  calculates a probability distribution which the calculated series feature amount follows (step S 213 ), and stores the probability distribution in the reference-probability-distribution storage unit  203  as a reference probability distribution (step S 214 ). 
     As described above, based on the present example embodiment, it is possible to accurately detect an anomaly from series data generated from a generation mechanism involving a change in a state, as in the first example embodiment. 
     In contrast, the method described in PTL 1 is a method of sensing a change point or an outlier, based on a series feature amount output by the series-feature extracting unit  201  referred to in the present example embodiment. Thus, in a situation where a generation mechanism of series data changes from moment to moment, all the changes of the generation mechanism are sensed as sensing targets, and therefore, it is not possible to determine whether the change is normal or anomalous to the generation mechanism. The present example embodiment detects an anomaly by using a state feature amount based on a fluctuation condition of a state in a generation mechanism of input series data with respect to a reference state, and therefore solves such an issue. 
     Example Embodiment 3 
     Next, an anomaly detecting device  300  according to a third example embodiment of the present invention is described.  FIG. 6  is a configuration diagram illustrating an example of the anomaly detecting device  300  according to the present example embodiment. The anomaly detecting device  300  illustrated in  FIG. 6  includes a series-data analyzing unit  301 , a normal-model storage unit  302 , and an anomaly detecting unit  303 . 
     The series-data analyzing unit  301  may be similar to the series-data analyzing unit  210  according to the second example embodiment. In other words, the series-data analyzing unit  301  acquires the series data x(t) (a signal in  FIG. 6 ) as an input, and calculates and outputs the state feature amount d(i). 
     The normal-model storage unit  302  stores a normal model in which a state feature amount in a normal state (normal time) is modeled. The normal model may be, for example, a probability distribution of a state feature amount in a normal state at a time point indicated by a model index signifying at least a time within a particular period. Alternatively, for example, the normal-model storage unit  302  may store a plurality of normal models related to a plurality of model indexes signifying different times within a particular period. 
     Note that, although illustration is omitted, the anomaly detecting device  300  may include, at a stage prior to the normal-model storage unit  302 , a normal-model generation unit that calculates a normal model from series data for calculating a normal model and then stores the normal model in the normal-model storage unit  302 . 
       FIG. 7  is a block diagram illustrating an example of the normal-model generation unit. In a normal model calculation method, first, series data for calculating a normal model (a signal for calculating a normal model in  FIG. 7 ) are input to a series-data analyzing unit  311 . The series-data analyzing unit  311  calculates a state feature amount from the input series data for calculating a normal model. Next, a normal-model calculating unit  312  acquires the calculated state feature amount as an input, models status of a periodic change of the state feature amount, and stores the model in the normal-model storage unit  302  as a normal model. Note that an operation of the series-data analyzing unit  311  may be similar to that of the series-data analyzing unit  301 . Moreover, prepared data, or past data (particularly, series data which are not determined to be anomalous) acquired during an operation of the anomaly detecting device  300  may be used for series data for calculating a normal model. In the latter case, the normal-model generation unit may successively calculate a normal model. 
     Note that the operation of the series-data analyzing unit  311  in the normal-model generation unit may be performed by the series-data analyzing unit  301 . 
     Hereinafter, a case where a probability distribution q m (d(i)) of a state feature amount d(i) of a time frame i related to m is used as a normal model is described as an example. Herein, m is an index regarding a model. 
     Hereinafter, a normal model generation method and an example of m are described by using, as an example, a case of detecting an anomaly of a facility from an acoustic signal. Sound generated in a facility or the like ordinarily changes with a period of one day, based on human activity. For example, it is assumed that a microphone is placed at an entrance of a school building for detection of an anomaly in a school facility or the like. In this case, at an entrance of a school building, there is almost no sound early in morning, and it rapidly becomes noisy with student&#39;s voice at a time of going to school. At an entrance of a school building, in daytime, a quiet time period and a noisy time period alternately repeat with a rapid change in accordance with repetitions of a lecture and a recess. Then, at an entrance of a school building, it gradually becomes silent after a time of leaving school, and night comes. In this way, a change of sound repeats with a 24-hour period. Along with such a change, the state feature amount d(i) also changes with a 24-hour period. 
     As already described, i is a time frame index, and keeps increasing in value while the device keeps operating with a predetermined time (e.g., a device operation start time) as 0. Considering a case where a normal model of each time in one day is made under such a condition, the normal-model generation unit needs only to designate m as a model index defined by a time (10:10 or the like) irrespective of date. For example, assuming that time width of a time frame i is one minute, and an actual time with an origin i=0 is 18:00 of a certain day, i=1440 also becomes 18:00. When m is defined by time by utilizing the property described above, the normal-model generation unit calculates q m (d(i)) by using a plurality of such d(i)s that i becomes the time m. Such q m (d(i)) is based on a premise that a state at the normal time m is observed with variation in accordance with a day (period) under a certain average. 
