WO2022125047A1 - Classification and optimization system on time-series - Google Patents
Classification and optimization system on time-series Download PDFInfo
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- WO2022125047A1 WO2022125047A1 PCT/TR2021/051377 TR2021051377W WO2022125047A1 WO 2022125047 A1 WO2022125047 A1 WO 2022125047A1 TR 2021051377 W TR2021051377 W TR 2021051377W WO 2022125047 A1 WO2022125047 A1 WO 2022125047A1
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- 238000005457 optimization Methods 0.000 title claims abstract description 32
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 11
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims description 14
- 238000007635 classification algorithm Methods 0.000 claims description 2
- 238000010801 machine learning Methods 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/906—Clustering; Classification
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Abstract
The present invention relates to a system (1) for classification and optimization of time-series data by using artificial intelligence algorithms.
Description
CLASSIFICATION AND OPTIMIZATION SYSTEM ON TIME-SERIES
Technical Field
The present invention relates to a system for classification and optimization of timeseries data by using artificial intelligence algorithms.
Background of the Invention
Optimization is a standard transaction in automated time-series estimation systems being used today. However, time and resource are needed in an optimization transaction. Cost of performing optimization individually in a system running a plurality of time-series is high.
Considering the studies in the state of the art, it is understood that there is need for a system which classifies artificial intelligence algorithms of time-series patterns (unsupervised learning) and carries out optimization transaction by selecting one time-series from each class.
The United States patent document no. US2019317952, an application in the state of the art, discloses a system for clustering and improving data by analyzing time data generally. The said invention discloses carrying out the analysis of data hierarchically so as to generate more accurate predictions. The time data mentioned in the invention represent one or more time-series. A flow enabling to generate a machine learning model so as to make the prediction mentioned in the said invention accurately, is disclosed. The machine learning model is a mathematical artificial intelligence model that can learn from, categorize data and make predictions about data. The machine model learning model can classify input data among two or more classes by analyzing. It is enabled to predict a result based on
input data and to identify patterns or trends in input data; identify a distribution of input data by identifying patterns and/or trends in input data. In embodiments of the current invention, a hierarchical analysis is generated automatically and it can be a recommended hierarchy. A recommended hierarchy enables to provide the best possible result with the attributes required to generate results that are optimized more accurately. Using a hierarchy optimized over a reference enables to obtain results providing high accuracy.
Summary of the Invention
An objective of the present invention is to realize a system which enables to make prediction more quickly, more cost-efficiently and more accurately without needing a separate optimization transaction for each time-series.
Detailed Description of the Invention
“Classification and Optimization System on Time-Series” realized to fulfil the objective of the present invention is shown in the figure attached, in which:
Figure l is a schematic view of the inventive system.
The components illustrated in the figure are individually numbered, where the numbers refer to the following:
1. System
2. Database
3. Classification server
4. Optimization server
5. Action server
The inventive system (1) for classification and optimization of time-senes data by using artificial intelligence algorithms comprises: at least one database (2) which is configured to store the data about timeseries; at least one classification server (3) which is configured to receive the data about time-series from the database (2) and then to classify these data by artificial intelligence algorithms, and to transmit the classification information to the database (2); at least one optimization server (4) which is configured to receive classification information from the database (2) and then to determine a method used for optimizing the information; at least one action server (5) which is configured to receive the optimization method determined by the optimization server (4) and the parameters about the time-series from the database (2), and to apply the determined method to the time-series in the related class.
The database (2) included in the inventive system (1) is configured to store data about time-series.
The classification server (3) included in the inventive system (1) is configured to receive the time-series data kept in the database (2) and then to classify these data by pre-determined unsupervised artificial intelligence algorithms. The classification server (3) is configured to transmit the outputs about the classification realized by it to the database (2).
The optimization server (4) included in the inventive system (1) is configured to receive the information about the related time-series from the database (2) by selecting a time-series from each class obtained by the classification server (3). In a preferred embodiment of the invention, the optimization server (4) is configured to find the optimum methods and parameters of the value of a class number via
Bayesian optimization by using the silhouette score of a classification algorithm on the received data and to transmit them to the database (2).
The action server (5) included in the inventive system (1) is configured to receive the classification data, the time-series data and the optimization methods and parameters of each class calculated, from the database (2). The action server (5) is configured to apply the optimum methods and parameters determined with respect to a class, to all time-series included in there related class. The action server (5) is configured to transmit the results of the optimization applied to the time-series, to the database (2).
In the inventive system (1), the database (2) stores the data about the time-series on it at first. The classification server (3) carries out a classification transaction by accessing these data and records these data in the database (2). The optimization server (4) receives the information about the related time-series by selecting at least one time-series from each class. It determines the methods and parameters related to the optimization with the received information. The action server (5) applies the optimum methods and parameters, that are determined to belong to a class, to all time-series in the related class and transmits them to the database (2). In different embodiments of the invention, each server (3, 4, 5) can store its outputs in different databases (2).
