Research on the Construction and Application of Macroeconomic Forecasting Model Based on Time Series Cluster Analysis

Aiming at the problem of large prediction error caused by the complex background of macroeconomic prediction, this paper proposes a macroeconomic prediction model based on time series clustering. The model adopts sparse self-encoder to deeply mine the features of the input vectors, constructs a bidirectional threshold cyclic unit network, and predicts the preliminary trend of the macroeconomy, and proposes a time series deep clustering algorithm that integrates the multi-scale feature extraction and clustering objectives of time series data into the same network. A sample generation strategy based on data augmentation and a multiclassification assistance module are used to extract the invariant patterns contained in the time series data to obtain a better representation for targeting time series clustering. Comparing this paper’s model with different forecasting models, the RMSE metrics are 0.0038 and 0.003 for the two time horizons, which are better than the other two models. The prediction range of this paper’s model for future GDP is 5.8%-5.9%, which is smaller than the GDP prediction range of the ARIMA model, indicating that this paper’s model is suitable for the realistic application of macroeconomic forecasting.