A common feature-driven prediction model for multivariate time series data.

• Existing time series forecasting models make it difficult to extract multiple common features in the data, such as sudden irregular fluctuation trends, trend features with large fluctuation amplitudes, and factors such as long-term dependencies in the time series. To solve this problem, a heterogeneous model NeA3L that considers the common features of time series is proposed. • The proposed model realizes multiple independent parallel feature extraction modules by combining multi-task learning, which effectively captures the common features of multivariate time series the long-term dependency features in the time series and enhances the model's ability to extract common features of time series. The model realizes the feature fusion of multivariate time series by combining the attention mechanism. By designing a deep iterative LSTM network, the model handles sudden irregular fluctuation regular features. • Comparing with six state-of-the-art time series forecasting models and three traditional forecasting models on four different domain public datasets, the model outperforms nine benchmark models in all forecasting indexes, indicating that the model in this paper has the ability of universal applicability in the field of multivariate time series forecasting, which is of great significance for optimizing intelligent management and decision-making. Multivariate time series data contain a variety of common features that are difficult to extract, among which the sudden irregular fluctuation trend, the trend feature of large fluctuation amplitude, and the long-term dependency relationship in the time series have an important impact on the accuracy of the prediction model. To predict non-stationary trends in large-scale data accurately using the common characteristics of multivariate time series. A novel generalised forecasting model NeA3L based on common features of time series is developed. The NeA3L model utilizes multiple independent parallel feature extraction modules to obtain the common features of multivariate time series. It utilizes the three-layer iterative structure to deal with sudden irregular fluctuation patterns. NeA3L optimizes the network structure to realize the heterogeneous codec structure. It applies the attention mechanism between the encoder and the decoder to accomplish the multivariate prediction of the multivariate time series, which has good stability for predicting various types of multivariate data. A comparison of the NeA3L model with nine current time series prediction methods on four publicly available datasets shows that the NeA3L model outperforms the current methods in various evaluation metrics. The RMSE is improved by 2.3 %, 26.5 %, 28.9 %, and 20.84 % on average, respectively. The NeA3L model can be universally applicable to the field of multivariate time series prediction, which is important for optimizing the intelligent management and decision-making. [ABSTRACT FROM AUTHOR]