Time series data evaluation device, time series data evaluation program, and time series data evaluation method

Abstract

To accelerate processing speed with high accuracy.SOLUTION: A time-series data evaluation device comprises: a probability calculation unit 201 that calculates a probability p(i) of being ξt of Ai; a division entropy calculation unit 202 that sets a subdivided segment Bi(i=1,2,...,M*Q) by further dividing a divided segment Ai(i=1,2,...,M) into Q equivalent portions and calculates a division entropy by using a measure in the subdivided segment; and a total sum calculation unit 203 that performs a total sum calculation over a divided segment range for multiplication of the probability p(i) and the division entropy.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to a time series data evaluation device, a time series data evaluation program, and a time series data evaluation method. [Background technology]

[0002] Traditionally, when quantifying the degree of chaos in time series data, the competition has been on how best to create a curve that approximates the Lyapunov exponent. In other words, an index called the Lyapunov exponent has long been used as a means of measuring / quantifying chaos. Since the Lyapunov exponent is calculated based on the characteristics of the equation of the data generation source, if the data generation source (the equation that generates the data, etc.) is unknown, it is necessary to estimate it via a large amount of data and complicated procedures, and it is not easy to obtain. In particular, it is difficult, and practically impossible, to employ the Lyapunov exponent when performing real-time processing.

[0003] For the above Lyapunov exponents, the following method called the chaos measure is known. In the chaos measure, τ is a mapping defined on a line,

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[0004] Based on the above assumptions, the chaos scale H is calculated by the following definition (7) using the formula (8).

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[0005] In response to the chaos scale, the inventors proposed an index called a modified chaos scale, and attempted to quantify the degree of chaos in time series data (see Patent Document 1). The modified chaos scale will be explained below. Let τ be a mapping τ defined on a straight line.

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[0006] Division Section A i Subdivision interval B, which is further divided into Q equal parts (i=1,2,...,M) i (i=1,2,...,M×Q). i and subdivision B where actual data exists i The relationship between is shown.

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[0007] Here, as shown in FIG. 3, the division section A of the actual measurement i and the subdivision interval B of the actual measurement i , and for the subdivision interval, the measure of the measured interval is set to 1. i In the subdivision interval B of the measure i The ratio q(i,j) of i,j is calculated from c3(i,j), u3(i,j), and u2(i,j) below. This ratio q(i,j) is called the "norm ratio". c3(i,j)=#{ξ t ∈A i ,ξ t+1 ∈B j |t=0,1,2,···,n-1}···(10)

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[0008] Modified chaos scale H * is obtained as shown in equation (16) for the following definition equation (14).

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[0009] The formula for calculating the chaos scale H above and the modified chaos scale H *Comparing the formula for calculating the modified chaos scale H * The formula for calculating chaos scale H is as follows:

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[0010] Furthermore, the inventors have provided the methods described in Patent Document 2 and Patent Document 3 as methods for quantifying the degree of chaos in time series data. The method described in Patent Document 2 introduces subdivision intervals (M×Q divisions) in the same way as in the case of the modified chaos scale, and calculates the division entropy in units of subdivision. While Equation (17) is added in the case of the modified chaos scale, the method described in Patent Document 2 uses

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[0011] The method described in Patent Document 3 introduces a subdivision interval (M×Q division) in the same way as the modified chaos scale, and calculates the division entropy in units of subdivision. Furthermore, to make the data density constant and to correct the magnification rate after mapping, the average of the entropies in the cases of the outer measure and the inner measure is used. [Prior art documents] [Non-patent literature]

[0012] [Non-Patent Document 1] Masanori Ohya, Toshihide Hara, “Fundamentals of Mathematical Physics and Mathematical Information,” Kindaikagakusha, 2016 [Patent documents]

[0013] [Patent Document 1] Patent Publication No. 2021-64323 [Patent Document 2] Patent Publication No. 2021-64324 [Patent Document 3] Patent Publication No. 2022-7775 Summary of the Invention [Problem to be solved by the invention]

[0014] Although the above conventional methods could obtain a curve approximating the Lyapunov exponent and improve accuracy, it was necessary to increase the number of data and the number of divisions. The object of the present invention is to provide a time series data evaluation device, a time series data evaluation program, and a time series data evaluation method that can increase the processing speed with high accuracy and quantify the degree of chaos in real time for real time data. [Means for solving the problem]

[0015] The time series data evaluation device according to the embodiment of the present invention is a method for evaluating a time series data by using a mapping τ defined on a line.