     When such q m (d(i)) is represented by a Gaussian distribution, q m (d(i)) becomes as in [Equation 5] below.
 
[Equation 5]
 
 q   m ( d ( i ))= N ( d ( i )|μ m ,Σ m )  (5)
 
     Note that, instead of the Gaussian distribution, the normal-model generation unit may use, for example, a Gaussian mixture distribution or a hidden Markov model, as an expression form of the normal model q m (d(i)). Note that, when the hidden Markov model is used, the normal-model generation unit designates a state of a generation mechanism of series data as a latent state. Then, the normal-model generation unit needs only to designate, as observation data, a state feature amount indicating a fluctuation condition of a series feature amount being a feature amount of series data generated from the generation mechanism with respect to a reference, and then calculate a probability that the state feature amount is observed in a normal state, and a transition probability of a state of a generation mechanism. 
     Furthermore, in the above-described example, an example in which a normal model is generated by using 24 hours as a unit of a period is described, but, when an activity period of a generation mechanism of series data to be a target is known in advance, a normal model needs only to be generated by using m defined by the activity period. For example, when a state (activity contents) of a generation mechanism of series data changes in accordance with not only time but also day of week, m may be defined by day of week and time, and a normal model related to time for each day of week may be generated. Moreover, for example, when a normal model related to repetitions of a lecture and a recess thereof at school needs to be generated, a user can freely set a period of generating a normal model so that m is defined in accordance to lengths of a lecture and a recess or the like. In addition, for a normal model, it is also possible to use a combination of a plurality of normal models, such as a normal model for each day of week, and a normal model for repetitions of a lecture and a recess thereof. In this case, one state feature amount may be used for generation of a plurality of normal models. 
     Based on a state feature amount d(i) input from the series-data analyzing unit  301 , the anomaly detecting unit  303  senses and outputs presence or absence of an anomaly in a state of a generation mechanism of series data input to the anomaly detecting device  300 . For example, the anomaly detecting unit  303  may calculate a score representing a probability that a normal model, which is indicated by using m to which i of an input state feature amount d(i) is related, takes the state feature amount d(i), and then sense presence or absence of an anomaly, based on the score. A score may be, for example, a probability value acquired by substituting an input state feature amount d(i) for a probability distribution q m (d(i)) stored in the normal-model storage unit  302  as a normal model. In this case, the anomaly detecting unit  303  may determine that there is an anomaly when a calculated score is less than a previously defined threshold, and may determine that there is no anomaly when a calculated score is equal to or more than the threshold. 
     Next, an operation in the present example embodiment is described.  FIG. 8  is a flowchart illustrating one example of an operation of the anomaly detecting device  300  according to the present example embodiment. 
     In the example illustrated in  FIG. 8 , first, series data x(t) are input to the anomaly detecting device  300  (step S 301 ). 
     Next, the series-data analyzing unit  301  performs analyzing processing of series data for the input series data x(t), and outputs an acquired state feature amount d(i) (step S 302 ). 
     Finally, the anomaly detecting unit  303  detects an anomaly of the series data x(t), based on the state feature amount d(i), and a normal model stored in the normal-model storage unit  302  (step S 303 ). 
     Note that, although illustration is omitted, the anomaly detecting device  300  may perform normal model generation processing as preprocessing of the above-described anomaly detecting processing, or in parallel with the anomaly detecting processing. 
     As described above, based on the present example embodiment, in addition to an advantageous effect in the second example embodiment, it is possible to sense not only an anomaly on a time series but also an anomaly based on a period of a state change of a generation mechanism of input series data. Therefore, based on the present example embodiment, it is possible to detect an anomaly viewed not only on a time series but also from any period. 
     Example Embodiment 4 
     Next, an anomaly detecting device  400  according to a fourth example embodiment of the present invention is described.  FIG. 9  is a configuration diagram illustrating an example of the anomaly detecting device  400  according to the present example embodiment. The anomaly detecting device  400  illustrated in  FIG. 9  includes a series-data analyzing unit  401 , a state-feature-series generation unit  402 , and an anomaly detecting unit  403 . 
     The series-data analyzing unit  401  is similar to the series-data analyzing unit  210  according to the second example embodiment. In other words, the series-data analyzing unit  401  acquires the series data x(t) (a signal in  FIG. 9 ) as an input, and calculates and outputs the state feature amount d(i). 
     The state-feature-series generation unit  402  acquires the state feature amount d(i) as an input, and outputs the state feature amount series d 2 (y). Herein, the state feature amount series d 2 (j) is a vector having a same dimensional number as d(i) acquired based on conversion (reconstruction) of the state feature amount d(i), or a scalar value. Moreover, j is an index regarding a state feature amount series. 
     Hereinafter, a generation method for a state feature amount series and an example of j are described by using, as an example, a case of detecting an anomaly of a facility from an acoustic signal. As already described, sound generated in a facility or the like ordinarily changes with a predetermined period (e.g., 24 hours) in accordance with human activity. This represents that a state feature amount also changes with a predetermined period. 
