Method and device for efficiently processing time series data
By using columnar storage and virtual table mapping to process time-series data, the problems of wasted storage space and reduced query speed in existing technologies are solved, achieving efficient time-series data processing and trend prediction, and optimizing user experience and data access efficiency.
Patent Information
- Application Number
- CN202410069598.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-01-17
- Publication Date
- 2026-02-03
AI Technical Summary
Existing NoSQL and relational databases suffer from problems such as wasted storage space, reduced query speed, and high deployment and maintenance costs when processing large-scale time-series data. They fail to effectively utilize the high-dimensional and high-frequency characteristics of time-series data, thus affecting processing efficiency.
The time-series data is compressed using columnar storage and stored in a pre-built time-series table. High-speed loading is achieved through a time-series fast loader built using virtual table mapping and the BulkLoad function. The data is recorded and analyzed in conjunction with relational and time-series databases.
It reduces storage space and costs, improves access efficiency and query speed for time-series data, optimizes user experience, and enhances data processing efficiency and reliability through trend prediction using analytical and statistical functions.
Smart Images
Figure CN121456027A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of computer, in particular to a method and device for efficient processing of time series data. BACKGROUND
[0002] With the rapid development of Internet of Things and cloud computing, the generation and accumulation of time series data grow exponentially, how to handle large-scale time series data, the existing methods are mainly divided into the following two kinds:
[0003] (1) using NoSQL database, using the good expansion ability, high speed read-write performance of NoSQL database and special time series database TSDB with time series data storage and query function, realizing the processing of large-scale time series data;
[0004] (2) using relational database, using table form to store time series data in relational database, and using SQL language to query and analyze time series data, so as to realize the processing of large-scale time series data.
[0005] Although the above two ways can realize the processing of large-scale time series data to some extent, there are still some problems and limitations, such as the deployment and maintenance cost of time series database in NoSQL database is high, there is a problem of waste of storage space or loss of time series data, and the relational database does not consider the characteristics of high latitude and high frequency of time series data, there is a problem of waste of storage space and decline of query speed, which affects the processing efficiency of time series data. SUMMARY
[0006] The technical problem solved by the present application is to provide a method and device for efficient processing of time series data, realizing efficient processing of time series data and improving processing efficiency.
[0007] In order to solve the above technical problems, the technical scheme adopted by the present application is:
[0008] In the first aspect, the present application provides a method for efficient processing of time series data, comprising:
[0009] real-time acquisition of time series data, obtaining the data attribute and timestamp of the time series data, storing the time series data with the same data attribute according to the corresponding timestamp in the form of column storage to the pre-constructed time series table, and compressing each row of time series data in the time series table to obtain the compressed time series table;
[0010] Map the compressed time series table to a virtual table to obtain a mapped virtual table, receive a time series data access request, obtain time series request data corresponding to the time series data access request from the mapped virtual table, and implement high-speed loading of the time series request data through a time series fast loader, wherein the time series loader is constructed based on a BulkLoad function.
[0011] The application has the advantages that the time series data of the time series table is compressed to reduce storage space and storage cost, the time series data in the time series table is stored in a columnar storage manner according to corresponding timestamps, the compression efficiency of the time series data is improved, the corresponding time series request data is obtained from the virtual table when loading the time series request data corresponding to the time series data access request, the user can access the time series request data in the SQL manner, the user experience is optimized, the time series fast loader constructed based on the BulkLoad function is used to implement high-speed loading of the time series request data, the access efficiency of the time series data is improved, and efficient processing of the time series data is realized.
[0012] Optionally, the real-time collection of the time series data comprises:
[0013] The time series data is preprocessed to obtain preprocessed time series data, wherein the preprocessing comprises missing value processing and smoothing processing.
[0014] According to the above description, the real-time collected time series data is subjected to missing value processing and smoothing processing, and the completeness and effectiveness of the time series data are improved.