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[0016] In the time series data evaluation device according to the embodiment of the present invention, the division entropy calculation unit is characterized by including an outer measure entropy calculation unit that calculates an outer measure entropy using the outer measure in the subdivision interval.

[0017] In the time series data evaluation device according to the embodiment of the present invention, the division entropy calculation unit is characterized by including an inner measure entropy calculation unit that calculates an inner measure entropy using the inner measure in the subdivision interval.

[0018] In the time-series data evaluation device according to the embodiment of the present invention, the divided entropy calculation unit uses an average value of the outer measure entropy and the inner measure entropy as a divided entropy for summation calculation.

[0019] In the time-series data evaluation device according to the embodiment of the present invention, i (i=1,2,...,M) is further divided into W equal parts. i For (i=1,2,...,M×W), the number of pre-mapping subdivision intervals in which real data exists is v i The actual data existence rate wi , w i =v i The division entropy calculation unit calculates the outer measure entropy and the inner measure entropy by the actual data existence rate w i The method is characterized in that the uncorrected measure entropy and the corrected inside measure entropy are generated by the above-mentioned method, and the average value of the uncorrected measure entropy and the corrected inside measure entropy is used as the division entropy in the summation calculation. [Brief description of the drawings]

[0020] [Figure 1] FIG. 13 is an explanatory diagram showing an example of the relationship between divided intervals and sub-divided intervals used in the embodiment of the present invention and interval numbers in a mapping. [Diagram 2] FIG. 2 is an explanatory diagram showing an example of the relationship between divided sections Ai and sub-divided sections Bi in which actual data exists, which are used in the embodiment of the present invention. [Diagram 3] FIG. 2 is an explanatory diagram showing an example of actual measurement division intervals Ai and actual measurement subdivision intervals Bi used in an embodiment of the present invention, and a case where the measure of the measured measurement interval for the subdivision intervals is set to 1. [Figure 4] 1 is a block diagram of a time-series data evaluation device according to an embodiment of the present invention. [Diagram 5] FIG. 5 is a block diagram of a calculation unit and the like that performs various calculations and the like realized by executing a time-series data evaluation program stored in an external storage device 104 in FIG. [Figure 6] An explanatory diagram of the distribution rate (entropy) of measures used in the chaos scale. [Figure 7] An explanatory diagram of the "norm expansion factor" used in Lyapunov exponents. [Figure 8] FIG. 13 is an explanatory diagram showing the magnification ratio by mapping when the measure of the measure interval for a subdivision interval is set to 1 in an embodiment of the present invention. [Figure 9] 11A and 11B are explanatory diagrams for a case where post-mapping correction using an outer measure is performed in an embodiment of the present invention. [Figure 10]11A and 11B are explanatory diagrams for a case where post-mapping correction using an inner measure is performed in the embodiment of the present invention. [Figure 11] 10A to 10C are diagrams for explaining an improvement in a method for estimating the enlargement rate of a section before mapping in an embodiment of the present invention. [Figure 12] FIG. 13 is a graph showing the change in evaluation value, which is the calculation result when the high-precision chaos scaling function H'' according to the present embodiment is used and when the Lyapunov exponent is used, in which the graph shows the case where the number of divisions M=8, the number of subdivisions Q=6, the number of pre-mapping subdivisions W=6, and the number of data n=1000. [Figure 13] FIG. 11 is a graph showing the change in evaluation value, which is the calculation result when the high-precision chaos scaling function H'' according to the present embodiment is used and when the Lyapunov exponent is used, in which the graph shows the case where the number of divisions M=10, the number of subdivisions Q=8, the number of pre-mapping subdivisions W=8, and the number of data n=2000. [Figure 14] FIG. 11 is a graph showing the change in evaluation value, which is the calculation result when the high-precision chaos scaling function H'' according to the present embodiment is used and when the Lyapunov exponent is used, in which the graph shows the case where the number of divisions M=16, the number of subdivisions Q=16, the number of pre-mapping subdivisions W=16, and the number of data n=10000. [Figure 15] FIG. 13 is a graph showing the change in evaluation value, which is the calculation result when the high-precision chaos scaling function H'' according to the present embodiment is used and when the Lyapunov exponent is used, in which the graph shows the case where the number of divisions M=32, the number of subdivisions Q=32, the number of pre-mapping subdivisions W=32, and the number of data n=50000. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] A time series data evaluation device, a time series data evaluation program, and a time series data evaluation method according to an embodiment of the present invention will be described below with reference to the accompanying drawings. In each drawing, the same components are given the same reference numerals and duplicated explanations will be omitted. FIG. 4 shows a block diagram of a time series data evaluation device 100 according to an embodiment. The time series data evaluation device 100 can be configured by a cloud computer, a server computer, a personal computer, or other computers.