     Accordingly, j may be defined as an index of a time frame within such a period. For example, by assuming that time width of a time frame i is one minute, and an actual time with an origin i=0 is January 1, at 18:00, i=1440 becomes January 2, at 18:00. In such a case, j may be defined as a minute-by-minute time frame index within a 24-hour period. Then, d 2 (j) becomes a series of a state feature amount at each time indicated by j in one day. In other words, in the above-described example, when j 1 =18:00 is defined, the time frame i is represented as data in which d(i)s related to 18:00 are arranged as d 2 (j 1 )=[d(0), d(1440), . . . ]. 
     Furthermore, a case where a period is 24 hours has been described as an example this time. However, when an activity period of a generation mechanism of series data to be a target is known in advance, j needs only to be defined based on the activity period. In this instance, for example, when a state feature amount series needs to be generated for each time of each day of week, y may be defined by a day of week and time. Moreover, for example, such that, when a state feature amount series related to repetitions of a lecture and a recess thereof at school needs to be generated, j is defined in such a way as to be related to lengths of a lecture and a recess, a user may freely set a period of extracting a state feature amount as an element of a state feature amount series. In this instance, j is preferably defined in such a way that a state feature amount of a time frame that can be regarded as a same state in a state of a generation mechanism of repeated series data can be extracted. Note that definition of j is not limited to one definition. For example, state feature amount series related to a plurality of periods may be generated such as a state feature amount series for each day of week, and state feature amount series for each lecture and for each recess. In this case, one state feature amount may be used for generation of a plurality of state feature amount series. 
     The anomaly detecting unit  403  acquires the state feature amount series d 2 (j) as an input, and senses an anomalous state of a generation mechanism of series data x(t). The anomaly detecting unit  403  may detect presence or absence of an anomaly from the state feature amount series d 2 (j), for example, by using the method described in above-described PTL 1. In this case, for each j, the anomaly detecting unit  403  sequentially inputs the state feature amount series d 2 (j) as series data. Then, the anomaly detecting unit  403  models a probability that such series data (e.g., a series of the state feature amount d(i) having the prior time frame i related to the j) are generated, by representing the probability with a probability distribution. Then, the anomaly detecting unit  403  may detect a statistical outlier or a change point in a state of a generation mechanism, based on an outlier score calculated based on a modeled probability distribution and input series data (a latest state feature amount d(i) having a time frame i related to the j). 
     Next, an operation in the present example embodiment is described.  FIG. 10  is a flowchart illustrating one example of an operation of the anomaly detecting device  400  according to the present example embodiment. Note that operations in a step S 401  to a step S 402  are similar to those in the step S 301  to the step S 302  according to the third example embodiment. 
     In the present example embodiment, when a state feature amount d(i) is calculated in series data analyzing processing (step S 402 ), the state-feature-series generation unit  402  generates a state feature amount series d 2 (j) from the state feature amount d(i) (step S 403 ). 
     Finally, the anomaly detecting unit  403  detects an anomaly of series data x(t), based on the state feature amount series d 2 (j) (step S 404 ). 
     As described above, based on the present example embodiment, in addition to an advantageous effect in the second example embodiment, it is possible to detect not only an anomaly on a time series but also an anomaly based on a period of a state change of a generation mechanism of input series data. Therefore, based on the present example embodiment, it is possible to detect an anomaly viewed not only on a time series but also from any period. 
     Example Embodiment 5 
     Next, an anomaly detecting device  500  according to a fifth example embodiment of the present invention is described.  FIG. 11  is a configuration diagram illustrating an example of the anomaly detecting device  500  according to the present example embodiment. The anomaly detecting device  500  illustrated in  FIG. 11  includes a distributed-data analyzing unit  510 , a normal-model storage unit  502 , and an anomaly detecting unit  503 . 
     The distributed-data analyzing unit  510  is a processing unit which acquires each piece of series data x 1 (t), . . . , and x N (t) (signals 1 to N in  FIG. 11 ) generated from N numbers of generation mechanisms as an input, and calculates a state feature amount from each piece of series data. The distributed-data analyzing unit  510  includes N numbers of series-data analyzing units  501  (series-data analyzing units  501 - 1  to  501 -N). 
     The series-data analyzing units  501 - 1  to  501 -N respectively acquire series data x n (t) related to themselves as an input, and output state feature amounts d(i,n). Herein, n represents an index of a generation mechanism of series data (hereinafter, referred to as a mechanism index). For example, the series-data analyzing unit  501 - 1  acquires series data x 1 (t) as an input, and outputs a state feature amount d(i,1). 
     Hereinafter, in order to detect an anomaly from acoustic signals, a case in which N numbers of microphones are spatially distributed in an environment where an anomaly needs to be detected (a target environment) is considered. In such a case, a generation mechanism of series data x n (t) is equivalent to a peripheral environment of a microphone recording an acoustic signal (x n (t)). In this case, n may be defined as an identifier of a microphone, or may be defined as an index of a space where a microphone is placed (e.g., a three-dimensional coordinate of a place where a microphone is placed). Alternatively, n may be defined as an identifier of a place where a microphone is placed. For example, when a microphone is placed in each classroom for anomaly detection in a school facility, n may be defined as an index of a classroom where a microphone is placed. 