[0015] Optionally, the preprocessed time series data is obtained by:
[0016] The collection frequency is obtained, a collection calendar is created according to the collection frequency, the preprocessed time series data is recorded in the collection calendar to obtain a recorded collection calendar.
[0017] The recorded collection calendar is stored in a dual-engine database, and the dual-engine database is a relational database and a time series database.
[0018] According to the above description, the preprocessed time series data is recorded in the collection calendar created according to the collection frequency, the completeness of the time series data is ensured, and the recorded collection calendar is stored not only in the relational database but also in the time series database, and the completeness and reliability of the time series data are further ensured.
[0019] Optionally, the compressed time series table is obtained by:
[0020] The compressed time series table is stored into a time series container, and the time series data in the compressed time series table is stored into a disk in a cluster-ordered manner through the time series container.
[0021] According to the above description, the time series table is stored in the time series container, that is, the time series container is a container for containing one or more time series data structures, so as to realize the storage of the time series data in a cluster-ordered manner on the disk and improve the efficiency of storage.
[0022] Optionally, the method further comprises:
[0023] The time series data is analyzed and processed through an analysis function to obtain an analysis result, and is statistically processed through a statistical function to obtain a statistical result, the time series data is predicted in a change trend according to the analysis result and the statistical result to obtain a change trend prediction result, and the change trend prediction result is displayed in a visual form.
[0024] The analysis function comprises a Clip function, a TSToXML function, a DelRange function and a DelTrim function, and the statistical function comprises a TSRunningAvg function and a TSRunningSum.
[0025] According to the above description, the time series data is analyzed and processed through a plurality of analysis functions and a plurality of statistical functions, respectively, the time series data is predicted in a change trend according to the obtained analysis result and statistical result, timely warning of the problem is realized, and the problem is displayed in a visual form, thereby optimizing the user experience.
[0026] In a second aspect, the application provides a device for efficient processing of time series data, comprising:
[0027] The acquisition module is configured to acquire time series data in real time, obtain data attributes and time stamps of the time series data, store time series data of the same data attribute in a columnar storage manner according to corresponding time stamps in a pre-constructed time series table, and compress each row of time series data in the time series table to obtain a compressed time series table.
[0028] The loading module is configured to map the compressed time series table into a virtual table to obtain a mapped virtual table, receive a time series data access request, obtain time series request data corresponding to the time series data access request from the mapped virtual table, and realize high-speed loading of the time series request data through a time series fast loader, wherein the time series loader is constructed based on a BulkLoad function.
[0029] The application has the beneficial effects that the time sequence data of the time sequence table is compressed, the storage space and cost are reduced, the time sequence data in the time sequence table is stored in a columnar storage mode according to the corresponding time stamp according to the data attribute of the time sequence data, the compression efficiency of the time sequence data is improved, when the corresponding time sequence request data is accessed according to the time sequence data access request, the corresponding time sequence request data is obtained from the virtual table, the user can access the time sequence request data in the SQL mode, the user experience is optimized, the high-speed loading of the time sequence request data is realized through the time sequence fast loader constructed based on the BulkLoad function, the access efficiency of the time sequence data is improved, and the efficient processing of the time sequence data is realized.
[0030] Optionally, the collection module comprises:
[0031] The preprocessing module is configured to preprocess the time sequence data to obtain preprocessed time sequence data, wherein the preprocessing comprises missing value processing and smoothing processing.
[0032] According to the above description, the real-time collected time sequence data is subjected to missing value processing and smoothing processing, so that the completeness and effectiveness of the time sequence data are improved.
[0033] Optionally, the preprocessing module is specifically configured to:
[0034] The collection frequency is obtained, a collection calendar is created according to the collection frequency, the preprocessed time sequence data is recorded in the collection calendar, and a recorded collection calendar is obtained.
[0035] The recorded collection calendar is stored in a dual-engine database, and the dual-engine database is a relational database and a time series database.
