High-precision long-term time series prediction method based on multivariate time series data analysis

A time-series data and time-series technology, applied in the field of high-precision long-term time series prediction, can solve the problems of insufficient ability to predict long-term sequences, large memory size, and high computational complexity, so as to improve prediction accuracy, improve fitting ability, increase The effect of predicted length

Pending Publication Date: 2022-03-25
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is that the accuracy of the existing model is insufficient, the computational complexity is too high, the memory size is large, and the ability to predict long sequences is insufficient

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  • High-precision long-term time series prediction method based on multivariate time series data analysis
  • High-precision long-term time series prediction method based on multivariate time series data analysis
  • High-precision long-term time series prediction method based on multivariate time series data analysis

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Embodiment Construction

[0054] Below in conjunction with accompanying drawing and specific implementation steps, the present invention has been further described:

[0055] A high-precision and low-memory discrete feature extraction method (Sepformer) based on multivariate time series prediction, including the following steps:

[0056] Step 1: Data preprocessing, obtain training data set and verification data set.

[0057] Select an appropriate public time series data set, and perform grouping and segmentation to meet the data format requirements of the model. First, set the historical sequence length, predicted sequence length, and initial sequence length in each set of data according to requirements. These three lengths correspond to three parts in each set of data: historical sequence, forecast sequence, and initial sequence. In terms of length, the length of the initial sequence is less than or equal to the length of the historical sequence, and in terms of value, the initial sequence is the same...

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Abstract

The invention discloses a high-precision long-term time series prediction method based on multivariate time series data analysis, and the method comprises the steps: employing a discrete network to extract global features and local features of a multivariate time series in a layered and parallel manner; the calculation complexity is reduced while the multivariate time sequence prediction precision is improved, the model scale is reduced, and the prediction length of the model is increased. According to the method, a mechanism for extracting the global features and the local features of the multivariate time series in a layered and parallel manner is adopted, the prediction precision is improved, the memory usage amount of the model is reduced, the local features are utilized to improve the fitting capability of local fine fluctuations of the multivariate time series, and the prediction length of the model is increased; and the effect of the model on multivariate time sequence prediction is greatly improved.

Description

technical field [0001] The invention belongs to the field of time series forecasting, and in particular relates to a high-precision long-term time series forecasting method based on multivariate time series data analysis. Background technique [0002] Time series forecasting is an important branch in the field of time series analysis, which is widely used in weather forecasting, stock forecasting and anomaly detection and other fields. The time series forecasting method predicts the time series in the future by learning the characteristic laws of the past time series. With the increase of the length of the forecast sequence and the transition from univariate time series to multivariate time series, the difficulty of time series forecasting increases accordingly. Long-term series forecasting requires methods with longer forecasting ability and higher forecasting accuracy, while Multivariate time series forecasting requires methods that can capture the relationship between mu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q10/04
CPCG06N3/04G06N3/084G06Q10/04G06F18/214
Inventor 王则昊樊谨俞晓锋汪炜杰孙丹枫
Owner HANGZHOU DIANZI UNIV
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