[0022] In the time series data evaluation device 100, a CPU 101 performs calculations based on programs and data in a main memory 102. An external storage device 104 is connected to the CPU 101 via a bus 103, and a time series data evaluation program is stored in the external storage device 104. The CPU 101 reads the time series data evaluation program from the external storage device 104 into the main memory 102 and executes the program, thereby functioning as the time series data evaluation device 100, and during this operation, a time series data evaluation method is executed.

[0023] In addition to the external storage device 104, a time series data supply unit 105 is connected to the bus 103. The time series data supply unit 105 can be configured to receive and hold time series data in real time from an external sensor or the like, or can be configured to receive and hold time series data collected by some external device or the like by performing mapping calculations. Furthermore, the time series data supply unit 105 may be configured to hold and supply time series data by setting a medium storing collected time series data. Furthermore, the time series data supply unit 105 may be configured to have all of the above-mentioned components. In any case, when the CPU 101 executes a time series data evaluation program to evaluate the time series data, the time series data is supplied from the time series data supply unit 105.

[0024] A result output unit 106 is connected to the bus 103. The result output unit 106 can be a device that outputs the results of processing in the time-series data evaluation device 100, such as a display device or a printer. The result output unit 106 may also be a medium that stores the results of processing in the time-series data evaluation device 100, and may further be a device that transmits the processing results to a requester (client) of the processing via a line or the like.

[0025] 5. That is, as shown in Fig. 5, the time series data evaluation device 100 includes a probability calculation unit 201, a divided entropy calculation unit 202, a sum calculation unit 203, and an actual data existence rate calculation unit 204. The divided entropy calculation unit 202 includes an outer measure entropy calculation unit 202A and an inner measure entropy calculation unit 202B.

[0026] In the time series data evaluation device 100 of this embodiment, as described below, by comparing the conventionally known chaos scale and modified chaos scale with the Lyapunov exponent, it was concluded that approaching the viewpoint of the Lyapunov exponent would enable high-precision and high-speed processing, and would lead to quantifying the degree of chaos in real time for real-time data. Below, the theory of the process of arriving at this conclusion and the calculation unit and the like that perform each calculation and the like that are provided in the time series data evaluation device 100 of this embodiment will be described.

[0027] The inventors of the present application have studied the chaos measure and Lyapunov exponent described above and found that the chaos measure uses the distribution rate of the measure (entropy) as shown in FIG.

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[0028] On the other hand, in the case of Lyapunov exponents, the "norm expansion factor" is used as shown in Figure 7. In other words,

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[0029] The above-mentioned change in perspective from "measure distribution rate to norm expansion rate" is realized by dividing the divided interval into subdivision intervals, as shown in Figure 8, and calculating the "sum value / Q" as the expansion rate by mapping in the interval in which the measure of the measured interval of the subdivision interval is changed to 1.

[0030] In this embodiment, the chaos scale H and the modified chaos scale H * By further modifying the above, we obtain a time series data evaluation device that can achieve high accuracy and high processing speed. Therefore, the definition of the chaos scale is shown in formula (7), and the definition of the modified chaos scale is shown in formula (14), and these are compared.

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[0031] In order to obtain a high-precision chaos scale function H'', in this embodiment, as shown in FIG. 9, the division interval is divided into subdivision intervals, the measure of the measured interval of the subdivision interval is changed to 1, and post-mapping correction using the outer measure is performed. i In the outer measure subdivision interval B i The proportion of O (i,j) is the following u 3O (i,j),u 2O (i,j),u 1O (i,j),p´ O Calculate from (i,j).