       FIG. 12  is a block diagram illustrating a configuration example of the series-data analyzing units  501 - n . As illustrated in  FIG. 12 , each series-data analyzing unit  501  includes a series-feature extracting unit  511 , a probability-distribution calculating unit  512 , a reference-probability-distribution storage unit  513 , and a state-feature calculating unit  514 . Note that operations of the series-feature extracting unit  511  and the probability-distribution calculating unit  512  are basically similar to those of the series-feature extracting unit  201  and the probability-distribution calculating unit  202  according to the second example embodiment. Similarly, operations of the reference-probability-distribution storage unit  513  and the state-feature calculating unit  514  are basically similar to those of the reference-probability-distribution storage unit  203  and the state-feature calculating unit  204  according to the second example embodiment. 
     Note that the reference-probability-distribution storage unit  513  can also store a reference probability distribution in a predetermined generation mechanism as a reference probability distribution. This is because an input of the anomaly detecting device  500  is each piece of series data generated from N numbers of generation mechanisms. In other words, in the anomaly detecting device  200  in which one piece of series data is input, a reference probability distribution represents a state of a target environment at a predetermined time. In contrast, in the present example embodiment, a normal state in a generation mechanism indicated by a mechanism index n can be defined as a reference. 
     For example, it is supposed that, when N numbers of microphones are spatially distributed, a peripheral state of the place represented by using the acoustic signal x n1 (t) acquired from the microphone placed at a first place (n=n1) is designated as a reference. In such a case, the state feature amount d(i,n) represent a feature of the peripheral state of the place indicated by the n when a peripheral state of the first place (n1) is regarded as a reference. In such a case, it is possible to sense an anomalous state by using not only a relationship in a time series but also a relationship (a spatial relationship when an acoustic signal is referred to) among a plurality of generation mechanisms. Note that a generation mechanism to be a reference may differ according to n. 
     The normal-model storage unit  502  stores a normal model in which a state feature amount d(i,n) in a normal state is modeled. Note that, although illustration is omitted, the anomaly detecting device  500  may include, at a stage prior to the normal-model storage unit  502 , the normal-model generation unit that generates a normal model from the state feature amount d(i,n) and stores the normal model in the normal-model storage unit  502 . 
     Hereinafter, a case where a probability distribution q m (d(i)) of a state feature amount d(i,n) is used as a normal model is described as an example. Herein, m is an index regarding a model. Note that, although m in the third example embodiment is an index relating to time, m in the present example embodiment is related to a mechanism index n in addition to time. For example, the normal-model generation unit may define m as “n related to the first place (n1)”, and then generate a normal model by using a state feature amount of the first place (n=n1). Alternatively, the normal-model generation unit may define m as “n and i at a time 18:00 related to the first place (n1)”, and then generate a normal model by using a state feature amount in which i is related to the time 18:00 among state feature amounts of the first place (n=n1). Moreover, for example, the normal-model generation unit may define m as “n related to n′(≠n) in which the first place (n1) is regarded as a reference”, and then generate a normal model by using a feature amount of n in which the first place (n1) is regarded as a reference. In addition, as in the third example embodiment, the normal-model generation unit may define m by combining a plurality of aspects (a day of week and time, a place and a reference, and the like) regarding a time and a mechanism index. Alternatively, the normal-model generation unit may generate a plurality of kinds of normal models by using a plurality of kinds of ms (m1, m2, . . . and the like) defined in accordance with the respective aspects. 
     Note that a normal model generation method may be similar to that in the third example embodiment. A normal model may be, for example, a probability distribution q m (d(i,n)) of a state feature amount d(i,n) of a time frame i and a mechanism index n related to m. 
     The anomaly detecting unit  503  senses and outputs presence or absence of an anomaly in a state of a generation mechanism of series data input to the anomaly detecting device  500 , based on a state feature amount d(i,n) input from the distributed-data analyzing unit  510 . For example, the anomaly detecting unit  503  may calculate a score representing a probability that a normal model, which is indicated in accordance with m to which i and n of each input state feature amount d(i,n) are related, takes the state feature amount d(i,n), and then sense presence or absence of an anomaly, based on the score. Note that a method of calculating a score and a method of determining presence or absence of an anomaly, based on a score, may be similar to those in the third example embodiment. 
     Next, an operation in the present example embodiment is described.  FIG. 13  is a flowchart illustrating one example of an operation of the anomaly detecting device  500  according to the present example embodiment. 
     In the example illustrated in  FIG. 13 , N numbers of series data x 1 (t), . . . , and x N (t) are input to the anomaly detecting device  500 . Each piece of series data x n (t) is input to the distributed-data analyzing unit  510 - n  related. Each of the distributed-data analyzing units  510 - n  performs series data analyzing processing for input series data x n (t) (step S 501 - 1  to step S 501 -N). The series data analyzing processing is similar to that in the second example embodiment. 