[0036] According to the above description, the preprocessed time sequence data is recorded in the collection calendar created according to the collection frequency, so that the completeness of the time sequence data is ensured, and the recorded collection calendar is stored not only in the relational database but also in the time series database, so that the completeness and reliability of the time sequence data are further ensured.
[0037] Optionally, the collection module is specifically configured to:
[0038] The compressed time sequence table is stored in a time series container, and the time sequence data in the compressed time sequence table is stored in a cluster-ordered manner in the disk through the time series container.
[0039] According to the above description, the time sequence table is stored in the time series container, that is, the time series container is a container for containing one or more time sequence data structures, so that the time sequence data is stored in a cluster-ordered manner on the disk, and the storage efficiency is improved.
[0040] Optionally, further comprising:
[0041] a prediction module, configured to analyze the time series data by an analysis function to obtain an analysis result, and to statistically process the time series data by a statistical function to obtain a statistical result, to predict a change trend of the time series data according to the analysis result and the statistical result, and to display the change trend prediction result in a visual form;
[0042] wherein the analysis function comprises a Clip function, a TSToXML function, a DelRange function and a DelTrim function, and the statistical function comprises a TSRunningAvg function and a TSRunningSum.
[0043] According to the above description, the time series data is analyzed and statistically processed by various analysis functions and various statistical functions, respectively, to predict a change trend of the time series data according to the obtained analysis result and statistical result, to realize timely early warning of the problem, and to display in a visual form, thereby optimizing the user experience. BRIEF DESCRIPTION OF DRAWINGS
[0044] Figure 1 a flow chart of a time series data efficient processing method provided by an embodiment of the present application;
[0045] Figure 2 a whole flow chart of a time series data efficient processing method provided by an embodiment of the present application;
[0046] Figure 3 a collection calendar diagram related to the present embodiment;
[0047] Figure 4 a diagram of storing time series data of the same data attribute in a columnar storage manner according to corresponding time stamps, related to the present embodiment;
[0048] Figure 5 a structure diagram of a time series data efficient processing device provided by an embodiment of the present application.
[0049]
BRIEF DESCRIPTION OF DRAWINGS
[0050] 1. A time series data efficient processing device;
[0051] 2. a collection module; 21, a preprocessing module;
[0052] 3. a loading module;
[0053] 4. a prediction module. DETAILED DESCRIPTION
[0054] In order to better understand the above technical solutions, the exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present application can be more clearly, thoroughly understood and the scope of the present application can be completely conveyed to those skilled in the art.
[0055] Embodiment one
[0056] The present embodiment is applicable to any field that needs to process time series data. The time series data in the time series table is stored in a columnar storage manner, in which the time series data of the same data attribute is stored in the time series table according to the corresponding timestamp, and each row of time series data in the time series table is compressed to reduce the storage space and storage cost of the time series data. The time series table is mapped to a virtual table. When obtaining time series request data corresponding to a time series data access request, the time series request data is obtained from the virtual table and loaded at high speed through a time series fast loader. This not only enables users to access the time series request data in the form of SQL, but also improves the access efficiency of the time series data. Therefore, the method for processing time series data in the present embodiment can be applied to different fields, such as the industrial field, to improve the detection efficiency of equipment status and faulty equipment, the transportation field, to improve the processing efficiency of traffic flow analysis and traffic signal control, the agricultural field, to improve the detection efficiency of crop growth monitoring, and the like.
[0057] Please refer to Figures 1 to 4 , the present application provides a kind of time series data efficient processing method, comprising the following steps:
[0058] S1, real-time acquisition time series data, obtains the data attribute and timestamp of the time series data, same data attribute time series data is stored in the time series table according to the corresponding timestamp in a columnar storage manner, and each row of time series data in the time series table is compressed, to obtain compressed time series table;
[0059] In the present embodiment, as Figure 2 shown, real-time acquisition time series data, and obtain the data attribute and timestamp of the time series data, wherein data attribute includes field type, field name and the like, same data attribute time series data is regarded as the same row, and the time series data in the same row is sequentially stored in the time series table according to the corresponding timestamp, i.e. according to time sequence, and each row of time series data is compressed to obtain compressed time series table.
