Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Intelligent predicting method for time sequence based on trend and periodic fluctuation

A time series and intelligent forecasting technology, applied in the field of data analysis, can solve problems such as unresolved fluctuation components

Inactive Publication Date: 2014-12-03
INST OF COTTON RES CHINESE ACAD OF AGRI SCI
View PDF4 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, there is an improved method for the regression method in the existing technology, that is, by reducing the space and dividing it into several small spaces so that the model is close to the trend, but the problem of the fluctuation component is still not solved

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Intelligent predicting method for time sequence based on trend and periodic fluctuation
  • Intelligent predicting method for time sequence based on trend and periodic fluctuation
  • Intelligent predicting method for time sequence based on trend and periodic fluctuation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0027] Such as figure 1 As shown, the present invention provides a kind of intelligent prediction method based on the time series of trend and periodic fluctuation, comprising the following steps:

[0028] S1, establishing a trend model library; three types of trend models are stored in the trend model library, namely: a linear trend model, a nonlinear trend model and an adaptive trend model; each type of trend model includes several specific trend models;

[0029] S2. Read the original time series to be predicted, calculate the original time series, and separate the fluctuation component and the trend component of the original time series;

[0030] S3, for the trend component, automatically select R in the trend model library through regression calculation 2 The largest trend model; wherein, the original time series is composed of N groups of original observation data; the R 2 The largest trend model is called the best trend model, and the expression is Y t =f(X); where, R...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides an intelligent predicting method for time sequence based on trend and periodic fluctuation, which comprises the steps of: computing an original time sequence to be predicated, separating a fluctuation component and a trend component of the sequence; for the trend component, automatically selecting a trend model with a maximal R2 from a trend model database through regression computing; converting the fluctuation component to a periodic variable according to an and-angle formula, adding the periodic variable into an optimal trend model; automatically selecting z periodical lengths with optimal influence functions according to a principle of maximizing a coefficient of determination, substituting z periodical lengths into the optical trend model with the periodical variable, and obtaining a final whole model; and determining a specific model parameter through computing the original time sequence by the final whole model, thereby predicting change trend of the original time sequence. The intelligent predicting method has advantages of high prediction intelligence and high precision through prediction processing automation and combining the trend component and the fluctuation component of a predicting model.

Description

technical field [0001] The invention belongs to the technical field of data analysis, and in particular relates to an intelligent prediction method based on time series of trends and periodic fluctuations. Background technique [0002] Quantitative forecasting methods for things mainly include time series forecasting and regression analysis. [0003] Time series refers to a collection of numerical values ​​that change over time in chronological order, which commonly exists in real life, such as: daily stock prices, seasonal rainfall, etc. This kind of data can be abstracted as a binary combination (t, x), where t is a time variable and x is a data variable. Time series prediction has important application value in many practical application fields. At present, good prediction results have been obtained for stationary time series. However, because the time series in actual production or life is very complex, showing nonlinear and non-stationary characteristics, therefore, m...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06Q10/04
Inventor 魏晓文雷亚平
Owner INST OF COTTON RES CHINESE ACAD OF AGRI SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com