CN114692987A - Time sequence data analysis method, device, equipment and storage medium - Google Patents
Time sequence data analysis method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a time sequence data analysis method, a time sequence data analysis device, a time sequence data analysis equipment and a storage medium, wherein the time sequence data analysis device comprises the following steps: acquiring target time sequence data to be analyzed, and respectively extracting seasonal components and non-seasonal components included in the target time sequence data; inputting the non-seasonal component into an ARIMA model trained in advance, and determining a non-seasonal prediction result corresponding to the target time sequence data through the ARIMA model; and combining the seasonal component included in the target time sequence data with the non-seasonal prediction result to obtain a comprehensive prediction result corresponding to the target time sequence data. The technical scheme of the embodiment of the invention can improve the universality of the time sequence data analysis method and the reliability of the analysis result.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a time sequence data analysis method, a time sequence data analysis device, time sequence data analysis equipment and a storage medium.
Background
The time series Model is a common method for analyzing time series data, and an algorithm represented by an Auto-Regressive and Moving Average Model (ARMA) is one of algorithms commonly used in the field of time series data analysis.
The ARMA model requires long and stable data during training, and has poor generalization capability. To improve the limitations of the ARMA model, the prior art has expanded a plurality of time series models based on the ARMA model, such as an Integrated Moving Average Autoregressive model (ARIMA), and a Seasonal differential Autoregressive Moving Average model (SARIMA).
However, the ARIMA model is not suitable for processing seasonal time series data, and the SARIMA model is only suitable for processing simple seasonal data, cannot be effectively applied to an actual production environment, and is low in universality; secondly, the SARIMA model is difficult to train, needs a large number of input parameters, and consumes a large amount of time in the model tuning and verification stage, resulting in low reliability of data analysis results.
Disclosure of Invention
Embodiments of the present invention provide a time series data analysis method, apparatus, device, and storage medium, which can improve the versatility of the time series data analysis method and the reliability of an analysis result.
In a first aspect, an embodiment of the present invention provides a time series data analysis method, where the method includes:
acquiring target time sequence data to be analyzed, and respectively extracting seasonal components and non-seasonal components included in the target time sequence data;
inputting the non-seasonal component into a pre-trained integrated moving average autoregressive model (ARIMA), and determining a non-seasonal prediction result corresponding to the target time sequence data through the ARIMA;
and combining the seasonal component included in the target time sequence data with the non-seasonal prediction result to obtain a comprehensive prediction result corresponding to the target time sequence data.
Optionally, before acquiring target time series data to be analyzed, the method further includes:
and acquiring original time sequence data to be analyzed, and carrying out denoising processing on the original time sequence data to obtain the target time sequence data.
Optionally, denoising the original time series data to obtain the target time series data, including:
detecting abnormal data included in the original time sequence data by adopting a preset abnormal detection algorithm, and removing the abnormal data to obtain a plurality of idle data bits corresponding to the original time sequence data;
interpolating according to a preset interpolation algorithm to obtain a plurality of interpolation data matched with the original time sequence data, and filling each idle data bit in sequence by using each interpolation data to obtain updated original time sequence data;
and denoising the updated original time sequence data to obtain the target time sequence data.
Optionally, the extracting seasonal components and non-seasonal components included in the target time series data respectively includes:
decomposing the target time sequence data into a seasonal component, a trend component and a residual component by adopting a time sequence decomposition algorithm;
and determining a non-seasonal component included in the target time series data according to the trend component and the residual component.
Optionally, the extracting seasonal components and non-seasonal components included in the target time series data respectively includes:
acquiring a plurality of periodic sequences included in the target time sequence data, determining a target differential item according to each periodic sequence, and taking the target differential item as a seasonal component corresponding to the target time sequence data;
and carrying out seasonal difference on the target difference item, and taking a difference result as a non-seasonal component corresponding to the target time sequence data.
Optionally, determining a non-seasonal prediction result corresponding to the target time series data through an ARIMA model includes:
and determining a non-seasonal prediction result corresponding to the target time sequence data according to a prediction parameter input by a user in advance through the ARIMA model.
In a second aspect, an embodiment of the present invention further provides a time series data analysis apparatus, where the apparatus includes:
the data extraction module is used for acquiring target time sequence data to be analyzed and respectively extracting seasonal components and non-seasonal components included in the target time sequence data;
the data input module is used for inputting the non-seasonal component into a pre-trained integrated moving average autoregressive model (ARIMA), and determining a non-seasonal prediction result corresponding to the target time sequence data through the ARIMA;
and the result determining module is used for combining the seasonal component included in the target time sequence data with the non-seasonal prediction result to obtain a comprehensive prediction result corresponding to the target time sequence data.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
storage means for storing one or more programs;
the time series data analysis method provided by any embodiment of the invention is implemented when the one or more programs are executed by the one or more processors, so that the one or more processors execute the programs.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the time series data analysis method provided in any embodiment of the present invention.