     Finally, the anomaly detecting unit  503  detects anomalies of the N numbers of series data x 1 (t), . . . , and x N (t), based on the state feature amounts d(i,n) acquired from the N numbers of the distributed-data analyzing units  510 - n , and a normal model (step S 502 ). 
     As described above, based on the present example embodiment, it is possible to sense an anomaly, based on the state feature amounts based on a relationship among a plurality of generation mechanisms, in addition to a relationship of time such as a period of change in a state of one generation mechanism. Therefore, in addition to an advantageous effect in the third example embodiment, it is possible to sense an anomaly based on a relationship among generation mechanisms of input series data. In other words, based on the present example embodiment, it is possible to detect an anomalous outlier or change viewed from a relationship among a plurality of generation mechanisms. 
     Example Embodiment 6 
     Next, an anomaly detecting device  600  according to a sixth example embodiment of the present invention is described.  FIG. 14  is a configuration diagram illustrating an example of the anomaly detecting device  600  according to the present example embodiment. The anomaly detecting device  600  illustrated in  FIG. 14  includes a distributed-data analyzing unit  610 , a state-feature-series generation unit  602 , and an anomaly detecting unit  603 . 
     The distributed-data analyzing unit  610  performs an operation similar to that of the distributed-data analyzing unit  510  according to the fifth example embodiment, acquires, as inputs, the series data x 1 (t), x 2 (t), . . . , and x N (t) (signals 1 to N in  FIG. 14 ) generated from N numbers of generation mechanisms, and outputs the state feature amount d(i,n). 
     The distributed-data analyzing unit  610  includes N numbers of series-data analyzing units  601  (series-data analyzing units  601 - 1  to  601 -N). 
     The state-feature-series generation unit  602  acquires the state feature amount d(i,n) as an input, and outputs a state feature amount series d 2 (j,n). Herein, the state feature amount series d 2 (j,n) is a vector having a same dimensional number as d(i,n) acquired based on conversion of the state feature amount d(i,n) or a scalar value. j is an index regarding a state feature amount series. Note that definition of j may be similar to that in the state-feature-series generation unit  402  according to the fourth example embodiment. In other words, the state feature amount series d 2 (j,n) can be said to be the state feature amount series d 2 (j) generated for each mechanism index n in the state-feature-series generation unit  402 . In other words, the state-feature-series generation unit  602  is obtained by changing a form related to n in such a way that the operation of the state feature series generation unit  402  is performed N times. 
     The anomaly detecting unit  603  acquires the state feature amount series d 2 (j,n) as an input, and senses an anomalous state of a generation mechanism of series data x n (t). In the present example embodiment, since d 2 (j,n) includes an index j related to time and a mechanism index n, the anomaly detecting unit  603  can detect an anomaly viewed from not only a time relationship but also a relationship of a generation mechanism. Note that an anomaly detecting method in the anomaly detecting unit  603  may be similar to that in the anomaly detecting unit  403  according to the fourth example embodiment. 
     Note that the anomaly detecting unit  603  may define j in such a way that j is related to not only i but also n, similarly to m. In this case, a format of a state feature amount series output by the state-feature-series generation unit  602  is d 2 (j), but a value that j can take only changes, and there is no change in that the state feature amount series can represent both a time relationship and a relationship among generation mechanisms. 
     Next, an operation in the present example embodiment is described.  FIG. 15  is a flowchart illustrating one example of an operation of the anomaly detecting device  600  according to the present example embodiment. Note that operations in a step S 601 - 1  to a step S 601 -N are similar to those in the step S 501 - 1  to the step S 501 -N according to the fifth example embodiment. 
     In the present example embodiment, when a state feature amount d(i,n) is calculated in analyzing processing of each piece of series data, the state-feature-series generation unit  602  generates the state feature amount series d 2 (j,n) from the state feature amount d(i,n) (step S 602 ). 
     Finally, the anomaly detecting unit  603  detects anomalies of the N numbers of series data x 1 (t), . . . , and x N (t), based on the state feature amount series d 2 (j,n) (step S 603 ). 
     As described above, the present example embodiment designates, as new series data (state feature amount series), a collection of state feature amounts based on a relationship among a plurality of generation mechanisms, in addition to a relationship of time such as a period of a change in a state of one generation mechanism, and detects an anomaly, based on the series data. Therefore, in addition to an advantageous effect in the fourth example embodiment, it is possible to detect an anomaly based on a relationship among generation mechanisms of input series data. In other words, based on the present example embodiment, it is possible to detect an anomaly viewed from a relationship among a plurality of generation mechanisms. 
