A recurrent neural network based on attention mechanism to predict the trend of univariate time series

摘要

For the time series with high acquisition frequency and high noise, it is difficult to establish the prediction model directly. If we simply take their average values, we will lose a lot of trend information. Therefore, we studied how to accurately obtain the trend information of the time series and establish its accurate prediction model, and proposed a prediction model based on K-means clustering. The first step of the model is to obtain the trend information of the original time series based on the K-means clustering idea, and the second step is to use the gated recurrent unit based on the attention mechanism to establish a prediction model for the trend information. Experiments on three dataset show that the proposed K-means clustering method can effectively reduce noise interference and accurately obtain trend information. Comparative experiments on different prediction models show that our proposed prediction model has the best prediction accuracy.