Prophet combination model-based monitoring time sequence data prediction method

Abstract

The invention discloses a monitoring time sequence data prediction method based on a Prophet combination model, and relates to the technical field of engineering construction and operation and maintenance. Comprising the following steps: S1, arranging a sensor on an engineering site to obtain original time sequence data X0 = {x01, x02,..., x0i,..., x0n}, and marking festivals and holidays for interference time sequence data; s2, constructing a Prophet combination model, substituting original time sequence data X0 into a data preprocessing layer of the Prophet combination model, and performing iterative calculation to obtain time sequence data X '= {x' 1, x '2,..., x'i,..., x'n} after the gross error is deleted; the method comprises the steps of (S1) obtaining a Prophet combination model prediction layer, (S2) obtaining a Prophet combination model prediction layer, (S3) substituting X'into the Prophet combination model regression layer, and carrying out regression vacancy calculation to obtain complete time sequence data Y '= {y' 1, y '2,..., y'i,..., y'n}, and (S4) substituting the complete time sequence data Y' into the Prophet combination model prediction layer to obtain predicted time sequence data Z '= {z'n-k + 1, z'n-k + 2,..., z'n}. According to the method, the anti-interference performance of a prediction model is enhanced, and the prediction accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of engineering construction and operation and maintenance, in particular to a method for processing and predicting monitoring time series data of engineering structures, and in particular to a method for predicting monitoring time series data based on a Prophet combination model. Background technique [0002] With the continuous improvement of national infrastructure construction, the field of civil engineering has developed from large-scale construction to operation and maintenance. Carrying out real-time monitoring of civil engineering structures can fully grasp the service status of the structure, predict the development trend of diseases, and timely propose corresponding maintenance measures to prolong the service life and reduce the loss of the national economy. Civil engineering structure monitoring generally adopts the method of arranging sensors to perceive structural service information. However, d...

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Problems solved by technology

[0003] At present, the popular prediction methods used in engineering practice mainly include the following categories: (1) ARIMA model, such as the patent "A Method for Predicting Industrial Waste Gas Emissions Based on ARIMA Model (CN202110051775.1)", this model is different from The ARMA model does not require the data to belong to a time-stationary sequence, but the prediction effect on complex data is not good, and there is a problem of insufficient accuracy

(2) LSTM neural network model, such as the patent "A Bridge Static Displacement Prediction Technology Based on Deep Learning LSTM Network (CN202011628545.9)", LSTM is improved on the traditional RNN to provide long and short-term memory functions, effectively improving data accuracy. Long-term prediction performance, but the training network takes a long time, and the network structure setting has a great influence on the prediction

(3) Combined models based on neural networks, such as the patented "Landslide Displacement Prediction Method Based on Intuitionistic Fuzzy Density PSO-LSTM (CN202110244201.6)", by optimizing the structural parameters of the neural network with the help of optimization algorithms, the engineering conditions can be obtained Optimize the model to further improve the prediction accuracy of the neural network, but the training time of the model is further increased, and the data volume requirements for the training set are further increased

However, in the actual application process, the Raida criterion is difficult to satisfy the assumption that the data requires a normal distribution; the boxplot method has limitations to a certain extent for the data whose median is unreliable for the overall expression of the data; The network method requires a large number of training data sets as algorithm support and other factors, which limit the practical application of the above method in engineering

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