Adaptive updating method applied to time sequence LSTM parameter prediction model
A forecasting model and time series technology, applied in forecasting, data processing applications, biological neural network models, etc., can solve problems such as the average forecast error exceeding the expected value and the decline of model forecasting accuracy.
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[0034] The invention designs an adaptive update method applied to the time series LSTM parameter prediction model, provides an update strategy for the automatic update of the prediction model, not only can use the standby prediction model to update the prediction model in time, but also can use the dynamic The adjustment coefficient improves the prediction accuracy of the parameters of the prediction model. The flowchart of the adaptive update method of the LSTM parameter prediction model is as follows figure 1 The specific operation steps are as follows:
[0035] Step 1: Use the parameter sample data (X 1 , X 2 ,...X num ) as the modeling data, find the mean of the modeling data As shown in formula (1);
[0036]
[0037] Step 2: Normalize the modeling data, as shown in formula (2) and formula (3), where is the mean of the modeling data described in step 1, S is the standard deviation, and the normalized data is used as the modeling data num is the amount of model...
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