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[0032] The above q´ O (i,j), p´ O Using (i,j), the entropy of the outer measure h′ O To find (i), we get

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[0033] In this embodiment, as shown in FIG. 10, the divided interval is divided into subdivision intervals, the measure of the measured interval of the subdivision interval is changed to 1, and post-mapping correction using the inner measure is performed. i In the subdivision interval B of the inner measure i The proportion of q´ I (i,j) is the following u 3I (i,j),u 2I (i,j),u 1I (i,j),p´ I Calculate from (i,j).

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[0034] The above q´ I (i,j), p´ I Using (i,j), the entropy of the inner measure h′ I To find (i), we get

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[0035] Entropy of outer measure h′ O (i) is

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[0036] Entropy of the inner measure h′ I (i) is

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[0037] In this embodiment, the entropy h′ of the outer measure O (i) and the entropy of the inner measure h′ I Let the average of (i) be the new entropy h′(i),

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[0038] Furthermore, the high-precision chaos scale function H′ obtained only by post-mapping correction is

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[0039] In this embodiment, the method of estimating the enlargement ratio of the pre-mapping section is improved. As shown in FIG. 11(a), the division number M (here, 8) of the division section A i The pre-mapping subdivision interval C is divided into W (here, 8) equal parts. i The expanded pre-mapping interval is shown in Figure 11(b). That is, the interval I is

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[0040] In the above, the number of "pre-mapping subdivision intervals" in which real data exists is v i Let the actual data presence rate in the divided section be w i Then, the actual data existence rate w i =v i / W. This actual data existence rate w i Divide into section A i If we calculate the proportion w(i) of the pre-mapping subdivision interval of the measure in i, we get d1(i)=#{ξ t ∈C i |t=0,1,2,···,n-1}···(2)

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[0041] In this embodiment, as described above, the divided section A i (i=1,2,...,M) is further divided into W equal parts. i For (i=1,2,...,M×W), the number of pre-mapping subdivision intervals in which real data exists is v i The actual data existence rate w i , w i =v i The present embodiment is provided with an actual data existence rate calculation unit 204 that calculates the actual data existence rate by using the above-mentioned method.

[0042] The entropy of equations (33) and (34) above

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[0043] By correcting equations (45) and (46) using the above w(i), we get

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[0044] From the above, the sum (average) of the entropy after the mapping and the entropy before the mapping, h′′(i), is

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[0045] In the time-series data evaluation device of this embodiment, the division entropy calculation unit 202 finally obtains the high-precision chaos scale function H'' as follows.

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[0046] Changes in evaluation values, which are the calculation results when using the high-precision chaos scale function H'' according to this embodiment, which performs the calculation as described above, and when using the Lyapunov exponent, are shown in Figs. 12 to 14. In both cases, the logistic map (a=3.5 to 4.0) is used. Fig. 12 shows the case where the number of divisions M=8, the number of subdivisions Q=6, the number of subdivisions before mapping W=6, and the number of data n=1000. Fig. 13 shows the case where the number of divisions M=10, the number of subdivisions Q=8, the number of subdivisions before mapping W=8, and the number of data n=2000. Fig. 14 shows the case where the number of divisions M=16, the number of subdivisions Q=16, the number of subdivisions before mapping W=16, and the number of data n=10000. Fig. 15 shows the case where the number of divisions M=32, the number of subdivisions Q=32, the number of subdivisions before mapping W=32, and the number of data n=50000.

[0047] In this embodiment, even if the number of divisions and the number of data are small, an evaluation value extremely close to the Lyapunov exponent can be obtained. That is, in the method described in Patent Document 3, when the number of divisions is 80 and the number of data is 10,000,000, or when the number of divisions is 320 and the number of data is 10,000,000, the evaluation value finally approaches the Lyapunov exponent, whereas in this embodiment, it was confirmed that the number of divisions is a single digit number and the number of data is about 1,000, and the evaluation value sufficiently approximates the Lyapunov exponent. This tendency was also able to improve accuracy in the range of mapping where the Lyapunov exponent takes a small value (about a=3.6).