     Note that, although a time-series acoustic signal is presented as an example of series data in the example embodiments described above, series data are not limited to a time-series acoustic signal. For example, series data may be any series data such as a time-series temperature signal acquired from a temperature sensor, a time-series vibration signal acquired from a vibration sensor, or a video signal acquired from a camera. Alternatively, series data may be time-series data of electric power usage amount, series data of electric power usage amount for each consumer, time-series data of traffic intensity in a network, time-series data of an air quantity, or spatial series data of precipitation amount in a certain range. Alternatively, series data may otherwise be angle series data, or discrete series data of text or the like. Naturally, series data include not only equally interval series data but also unequally interval series data. 
     Furthermore, the present invention may be applied to a system composed of a plurality of apparatuses, or may be applied to a single device. Moreover, the present invention is also applicable to a case where an information processing program which achieves a function according to an example embodiment is directly or remotely supplied to a system or a device. Therefore, in order to achieve a function according to the present invention in a computer, a program installed in a computer, or a medium storing the program, and a world wide web (WWW) server into which the program is downloaded also fall within the scope of the present invention. Particularly, at least a non-transitory computer readable medium storing a program for a computer to execute a processing step included in the example embodiments described above falls within the scope of the present invention. 
     [Hardware Configurations] 
     Hardware configurations of the above-described anomaly detecting devices  100  to  600  are described with reference to the anomaly detecting device  100 . 
     The anomaly detecting device  100  is configured as follows. For example, each component of the anomaly detecting device  100  may be configured by a hardware circuit. Moreover, in the anomaly detecting device  100 , each component may be configured by using a plurality of devices connected via a network. Further, in the anomaly detecting device  100 , a plurality of components may be configured by one piece of hardware. Still further, the anomaly detecting device  100  may be achieved as a computer device including a central processing unit (CPU), a read only memory (ROM), and a random access memory (RAM). The anomaly detecting device  100  may be achieved as a computer device further including, in addition to the above-described configuration, an input and/or output circuit (IOC) and a network interface circuit (NIC). 
       FIG. 16  is a block diagram illustrating one example of a configuration of an information processing device  700  being one example of hardware of the anomaly detecting device  100 . 
     The information processing device  700  includes a CPU  710 , a ROM  720 , a RAM  730 , an internal storage device  740 , an IOC  750 , and a NIC  780 , and configures a computer device. 
     The CPU  710  reads a program from the ROM  720 . Then, based on the read program, the CPU  710  controls the RAM  730 , the internal storage device  740 , the IOC  750 , and the NIC  780 . Then, a computer including the CPU  710  controls these components, and achieves functions as the series-feature extracting unit  101 , the series-probability-distribution calculating unit  102 , the state-feature calculating unit  104 , and the anomaly detecting unit  105 . 
     When achieving each function, the CPU  710  may use the RAM  730  or the internal storage device  740  as a temporary storage medium of a program. 
     Furthermore, the CPU  710  may read a program included in a recording medium  790  which computer-readably stores the program, by using a non-illustrated storage medium reading device. Alternatively, the CPU  710  may receive a program from a non-illustrated external device via the NIC  780 , saves the program in the RAM  730 , and then operate, based on the saved program. 
     The ROM  720  stores a program executed by the CPU  710 , and fixed data. The ROM  720  is, for example, a programmable-ROM (P-ROM) or a flash ROM. 
     The RAM  730  temporary stores a program executed by the CPU  710 , and data. The RAM  730  is, for example, a dynamic-RAM (D-RAM). 
     The internal storage device  740  stores data and a program saved by the information processing device  700  for a long period. The internal storage device  740  operates as the reference-probability-distribution storage unit  103 . Moreover, the internal storage device  740  may operate as a temporary storage device of the CPU  710 . The internal storage device  740  is, for example, a hard disk device, a magneto-optical disk, a solid state drive (SSD), or a disk array device. 
     Herein, the ROM  720  and the internal storage device  740  are non-transitory storage media. On the other hand, the RAM  730  is a transitory storage medium. Then, the CPU  710  is operable, based on a program stored in the ROM  720 , the internal storage device  740 , or the RAM  730 . In other words, the CPU  710  is operable by using a non-transitory storage medium or a transitory storage medium. 
     The IOC  750  mediates data between the CPU  710  and an input apparatus  760  as well as a display apparatus  770 . The IOC  750  is, for example, an IO interface card or a universal serial bus (USB) card. Moreover, the IOC  750  is not limited to a wire such as a USB, and may use wireless. 
     The input apparatus  760  is an apparatus which receives an input instruction from an operator of the information processing device  700 . The input apparatus  760  is, for example, a keyboard, a mouse, or a touch panel. 
     The display apparatus  770  is an apparatus which displays information to an operator of the information processing device  700 . The display apparatus  770  is, for example, a liquid crystal display. 
     The NIC  780  relays exchange of data with a non-illustrated external device via a network. The NIC  780  is, for example, a local area network (LAN) card. Moreover, the NIC  780  is not limited to a wire, and may use wireless. 
     The information processing device  700  configured in this way can acquire an advantageous effect similar to that of the anomaly detecting device  100 . 