In a fifth aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the time series data analysis method provided in any embodiment of the present invention.
According to the technical scheme, the target time sequence data to be analyzed are obtained, the seasonal component and the non-seasonal component included in the target time sequence data are respectively extracted, the non-seasonal component is input into the pre-trained ARIMA model, the non-seasonal prediction result corresponding to the target time sequence data is determined through the ARIMA model, the seasonal component included in the target time sequence data is combined with the non-seasonal prediction result, the comprehensive prediction result corresponding to the target time sequence data is obtained, and the universality of the time sequence data analysis method and the reliability of the analysis result can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for analyzing time series data according to an embodiment of the present invention;
FIG. 2 is a flowchart of a time series data analysis method according to a second embodiment of the present invention;
FIG. 3 is a flowchart of a method for analyzing time series data according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a time series data analysis apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the time series data analysis method according to the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a time series data analysis method according to an embodiment of the present invention, which is applicable to analyzing and predicting time series data and can be executed by a time series data analysis apparatus. The time series data analysis device may be implemented by software and/or hardware, and may be generally integrated in an electronic device (e.g., a terminal or a server) having a data processing function, and specifically includes the following steps:
In the present embodiment, the target time-series data refers to time-series data to be analyzed, which can be understood as a data series recorded in time series under the same index. After the target time series data is acquired, optionally, the periodic sequence data (that is, the seasonal component) in the target time series data may be determined according to the data characteristics of the target time series data, and the remaining data except the seasonal component may be used as the non-seasonal component.
In a specific embodiment, optionally, the target time series data may be divided into a plurality of data segments according to a preset data length, then a period corresponding to each data segment is calculated, and a seasonal component and a non-seasonal component included in the target time series data are determined according to a period calculation result corresponding to each data segment.
And 120, inputting the non-seasonal component into a pre-trained integrated moving average autoregressive model ARIMA, and determining a non-seasonal prediction result corresponding to the target time sequence data through the ARIMA.
In this embodiment, since the ARIMA model is good at processing non-seasonal time series data, the non-seasonal component may be input into the ARIMA model trained in advance, the ARIMA model may analyze and process the non-seasonal component to obtain a data feature corresponding to the non-seasonal component, and the non-seasonal data corresponding to the target time series data may be predicted according to the data feature corresponding to the non-seasonal component to obtain a non-seasonal prediction result.
And step 130, combining the seasonal component included in the target time sequence data with the non-seasonal prediction result to obtain a comprehensive prediction result corresponding to the target time sequence data.
In this embodiment, after determining the non-seasonal prediction result corresponding to the target time series data through the ARIMA model, the seasonal component extracted in step 110 may be combined with the non-seasonal prediction result to obtain a comprehensive prediction result corresponding to the target time series data.
In the embodiment, by inputting the non-seasonal component in the target time sequence data into the ARIMA model, compared with the existing SARIMA model, the computation complexity of the time sequence model can be effectively reduced, and the training time and the parameter number of the model are reduced; and secondly, by respectively extracting the seasonal component and the non-seasonal component in the target time sequence data and respectively processing the seasonal component and the non-seasonal component, the time sequence data with complex components can be analyzed, and the universality of a time sequence data analysis method and the reliability of an analysis result are improved.
According to the technical scheme, the target time sequence data to be analyzed are obtained, the seasonal component and the non-seasonal component included in the target time sequence data are respectively extracted, the non-seasonal component is input into the ARIMA model trained in advance, the non-seasonal prediction result corresponding to the target time sequence data is determined through the ARIMA model, the seasonal component included in the target time sequence data is combined with the non-seasonal prediction result, the comprehensive prediction result corresponding to the target time sequence data is obtained, and the universality of the time sequence data analysis method and the reliability of the analysis result can be improved.
On the basis of the above embodiment, before acquiring target time series data to be analyzed, the method further includes: and acquiring original time sequence data to be analyzed, and carrying out denoising processing on the original time sequence data to obtain the target time sequence data.
In this embodiment, the raw time series data may be unprocessed time series data. After the original time sequence data is obtained, denoising processing can be performed on the original time sequence data by adopting a preset denoising algorithm to obtain target time sequence data. This arrangement has an advantage that it is possible to prevent noise data in the original time series data from affecting the analysis result, and thus it is possible to improve the reliability of the time series data analysis result.