[0048] Unlike the invention according to the present application, the method described in Patent Document 3 does not perform measure correction before correction. The method described in Patent Document 3 also uses a measure correction in which the measure of the divided section is converted to 1 when it has a measure and 0 otherwise, and uses a measure-corrected outer measure and a measure-corrected inner measure.

[0049] In the method of this embodiment, in which only measure transformation after mapping is applied, Outer measure entropy h'' obtained by calculation using the corrected outer measure O and, The inner measure entropy h'' obtained by calculation using the corrected inner measure I and Using these average values, we set the new entropy h′′(i) The aim is to obtain a high-precision chaos scale function H''.

[0050] In the method described in Patent Document 3, The external measurement data evaluation value H is calculated by using the corrected external measurement as a measurement. ★ out Calculate The corrected inner measure is used as the measure to calculate the inner measure data evaluation value H ★ inn Calculate The average of these values ​​is the final evaluation value H ★ This evaluation value H ★ The calculation formula for is written in the format adopted in this embodiment as follows:

[0051]

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[0052] As described above, the final evaluation value H ★ The formula using the high-precision chaos scale function H' by only post-mapping correction described in formula (36) is clearly different from the formula of this embodiment, even though the measure correction before correction is not added. And, this embodiment can obtain an evaluation value that is very close to the Lyapunov exponent even if the number of divisions and the number of data are small.

[0053] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims. [Explanation of symbols]

[0054] 100 Time series data evaluation device 101 CPU 102 Main memory 103 Bus 104 External storage device 104A Time series data evaluation program 105 Time series data supply unit 106 Result output unit 201 Probability calculation unit 202 Division entropy calculation unit 202A outer measure entropy calculation unit 202B inner measure entropy calculation unit 203 summation calculation unit 204 real data existence rate calculation unit< / h>

Claims

1. Let τ be a mapping τ defined on a line. [0010] Let the initial value ξ 0 For ∈I, ξ t = τ(ξ t-1 ) = τ t (ξ 0 ), t=1, 2,..., n A total of n+1 time series data obtained by n iterations of {ξ 0 , ξ 1 , ξ 2 , ..., ξ n} Then, ξ t The section I including the above is divided into M equal sections, and the divided sections are called A. i When expressed as (i=1, 2, ..., M), the division section satisfies the following formula (1): [0025] c 1 Let (i) and p(i) be the following equations (2) and (4), c 1 (i) = # {ξ t ∈A i |t=0,1,2,...,n-1}...(2) [0030] ξ t ∈A i A probability calculation unit that calculates the probability p(i) that The divided section A i (i=1, 2, ..., M) is further divided into Q equal parts i a division entropy calculation unit that sets (i=1, 2, . . . , M×Q) and calculates the division entropy using a measure in this subdivision interval; a summation calculation unit that performs a summation calculation over the range of the divided interval for multiplication of the probability p(i) and the divided entropy; A time series data evaluation device comprising:

2. 2. The time series data evaluation device according to claim 1, wherein the division entropy calculation unit includes an outer measure entropy calculation unit that calculates an outer measure entropy by using an outer measure in the subdivision interval.

3. 3. The time-series data evaluation device according to claim 2, wherein the division entropy calculation unit includes an inner measure entropy calculation unit that calculates an inner measure entropy by using an inner measure in the subdivision interval.

4. 4. The time series data evaluation device according to claim 3, wherein the divided entropy calculation unit uses an average value of the outer measure entropy and the inner measure entropy as a divided entropy for summation calculation.

5. The divided section A i (i = 1, 2, ..., M) is further divided into W equal parts to form a pre-mapping subdivision interval C i For (i = 1, 2, ..., M × W), the number of pre-mapping subdivision intervals in which real data exists is v i The actual data existence rate w i Wow i = v i / W, The division entropy calculation unit calculates the outer measure entropy and the inner measure entropy by the actual data existence rate w i 5. The time series data evaluation device according to claim 4, wherein an average value of the uncorrected measure entropy and the corrected measure entropy is used as a division entropy for summation calculation.