     A reason for this is that the CPU  710  of the information processing device  700  can achieve a function similar to that of the anomaly detecting device  100 , based on a program. 
     The whole or part of the example embodiments disclosed above can be described as the following supplementary notes. 
     (Supplementary Note 1) 
     An anomaly detecting device includes: 
     a memory; and 
     at least one processor coupled to the memory, 
     the processor performing operations, the operations including: 
     when first series data are input, extracting a series feature amount being a feature amount of a signal included in the first series data; 
     calculating a series probability distribution being a probability distribution which the series feature amount follows; 
     storing a reference probability distribution being a probability distribution designated as a reference for the series feature amount in the first series data; 
     calculating a state feature amount representing a fluctuation condition of the series probability distribution with respect to the reference probability distribution; and 
     detecting an anomaly of the first series data, based on a plurality of the state feature amounts calculated from the first series data. 
     (Supplementary Note 2) 
     The anomaly detecting device according to supplementary note 1, 
     wherein the operations further includes 
     extracting, as the series feature amount, a predetermined feature amount of a signal at a time point of each time frame defined with a predetermined time unit, 
     calculating a probability distribution in which the series feature amount for each of the time frames is designated as a stochastic variable, 
     calculating, for each of the time frames, the state feature amount representing, by a predetermined method, a distance between the series probability distribution at a time point of the time frame and the reference probability distribution related to the time point of the time frame, and 
     detecting the anomaly, based on the state feature amount for each of the time frames calculated from the first series data. 
     (Supplementary Note 3) 
     The anomaly detecting device according to supplementary note 2, 
     wherein the operations further includes 
     designating, as second series data, data in which the state feature amount for each of the time frames calculated from the first series data is arranged in a time-series form, statistically processing the second series data, and then detecting the anomaly. 
     (Supplementary Note 4)
         The anomaly detecting device according to any one of supplementary notes 1 to 3,       

     wherein the operations further includes: 
     storing a normal model being a probability distribution of the state feature amount at a normal time in the first series data, and being a probability distribution of the state feature amount specified based on a model index signifying a time within at least a particular period, and 
     detecting the anomaly, based on the calculated state feature amount and the normal model. 
     (Supplementary Note 5) 
     The anomaly detecting device according to supplementary note 4, 
     wherein the operations further includes 
     detecting the anomaly, based on a score calculated based on a probability that the state feature amount occurs, the state feature amount indicated by the normal model related to a time point when the calculated state feature amount is acquired. 
     (Supplementary Note 6) 
     The anomaly detecting device according to supplementary note 2, 
     wherein the operations further includes: 
     reconstructing the state feature amount for each of the time frames defined with a predetermined time unit, according to a predetermined period, then generating one or more state feature amount series, 
     designating each of the state feature amount series as second series data, statistically processing the second series data, and then detecting the anomaly. 
     (Supplementary Note 7) 
     The anomaly detecting device according to any one of supplementary notes 1 to 6, 
     wherein a plurality of pieces of the first series data each representing a state of a different place in a target environment are input, 
     wherein the operations further includes 
     detecting, for each piece of the first series data, the anomaly, based on a plurality of the state feature amounts calculated from the piece of the first series data. 
     (Supplementary Note 8) 
     The anomaly detecting device according to supplementary note 7, 
     wherein the operations further includes 
     storing a probability distribution designated as a reference for the series feature amount in the predetermined first series data. 
     (Supplementary Note 9) 
     The anomaly detecting device according to supplementary note 1 or 2, 
     wherein a plurality of pieces of the first series data each representing a state of a different place in a target environment are input, 
     wherein the operations further includes 
     detecting, for each piece of the first series data, the anomaly, based on a plurality of the state feature amounts calculated from the piece of the first series data. 
     (Supplementary Note 10) 
     The anomaly detecting device according to supplementary note 9, 
     wherein the operations further includes: 
     storing a normal model being a probability distribution of the state feature amount at a normal time in each piece of the first series data, and being a probability distribution of the state feature amount specified based on a model index signifying a time within at least a particular period, and 
     detecting, for each piece of the first series data, the anomaly, based on the state feature amount calculated from the piece of the first series data, and the normal model related to the piece of the first series data. 
     (Supplementary Note 11) 
     The anomaly detecting device according to supplementary note 9, 
     wherein the operations further includes: 
     reconstructing, for each piece of the first series data, the state feature amount calculated from the piece of the first series data according to a predetermined period, then generating a series of one or more of the state feature amounts, 
     for each piece of the first series data, designating, as second series data, each of the series of the state feature amounts generated from a plurality of the state feature amounts calculated from the piece of the first series data, statistically processing the second series data, and then detecting the anomaly. 
     (Supplementary Note 12) 
     The anomaly detecting device according to any one of supplementary notes 1, 2, and 9 to 11, 
     wherein the operations further includes: 
     generating the reference probability distribution by using the first series data at a normal time. 
