  Since the chemical industry index is one of the important indicators to
measure the development of the chemical industry, forecasting it is critical
for understanding the economic situation and trends of the industry. Taking the
multivariable nonstationary series-synthetic material index as the main
research object, this paper proposes a new prediction model: DeepVARMA, and its
variants Deep-VARMA-re and DeepVARMA-en, which combine LSTM and VARMAX models.
The new model firstly uses the deep learning model such as the LSTM remove the
trends of the target time series and also learn the representation of
endogenous variables, and then uses the VARMAX model to predict the detrended
target time series with the embeddings of endogenous variables, and finally
combines the trend learned by the LSTM and dependency learned by the VARMAX
model to obtain the final predictive values. The experimental results show that
(1) the new model achieves the best prediction accuracy by combining the LSTM
encoding of the exogenous variables and the VARMAX model. (2) In multivariate
non-stationary series prediction, DeepVARMA uses a phased processing strategy
to show higher adaptability and accuracy compared to the traditional VARMA
model as well as the machine learning models LSTM, RF and XGBoost. (3) Compared
with smooth sequence prediction, the traditional VARMA and VARMAX models
fluctuate more in predicting non-smooth sequences, while DeepVARMA shows more
flexibility and robustness. This study provides more accurate tools and methods
for future development and scientific decision-making in the chemical industry.