6. Computer, Let τ be a mapping τ defined on a line. [0045] Let the initial value ξ 0 For ∈I, ξ t = τ(ξ t-1 ) = τ t (ξ 0), t=1, 2,..., n A total of n+1 time series data obtained by n iterations of {ξ 0 , ξ 1 , ξ 2 , ..., ξ n} Then, ξ t The section I including the above is divided into M equal sections, and the divided sections are called A. i When expressed as (i=1, 2, ..., M), the division section satisfies the following formula (1): [0050] c 1 Let (i) and p(i) be the following equations (2) and (4), c 1 (i) = # {ξ t ∈A i |t=0,1,2,...,n-1}...(2) [006] ξ t ∈A i A probability calculation unit that calculates the probability p(i) that The divided section A i (i = 1, 2, ..., M) is further divided into Q equal parts i a division entropy calculation unit that sets (i=1, 2, ..., M x Q) and calculates the division entropy using the measure in this subdivision interval; a summation calculation unit that performs a summation calculation over the range of the divided interval for the multiplication of the probability p(i) and the divided entropy; A time series data evaluation program characterized by causing the program to function as follows.

7. The time series data evaluation program according to claim 6, characterized in that the computer functioning as the division entropy calculation unit functions as an outer measure entropy calculation unit that calculates an outer measure entropy using an outer measure in the subdivision interval.

8. The time series data evaluation program according to claim 7, characterized in that the computer functioning as the division entropy calculation unit is made to function as an inner measure entropy calculation unit that calculates an inner measure entropy using the inner measures in the subdivision intervals.

9. The time series data evaluation program according to claim 8, characterized in that the computer functioning as the split entropy calculation unit is caused to function so as to use an average value of the outer measure entropy and the inner measure entropy as a split entropy for summation calculation.

10. The computer further comprises: The divided section A i (i = 1, 2, ..., M) is further divided into W equal parts to form a pre-mapping subdivision interval C i For (i = 1, 2, ..., M × W), the number of pre-mapping subdivision intervals in which real data exists is v i The actual data existence rate w i Wow i = v i / W to function as an actual data existence rate calculation unit, The computer is used as the division entropy calculation unit, and the outer measure entropy and the inner measure entropy are calculated based on the actual data existence rate w i 10. The time series data evaluation program according to claim 9, characterized in that the program functions to create a corrected outside measure entropy and a corrected inside measure entropy by the above method, and to use an average value of the outside measure entropy and the corrected inside measure entropy as a division entropy for summation calculation.

11. Let τ be a mapping defined on a line. [0070] Let the initial value ξ 0 For ∈I, ξ t = τ(ξ t-1 ) = τ t (ξ 0 ), t=1, 2,..., n A total of n+1 time series data obtained by n iterations of {ξ 0 , ξ 1 , ξ 2 , ..., ξ n} Then, ξ t The section I including the above is divided into M equal sections, and the divided sections are called A. i When expressed as (i=1, 2, ..., M), the division section satisfies the following formula (1): [0080] c 1 Let (i) and p(i) be the following equations (2) and (4), c 1 (i) = # {ξ t ∈A i |t=0,1,2,...,n-1}...(2) [0097] ξ t ∈A i Calculate the probability p(i) that The divided section A i (i = 1, 2, ..., M) is further divided into Q equal parts i (i=1, 2, ..., M x Q), and calculate the division entropy using the measure in this subdivision interval. A summation operation is performed for the range of the divided interval for multiplying the probability p(i) and the divided entropy. A time series data evaluation method comprising:

12. 12. The time series data evaluation method according to claim 11, wherein in the calculation of the division entropy, an outer measure entropy is calculated using an outer measure in the subdivision interval.

13. 13. The time-series data evaluation method according to claim 12, wherein in the calculation of the division entropy, an inner measure entropy is calculated using an inner measure in the subdivision interval.

14. 14. The time series data evaluation method according to claim 13, wherein in the calculation of the partitioned entropy, an average value of the outer measure entropy and the inner measure entropy is used as the partitioned entropy for a summation operation.

15. The divided section A i (i = 1, 2, ..., M) is further divided into W equal parts to form a pre-mapping subdivision interval C i For (i = 1, 2, ..., M × W), the number of pre-mapping subdivision intervals in which real data exists is v i The actual data existence rate w i Wow i = v i / W, In calculating the division entropy, the outer measure entropy and the inner measure entropy are calculated by dividing the actual data existence rate w i The method for evaluating time series data according to claim 14, characterized in that an average value of the uncorrected measure entropy and the corrected measure entropy is used as a division entropy for summation calculation.

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