In a specific embodiment, optionally, the denoising algorithm may be a k-nearest neighbor replacement method, a local weighted replacement method, or a regression filling method, which is not limited in this embodiment.
Example two
This embodiment is a further refinement of the above embodiment, and the same or corresponding terms as those of the above embodiment are explained, and this embodiment is not described again. Fig. 2 is a flowchart of a time series data analysis method provided in the second embodiment, the technical solution of the second embodiment may be combined with one or more methods in the solutions of the foregoing embodiments, as shown in fig. 2, the method provided in the second embodiment may further include:
In this embodiment, the anomaly detection algorithm may be a Three Sigma criteria (Three Sigma Guidelines, 3-Sigma) algorithm, or other unsupervised anomaly detection algorithm. Each data in the original time sequence data corresponds to one data bit, and after a plurality of abnormal data are detected by adopting an abnormal detection algorithm, the abnormal data can be removed to obtain a plurality of idle data bits.
The advantage of setting up like this is that can avoid unusual data in the time series data to cause the influence to the analysis result, can improve the accuracy of time series data analysis result from this.
And 220, interpolating according to a preset interpolation algorithm to obtain a plurality of interpolation data matched with the original time sequence data, and sequentially filling each idle data bit with each interpolation data to obtain updated original time sequence data.
In this embodiment, a plurality of interpolation data matched with the plurality of idle data bits may be obtained by interpolation according to a preset interpolation algorithm, and each idle data bit is sequentially filled with each interpolation data. The method has the advantages that the continuity of time sequence data can be guaranteed, and the accuracy of subsequent prediction results is improved. Specifically, the interpolation algorithm may be a linear interpolation algorithm, a nonlinear interpolation algorithm, or a periodic interpolation algorithm, which is not limited in this embodiment.
And 230, denoising the updated original time sequence data to obtain target time sequence data.
In this step, optionally, a mean sliding window or a filtering algorithm may be used to perform denoising processing on the updated original time series data to obtain target time series data.
And 240, decomposing the target time sequence data into a seasonal component, a trend component and a residual component by adopting a time sequence decomposition algorithm.
In the present embodiment, a Time Series Decomposition (TSD) algorithm may be employed to decompose the target Time Series data into a seasonal component, a trend component, and a residual component.
In a specific embodiment, assuming that the target time series data is X, after the target time series data is decomposed by the TSD algorithm, the obtained seasonal component is S, the trend component is T, and the residual component is R, the operational relationship between the target time series data and each component may be: x ═ S + T + R, or X ═ sxt + R.
And step 250, determining a non-seasonal component included in the target time series data according to the trend component and the residual component.
In this step, optionally, a trend component and a residual component obtained after the target time series data is decomposed may be used as the non-seasonal component.
The TSD algorithm is adopted to decompose the target time sequence data, so that seasonal components and non-seasonal components in the target time sequence data can be accurately obtained, and the accuracy of time sequence data analysis results can be improved.
And step 260, inputting the non-seasonal component into an ARIMA model trained in advance, and determining a non-seasonal prediction result corresponding to the target time sequence data through the ARIMA model.
And 270, combining the seasonal component included in the target time sequence data with the non-seasonal prediction result to obtain a comprehensive prediction result corresponding to the target time sequence data.
The technical scheme of the embodiment of the invention comprises the steps of obtaining original time sequence data to be analyzed, detecting abnormal data in the original time sequence data by adopting a preset abnormal detection algorithm, removing the abnormal data to obtain a plurality of idle data bits, interpolating according to the preset interpolation algorithm to obtain a plurality of interpolation data, sequentially filling each idle data bit by using each interpolation data to obtain updated original time sequence data, denoising the updated original time sequence data to obtain target time sequence data, decomposing the target time sequence data into a seasonal component, a trend component and a residual component by adopting a TSD algorithm, determining a non-seasonal component according to the trend component and the residual component, inputting the non-seasonal component into an ARIMA model, determining a non-seasonal prediction result by the ARIMA model, and combining the seasonal component and the non-seasonal prediction result in the target time sequence data, the technical means for obtaining the comprehensive prediction result can improve the universality of the time series data analysis method and the reliability of the analysis result.
