Abstract
The fuzzy time series (FTS) model is widely used to forecast time series data. However, the predicted results of FTS are poor for industrial time series data, especially when data changes rapidly and its volume is enormous. Therefore, a dynamic soft sensor model is proposed based on propositional linear temporal logic (PLTL) with a sliding window. First, the sliding window is used to extract dynamic data. Then the extracted data is modeled by FTS to generate an initial forecasting result. Finally, according to the data in the window, a PLTL formula is generated to describe the trend of the data. The generated formula is used as a formal label of the data in the window to weight the initial forecasting result. The proposed method is verified with the TAIEX data set. Analysis of variance is used to test the significance of selected data sets. The experimental results prove that the new method has good regression forecasting performance. Finally, an example of industrial application is introduced. The experimental results demonstrate the effectiveness of the model for industrial time series data.
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