With the present invention, classification of artificial intelligence algorithms of time-series patterns (unsupervised learning) and optimization transactions can be carried out by selecting one time-series from each class.
Within these basic concepts; it is possible to develop various embodiments of the inventive system (1); the invention cannot be limited to examples disclosed herein and it is essentially according to claims.
Claims
CLAIMS A system (1) for classification and optimization of time-series data by using artificial intelligence algorithms; characterized by at least one database (2) which is configured to store the data about timeseries; at least one classification server (3) which is configured to receive the data about time-series from the database (2) and then to classify these data by artificial intelligence algorithms, and to transmit the classification information to the database (2); at least one optimization server (4) which is configured to receive classification information from the database (2) and then to determine a method used for optimizing the information; at least one action server (5) which is configured to receive the optimization method determined by the optimization server (4) and the parameters about the time-series from the database (2), and to apply the determined method to the time-series in the related class. A system (1) according to Claim 1 ; characterized by the database (2) which is configured to store the data about time-series. A system (1) according to Claim 1 or 2; characterized by the classification server (3) which is configured to receive the time-series data kept in the database (2) and then to classify these data by pre-determined unsupervised artificial intelligence algorithms. A system (1) according to any of the preceding claims; characterized by the classification server (3) which is configured to transmit the outputs about the classification realized by it to the database (2).
5
A system (1) according to any of the preceding claims; characterized by the optimization server (4) which is configured to receive the information about the related time-series from the database (2) by selecting a time-series from each class obtained by the classification server (3). A system (1) according to any of the preceding claims; characterized by the optimization server (4) which is configured to find the optimum methods and parameters of the value of a class number via Bayesian optimization by using the silhouette score of a classification algorithm on the received data and to transmit them to the database (2). A system (1) according to any of the preceding claims; characterized by the action server (5) which is configured to receive the classification data, the time-series data and the optimization methods and parameters of each class calculated, from the database (2). A system (1) according to any of the preceding claims; characterized by the action server (5) which is configured to apply the optimum methods and parameters determined with respect to a class, to all time-series included in there related class. A system (1) according to any of the preceding claims; characterized by the action server (5) which is configured to transmit the results of the optimization applied to the time-series, to the database (2).
6
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| TR2020/20136A TR202020136A2 (en) | 2020-12-09 | 2020-12-09 | CLASSIFICATION AND OPTIMIZATION SYSTEM ON TIME SERIES |
| TR2020/20136 | 2020-12-09 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| WO2022125047A1 true WO2022125047A1 (en) | 2022-06-16 |
Family
ID=76373809
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/TR2021/051377 WO2022125047A1 (en) | 2020-12-09 | 2021-12-08 | Classification and optimization system on time-series |
Country Status (2)
| Country | Link |
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| TR (1) | TR202020136A2 (en) |
| WO (1) | WO2022125047A1 (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170329660A1 (en) * | 2016-05-16 | 2017-11-16 | Oracle International Corporation | Correlation-based analytic for time-series data |
| JP2018205994A (en) * | 2017-06-01 | 2018-12-27 | 株式会社東芝 | Time series data analysis device, time series data analysis method, and computer program |
| US20190079846A1 (en) * | 2017-09-08 | 2019-03-14 | Performance Sherpa, Inc. | Application performance control system for real time monitoring and control of distributed data processing applications |
| CN110633741A (en) * | 2019-09-05 | 2019-12-31 | 河海大学常州校区 | Time sequence classification method based on improved impulse neural network |
| KR102091529B1 (en) * | 2019-09-03 | 2020-03-23 | (주)빅인사이트 | Method and apparatus for training AI model using user's time series behavior data |
| US20200250027A1 (en) * | 2019-02-01 | 2020-08-06 | EMC IP Holding Company LLC | Time series forecasting classification |
-
2020
- 2020-12-09 TR TR2020/20136A patent/TR202020136A2/en unknown
-
2021
- 2021-12-08 WO PCT/TR2021/051377 patent/WO2022125047A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170329660A1 (en) * | 2016-05-16 | 2017-11-16 | Oracle International Corporation | Correlation-based analytic for time-series data |
| JP2018205994A (en) * | 2017-06-01 | 2018-12-27 | 株式会社東芝 | Time series data analysis device, time series data analysis method, and computer program |
| US20190079846A1 (en) * | 2017-09-08 | 2019-03-14 | Performance Sherpa, Inc. | Application performance control system for real time monitoring and control of distributed data processing applications |
| US20200250027A1 (en) * | 2019-02-01 | 2020-08-06 | EMC IP Holding Company LLC | Time series forecasting classification |
| KR102091529B1 (en) * | 2019-09-03 | 2020-03-23 | (주)빅인사이트 | Method and apparatus for training AI model using user's time series behavior data |
| CN110633741A (en) * | 2019-09-05 | 2019-12-31 | 河海大学常州校区 | Time sequence classification method based on improved impulse neural network |
Also Published As
| Publication number | Publication date |
|---|---|
| TR202020136A2 (en) | 2021-03-22 |
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