EXAMPLE III
This embodiment is a further refinement of the above embodiment, and the same or corresponding terms as those of the above embodiment are explained, and this embodiment is not described again. Fig. 3 is a flowchart of a time series data analysis method provided in a third embodiment, in the third embodiment, the technical solution of the third embodiment may be combined with one or more methods in the solutions of the foregoing embodiments, as shown in fig. 3, the method provided in the third embodiment may further include:
In this embodiment, after the target time series data is acquired, a period corresponding to the target time series data may be calculated, the target time series data is divided into a plurality of period sequences according to a period calculation result, then a first period sequence or a median of all the period sequences is used as a target difference item, and finally the target difference item is used as a seasonal component corresponding to the target time series data.
And 320, carrying out seasonal difference on the target difference item, and taking a difference result as a non-seasonal component corresponding to the target time sequence data.
In one particular embodiment, the target differential term is assumed to be
The target difference term may be seasonally differentiated by the following formula:
y＝{|xi-xj|j＝i mod s,i＝s+1…}
the advantage of this arrangement is that by performing seasonal differentiation on the target time series data, the seasonal component and the non-seasonal component in the target time series data can be obtained quickly and accurately, and thus the accuracy of the time series data analysis result can be improved.
And step 330, inputting the non-seasonal component into a pre-trained ARIMA model.
And 340, determining a non-seasonal prediction result corresponding to the target time sequence data according to a prediction parameter input by a user in advance through the ARIMA model.
In this embodiment, before inputting the non-seasonal component to the ARIMA model, the ARIMA model may be trained using a large amount of non-seasonal time series data as training samples, and then the non-seasonal component is input to the trained ARIMA model. The ARIMA model can receive the non-seasonal component and also can receive a prediction parameter input by a user, then analyze and process the non-seasonal component to obtain a data characteristic corresponding to the non-seasonal component, and predict non-seasonal data corresponding to target time sequence data according to the data characteristic corresponding to the non-seasonal component and the prediction parameter to obtain a non-seasonal prediction result.
And 350, combining the seasonal component included in the target time sequence data with the non-seasonal prediction result to obtain a comprehensive prediction result corresponding to the target time sequence data.
According to the technical scheme, the target time sequence data to be analyzed are obtained, the plurality of periodic sequences included in the target time sequence data are obtained, the target difference item is determined according to each periodic sequence, the target difference item is used as a seasonal component, seasonal difference is conducted on the target difference item, the difference result is used as a non-seasonal component, the non-seasonal component is input into the ARIMA model, the non-seasonal prediction result is determined according to the prediction parameters through the ARIMA model, the seasonal component included in the target time sequence data is combined with the non-seasonal prediction result, and the comprehensive prediction result corresponding to the target time sequence data is obtained.
Example four
Fig. 4 is a schematic structural diagram of a time series data analysis apparatus according to a fourth embodiment of the present invention, as shown in fig. 4, the apparatus includes: a data extraction module 410, a data input module 420, and a result determination module 430.
The data extraction module 410 is configured to obtain target time series data to be analyzed, and respectively extract a seasonal component and a non-seasonal component included in the target time series data;
a data input module 420, configured to input the non-seasonal component into a pre-trained integrated moving average autoregressive model ARIMA, and determine, through an ARIMA model, a non-seasonal prediction result corresponding to the target time series data;
a result determining module 430, configured to combine the seasonal component included in the target time series data with the non-seasonal prediction result to obtain a comprehensive prediction result corresponding to the target time series data.
According to the technical scheme provided by the embodiment of the invention, the target time sequence data to be analyzed is obtained, the seasonal component and the non-seasonal component included in the target time sequence data are respectively extracted, the non-seasonal component is input into the pre-trained ARIMA model, the non-seasonal prediction result corresponding to the target time sequence data is determined through the ARIMA model, the seasonal component included in the target time sequence data is combined with the non-seasonal prediction result to obtain the comprehensive prediction result corresponding to the target time sequence data, and the universality of the time sequence data analysis method and the reliability of the analysis result can be improved.
On the basis of the above embodiment, the time-series data analysis apparatus further includes:
and the original data processing module is used for acquiring original time sequence data to be analyzed and carrying out denoising processing on the original time sequence data to obtain the target time sequence data.
The raw data processing module comprises:
the abnormal detection unit is used for detecting abnormal data included in the original time sequence data by adopting a preset abnormal detection algorithm and removing the abnormal data to obtain a plurality of idle data bits corresponding to the original time sequence data;
the interpolation unit is used for interpolating according to a preset interpolation algorithm to obtain a plurality of interpolation data matched with the original time sequence data, and filling each idle data bit in sequence by using each interpolation data to obtain updated original time sequence data;
and the denoising unit is used for denoising the updated original time sequence data to obtain the target time sequence data.