[0060] In one specific embodiment, as Figure 3 shown, real-time acquisition is the current time series data and voltage time series data of electric meter number 901, as Figure 4 shown,Figure 3 The collected current time series data and voltage time series data of the electric meter number 901 are stored as the same row and in chronological order according to the time stamp, wherein meter_id in corresponds to Meter id in Figure 4 Figure 3 TimeSeries represents time series data.
[0061] At this time, the real-time collection of time series data in step S1 includes:
[0062] S11, pre-processing the time series data to obtain pre-processed time series data, wherein the pre-processing includes missing value processing and smoothing processing.
[0063] At this time, the pre-processed time series data obtained in step S11 includes:
[0064] S111, obtaining a collection frequency, creating a collection calendar according to the collection frequency, and recording the pre-processed time series data into the collection calendar to obtain a recorded collection calendar.
[0065] In this embodiment, the collection calendar is created according to the collection frequency, that is, the pre-processed time series data is data modeled. For example, if the collection frequency is one minute, a collection calendar with a one-minute time interval is created. The related program code is shown in code 1, and the collection calendar obtained after executing code 1 is shown in Figure 3 .
[0066] Code 1:
[0067] nsert into calendarpatterns values('ts_1min','{1on},minutes');
[0068] insert into CalendarTable(c_name,c_calendar)values('ts_1min',
[0069] 'startdate(2023-01-01 00:00:00),pattstart(2023-01-01 00:00:00),
[0070] pattname(ts_1min)');
[0071] S112, storing the recorded collection calendar into a dual-engine database, wherein the dual-engine database is a relational database and a time series database.
[0072] In this embodiment, as shown in Figure 2 As shown, the recorded collection calendar is stored into a relational database and a time series database, wherein the relational database and the time series database complement each other.
[0073] At this time, the compressed time sequence table obtained in step S1 includes:
[0074] S12, store the compressed time sequence table into a time series container, and store the time sequence data in the compressed time sequence table into a disk in a cluster-ordered manner through the time series container.
[0075] In the embodiment, as shown, Figure 2 the compressed time sequence table is stored into a time series container, wherein the time series container is constructed in advance, and before constructing the time series container, a database space carrying the container is created, and the related program code is shown in code 2:
[0076] Code 2:
[0077] execute procedure TSContainerCreate('ts_cont','datadbs',
[0078] 'meter_read',0,0);
[0079] S2, map the compressed time sequence table to a virtual table to obtain a mapped virtual table, receive a time sequence data access request, obtain time sequence request data corresponding to the time sequence data access request from the mapped virtual table, and load the time sequence request data at high speed through a time sequence fast loader, wherein the time sequence loader is constructed based on a BulkLoad function.
[0080] In the embodiment, as shown, Figure 2 the time sequence table in the time series container is mapped to a virtual table, so that when obtaining time sequence request data corresponding to a time sequence data access request, the time sequence request data is obtained from the mapped virtual table, and the obtained time sequence request data is loaded at high speed through a time sequence fast loader constructed based on a BulkLoad function, that is, an interface based on the BulkLoad function returns the time sequence request data to the server, wherein the related program code for creating the virtual table is shown in code 3:
[0081] Code 3: execute procedure tscreatevirtualtab('smart_meter_virtual','smart_meter');
[0082] The virtual table after successful creation can execute user standard SQL.
[0083] This embodiment also includes predicting the changing trends of time-series data, with the following specific steps:
[0084] The time series data is analyzed and processed by an analytical function to obtain analytical results, and the time series data is statistically processed by a statistical function to obtain statistical results. Based on the analytical results and the statistical results, the time series data is predicted to change trends to obtain trend prediction results. The trend prediction results are then displayed in a visual form.