     (Supplementary Note 13) 
     The anomaly detecting device according to any one of supplementary notes 1, 2, and 9 to 12, 
     wherein the operations further includes: 
     generating a normal model by using a plurality of the state feature amounts calculated from the first series data at a normal time. 
     (Supplementary Note 14) 
     The anomaly detecting device according to any one of supplementary notes 1, 2, and 9 to 13, 
     wherein the first series data are time-series acoustic signals. 
     (Supplementary Note 15) 
     The anomaly detecting device according to any one of supplementary notes 1, 2, and 9 to 14, 
     wherein the series feature amount is a feature amount expressing a frequency and/or power of sound included in time-series acoustic signals, 
     expression forms of the series probability distribution and the reference probability distribution are a Gaussian mixture distribution, and 
     the state feature amount is a Kullback-Leibler (KL) divergence, a vector in which a predetermined number of the KL divergences are arranged, a vector in which a predetermined number of square distances of mean vectors of the Gaussian distributions in a predetermined rank are arranged, or a norm of each of the vectors. 
     (Supplementary Note 16) 
     An anomaly detecting method includes: 
     when first series data are input, extracting a series feature amount being a feature amount of a signal included in the first series data; 
     calculating a series probability distribution being a probability distribution which the series feature amount follows; 
     calculating a state feature amount representing a fluctuation condition of the series probability distribution with respect to a reference probability distribution being a probability distribution designated as a reference for the series feature amount in the first series data; and 
     detecting an anomaly of the first series data, based on a plurality of the state feature amounts calculated from the first series data. 
     (Supplementary Note 17) 
     The anomaly detecting method according to supplementary note 16, further includes: 
     extracting, as the series feature amount, a predetermined feature amount of a signal at a time point of each time frame defined with a predetermined time unit; 
     calculating a probability distribution in which the series feature amount for each of the time frames is designated as a stochastic variable; 
     calculating, for each of the time frames, the state feature amount representing, by a predetermined method, a distance between the series probability distribution at a time point of the time frame and the reference probability distribution related to a time point of the time frame; and 
     detecting the anomaly, based on the state feature amount for each of the time frames calculated from the first series data. 
     (Supplementary Note 18) 
     A non-transitory computer-readable recording medium embodying an anomaly detecting program, the anomaly detecting program causing a computer to perform a method, the method including: 
     when first series data are input, extracting a series feature amount being a feature amount of a signal included in the first series data; 
     calculating a series probability distribution being a probability distribution which the series feature amount follows; 
     calculating a state feature amount representing a fluctuation condition of the series probability distribution with respect to a reference probability distribution being a probability distribution designated as a reference for the series feature amount in the first series data; and 
     detecting an anomaly of the first series data, based on a plurality of the state feature amounts calculated from the first series data. 
     (Supplementary Note 19) 
     The recording medium according to supplementary note 18, embodying the anomaly detecting program for further causing a computer to perform the method, the method further including: 
     extracting, as the series feature amount, a predetermined feature amount of a signal at a time point of each time frame defined with a predetermined time unit; 
     calculating, a probability distribution in which the series feature amount for each of the time frames is designated as a stochastic variable; 
     calculating, for each of the time frames, the state feature amount representing, by a predetermined method, a distance between the series probability distribution at a time point of the time frame and the reference probability distribution related to the time point; and 
     detecting the anomaly, based on the state feature amount for each of the time frames calculated from the first series data. 
     While the invention has been particularly shown and described with reference to example embodiments thereof, the invention is not limited to these embodiments. It will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the claims. 
     This application is based upon and claims the benefit of priority from Japanese patent application No. 2016 175402, filed on Sep. 8, 2016, the disclosure of which is incorporated herein in its entirety by reference. 
     INDUSTRIAL APPLICABILITY 
     The present invention is suitably applicable to not only anomaly detection targeted for such an environment as to change in state even at a normal time, but also anomaly detection for any series data. 
     REFERENCE SIGNS LIST 
     
         
           100 ,  200 ,  300 ,  400 ,  500 ,  600  Anomaly detecting device 
           101 ,  201 ,  221 ,  511  Series-feature extracting unit 
           102  Series-probability-distribution calculating unit 
           202 ,  512  Probability-distribution calculating unit 
           222  Reference-probability distribution calculating unit 
           103 ,  203 ,  513  Reference-probability-distribution storage unit 
           104 ,  204 ,  514  State-feature calculating unit 
           105 ,  205 ,  303 ,  403 ,  503 ,  603  Anomaly detecting unit 
           210 ,  301 ,  311 ,  401 ,  501 ,  601  Series-data analyzing unit 
           302 ,  502  Normal-model storage unit 
           312  Normal-model calculating unit 
           402 ,  602  State-feature-series generation unit 
           510 ,  610  Distributed-data analyzing unit 
           700  Information processing device 
           710  CPU 
           720  ROM 
           730  RAM 
           740  Internal storage device 
           750  IOC 
           760  Input apparatus 
           770  Display apparatus 
           780  NIC 
           790  Recording medium