The data extraction module 410 includes:
the data decomposition unit is used for decomposing the target time sequence data into a seasonal component, a trend component and a residual error component by adopting a time sequence decomposition algorithm;
a non-seasonal component determination unit configured to determine a non-seasonal component included in the target time series data, according to the trend component and the residual component;
a differential item determining unit, configured to obtain multiple periodic sequences included in the target time series data, determine a target differential item according to each periodic sequence, and use the target differential item as a seasonal component corresponding to the target time series data;
and the seasonal difference unit is used for carrying out seasonal difference on the target difference item and taking a difference result as a non-seasonal component corresponding to the target time sequence data.
The data input module 420 includes:
and the prediction result determining unit is used for determining a non-seasonal prediction result corresponding to the target time sequence data according to a prediction parameter input by a user in advance through the ARIMA model.
The device can execute the methods provided by all the embodiments of the invention, and has corresponding functional modules and beneficial effects for executing the methods. For technical details which are not described in detail in the embodiments of the present invention, reference may be made to the methods provided in all the aforementioned embodiments of the present invention.
EXAMPLE five
FIG. 5 illustrates a block diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the time series data analysis method.
In some embodiments, the time series data analysis method may be implemented as a computer program tangibly embodied in a computer readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the time series data analysis method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the time series data analysis method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of time series data analysis, the method comprising:
acquiring target time sequence data to be analyzed, and respectively extracting seasonal components and non-seasonal components included in the target time sequence data;
inputting the non-seasonal component into a pre-trained integrated moving average autoregressive model (ARIMA), and determining a non-seasonal prediction result corresponding to the target time sequence data through the ARIMA;
and combining the seasonal component included in the target time sequence data with the non-seasonal prediction result to obtain a comprehensive prediction result corresponding to the target time sequence data.
2. The method of claim 1, further comprising, before obtaining the target time series data to be analyzed:
and acquiring original time sequence data to be analyzed, and carrying out denoising processing on the original time sequence data to obtain the target time sequence data.
3. The method of claim 2, wherein denoising the raw time series data to obtain the target time series data comprises:
detecting abnormal data included in the original time sequence data by adopting a preset abnormal detection algorithm, and removing the abnormal data to obtain a plurality of idle data bits corresponding to the original time sequence data;
interpolating according to a preset interpolation algorithm to obtain a plurality of interpolation data matched with the original time sequence data, and filling each idle data bit in sequence by using each interpolation data to obtain updated original time sequence data;
and denoising the updated original time sequence data to obtain the target time sequence data.
4. The method of claim 1, wherein separately extracting seasonal and non-seasonal components included in the target time series data comprises:
decomposing the target time sequence data into a seasonal component, a trend component and a residual component by adopting a time sequence decomposition algorithm;
and determining a non-seasonal component included in the target time series data according to the trend component and the residual component.
5. The method of claim 1, wherein separately extracting seasonal and non-seasonal components included in the target time series data comprises:
acquiring a plurality of periodic sequences included in the target time sequence data, determining a target differential item according to each periodic sequence, and taking the target differential item as a seasonal component corresponding to the target time sequence data;
and carrying out seasonal difference on the target difference item, and taking a difference result as a non-seasonal component corresponding to the target time sequence data.
6. The method of claim 5, wherein determining a non-seasonal prediction result corresponding to the target temporal data by an ARIMA model comprises:
and determining a non-seasonal prediction result corresponding to the target time sequence data according to a prediction parameter input by a user in advance through the ARIMA model.
7. An apparatus for analyzing time series data, the apparatus comprising:
the data extraction module is used for acquiring target time sequence data to be analyzed and respectively extracting seasonal components and non-seasonal components included in the target time sequence data;
the data input module is used for inputting the non-seasonal component into a pre-trained integrated moving average autoregressive model (ARIMA), and determining a non-seasonal prediction result corresponding to the target time sequence data through the ARIMA;
and the result determining module is used for combining the seasonal component included in the target time sequence data with the non-seasonal prediction result to obtain a comprehensive prediction result corresponding to the target time sequence data.
8. An electronic device, the electronic device comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of temporal data analysis of any of claims 1-6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the time series data analysis method according to any one of claims 1 to 6.
10. A computer program product, characterized in that the computer program product comprises a computer program which, when being executed by a processor, implements the time series data analysis method according to any one of claims 1-6.
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| CN115292304A (en) * | 2022-10-09 | 2022-11-04 | 深圳市明源云科技有限公司 | Method and apparatus for detecting abnormality of time series data, and computer-readable storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115292304A (en) * | 2022-10-09 | 2022-11-04 | 深圳市明源云科技有限公司 | Method and apparatus for detecting abnormality of time series data, and computer-readable storage medium |
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