[0085] The analysis functions include the Clip function, TSToXML function, DelRange function, and DelTrim function, and the statistical functions include the TSRunningAvg function and TSRunningSum function.
[0086] In this embodiment, as Figure 2 As shown, analytical functions are used to analyze and process time-series data. These functions include, but are not limited to, the Clip, TSToXML, DelRange, and DelTrim functions. The Clip function extracts time-series data within a preset time period; the TSToXML function allows viewing and exporting time-series data in XML format; the DelRange function clears time-series data within any given range; and the DelTrim function deletes data from a specified time range within a given time-series data set and releases space. Statistical functions are used to perform statistical processing on the time-series data. These functions include, but are not limited to, the TSRunningAvg function, which calculates the moving average of the time-series data, and the TSRunningSum function, which calculates the running sum of a specified number of time-series data sets. Based on the obtained analytical and statistical results, the trend of the time-series data is predicted and displayed in a visual form.
[0087] Example 2
[0088] Please refer to Figure 5 A device 1 for efficient processing of time-series data specifically includes: an acquisition module 2, a preprocessing module 21, a loading module 3, and a prediction module 4.
[0089] Among them, the acquisition module 2 is used to acquire time series data in real time, obtain the data attributes and timestamps of the time series data, store the time series data with the same data attributes in a columnar storage manner according to the corresponding timestamps into a pre-built time series table, and compress each row of time series data in the time series table to obtain a compressed time series table.
[0090] Loading module 3 is used to map the compressed timing table to a virtual table to obtain the mapped virtual table, receive timing data access requests, obtain timing request data corresponding to the timing data access request from the mapped virtual table, and realize high-speed loading of the timing request data through a timing fast loader, wherein the timing loader is built based on the BulkLoad function.
[0091] Specifically, the acquisition module 2 includes:
[0092] Preprocessing module 21 is used to preprocess the time series data to obtain preprocessed time series data, wherein the preprocessing includes missing value processing and smoothing processing.
[0093] The preprocessing module 21 is specifically used for:
[0094] Obtain the acquisition frequency, create an acquisition calendar based on the acquisition frequency, and record the preprocessed time series data into the acquisition calendar to obtain the recorded acquisition calendar;
[0095] The recorded data collection calendar is stored in a dual-engine database, which consists of a relational database and a time-series database.
[0096] Specifically, the acquisition module 2 is used for:
[0097] The compressed time series table is stored in a time series container, and the time series container is used to store the time series data in the compressed time series table to the disk in a clustered and ordered manner.
[0098] Specifically, the prediction module 4 is used to analyze and process the time series data through an analysis function to obtain analysis results, and to perform statistical processing on the time series data through a statistical function to obtain statistical results. Based on the analysis results and the statistical results, the module predicts the trend of the time series data to obtain trend prediction results, and displays the trend prediction results in a visual form.
[0099] The analysis functions include the Clip function, TSToXML function, DelRange function, and DelTrim function, and the statistical functions include the TSRunningAvg function and TSRunningSum function.
[0100] Since the systems / devices described in the above embodiments of the present invention are systems / devices used to implement the methods of the above embodiments of the present invention, those skilled in the art can understand the specific structure and modifications of the systems / devices based on the methods described in the above embodiments of the present invention, and therefore will not be repeated here. All systems / devices used in the methods of the above embodiments of the present invention fall within the scope of protection of the present invention.
[0101] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0102] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions.
[0103] It should be noted that any reference numerals placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In claims that enumerate several means, several of these means may be embodied by the same hardware. The use of the terms first, second, third, etc., is merely for convenience of expression and does not indicate any order. These terms can be understood as part of the component names.
[0104] Furthermore, it should be noted that in the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0105] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the claims should be interpreted to include both the preferred embodiments and all changes and modifications falling within the scope of the invention.
[0106] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, then this invention should also include these modifications and variations.
Claims
1. A method for efficient processing of time-series data, characterized in that, include: Real-time acquisition of time-series data, obtaining the data attributes and timestamps of the time-series data, storing time-series data with the same data attributes in a columnar storage manner according to the corresponding timestamps into a pre-constructed time-series table, and compressing each row of time-series data in the time-series table to obtain a compressed time-series table; The compressed timing table is mapped to a virtual table to obtain the mapped virtual table. A timing data access request is received, and the timing request data corresponding to the timing data access request is obtained from the mapped virtual table. The timing request data is loaded at high speed through a timing fast loader, wherein the timing loader is built based on the BulkLoad function.
2. The method for efficient processing of time-series data as described in claim 1, characterized in that, The real-time acquired time-series data includes: The time series data is preprocessed to obtain preprocessed time series data, wherein the preprocessing includes missing value handling and smoothing.
3. The method for efficient processing of time-series data as described in claim 2, characterized in that, The preprocessed time series data includes: Obtain the acquisition frequency, create an acquisition calendar based on the acquisition frequency, and record the preprocessed time series data into the acquisition calendar to obtain the recorded acquisition calendar; The recorded data collection calendar is stored in a dual-engine database, which consists of a relational database and a time-series database.
4. The method for efficient processing of time-series data as described in claim 1, characterized in that, The obtained compressed timing table includes: The compressed time series table is stored in a time series container, and the time series container is used to store the time series data in the compressed time series table to the disk in a clustered and ordered manner.
5. The method for efficient processing of time-series data as described in claim 1, characterized in that, Also includes: The time series data is analyzed and processed by an analytical function to obtain analytical results, and the time series data is statistically processed by a statistical function to obtain statistical results. Based on the analytical results and the statistical results, the time series data is predicted to change trends to obtain trend prediction results. The trend prediction results are then displayed in a visual form. The analysis functions include the Clip function, TSToXML function, DelRange function, and DelTrim function, and the statistical functions include the TSRunningAvg function and TSRunningSum function.
6. An apparatus for efficient processing of time-series data, characterized in that, include: The acquisition module is used to acquire time-series data in real time, obtain the data attributes and timestamps of the time-series data, store time-series data with the same data attributes in a columnar storage manner according to the corresponding timestamps into a pre-built time-series table, and compress each row of time-series data in the time-series table to obtain a compressed time-series table. The loading module is used to map the compressed timing table to a virtual table to obtain the mapped virtual table, receive timing data access requests, obtain the timing request data corresponding to the timing data access request from the mapped virtual table, and realize high-speed loading of the timing request data through a timing fast loader, wherein the timing loader is built based on the BulkLoad function.
7. The apparatus for efficient processing of time-series data as described in claim 6, characterized in that, The acquisition module includes: The preprocessing module is used to preprocess the time series data to obtain preprocessed time series data, wherein the preprocessing includes missing value handling and smoothing.
8. The apparatus for efficient processing of time-series data as described in claim 7, characterized in that, The preprocessing module is specifically used for: Obtain the acquisition frequency, create an acquisition calendar based on the acquisition frequency, and record the preprocessed time series data into the acquisition calendar to obtain the recorded acquisition calendar; The recorded data collection calendar is stored in a dual-engine database, which consists of a relational database and a time-series database.
9. The apparatus for efficient processing of time-series data as described in claim 6, characterized in that, The acquisition module is specifically used for: The compressed time series table is stored in a time series container, and the time series container is used to store the time series data in the compressed time series table to the disk in a clustered and ordered manner.
10. The apparatus for efficient processing of time-series data as described in claim 6, characterized in that, Also includes: The prediction module is used to analyze and process the time series data through analysis functions to obtain analysis results, and to statistically process the time series data through statistical functions to obtain statistical results. Based on the analysis results and the statistical results, the module predicts the trend of the time series data to obtain trend prediction results, and displays the trend prediction results in a visual form. The analysis functions include the Clip function, TSToXML function, DelRange function, and DelTrim function, and the statistical functions include the TSRunningAvg function and TSRunningSum function.