## 1 Introduction  
Financial time-series forecasting predicts the future movements of financial markets using time-series data and statistical methods. It can be applied to forecasting in economic areas such as stock prices, currency exchange rates, bond yields, etc. Such forecasting can help investors and financial institutions make more informed investment decisions. Generally, the primary methods for making time-series forecasts in finance are time-series and machine-learning models. Time-series models include the ARIMA [1], VAR [2] and GARCH [3] models. These models can predict future trends based on the direction and pattern of historical data. Machine learning models include BP neural networks [4], support vector machines [5], random forests [6], etc. These models can learn features and practices to predict future trends through training data. However, hybrid models combine multiple methods, such as time-series and machine learning models, and thus have the advantage of combining both benefits to improve their forecasting accuracy. Data sources for financial time-series forecasting include historical market data, economic data, policy data, etc. To improve the forecasting accuracy, a series of tasks such as data cleaning, feature selection, model training and evaluation is sometimes required [7]. Meanwhile, due to the complexity and volatility of financial markets, financial time-series forecasting faces many challenges, such as data quality problems--historical data may have missing values, outliers and other issues, which require data cleaning and pre-processing; model selection problems--selecting appropriate models and algorithms requires the consideration of data features, forecasting objectives and other factors; overfitting problems--in financial time-series forecasting, there is often the problem of sample data overfitting; model evaluation problems--for different prediction models, it is necessary to make a reasonable evaluation and comparison and choose the optimal model; and forecast error problems--changes in financial markets. Uncertainty makes forecasting errors unavoidable, so it is necessary to optimize models and algorithms to continuously improve forecasting accuracy. To better solve the above problems, this paper proposes a financial time-series prediction model based on multiplex attention and a linear transformer based on a transformer [8] that can use distributed CPU for parallel training to improve the training efficiency of the model. When analyzing and predicting longer time series, the semantic association of capturing more prolonged problems is better, and after adding the attention mechanism, the prediction accuracy of the model is higher and the inference speed is faster. Therefore, we use a linear transformer to reduce the complexity of the original transformer and, at the same time, introduce a multiplexed attention mechanism to increase the model's prediction accuracy. The results show that the proposed method has better results in the financial prediction of long time series, which has promising implications for economic time-series prediction. The contribution points of this paper are as follows. * In this paper, a new transformer model is used to solve the problem of financial time-series forecasting, which is highly innovative, while giving full play to the advantages of the transformer model, simplifying it and introducing the attention mechanism so that it can better solve the problem of financial time-series forecasting. * Compared with the traditional ARIMA, VAR, GARCH model and other models, it can more accurately predict long time-series data and improve the prediction accuracy. At the same time, it can solve the problem that historical data may have missing values and outliers. * Compared with machine learning models such as neural networks, support vector machines, or hybrid models such as CNN-LSTM models [9], the proposed method can be trained faster and more efficiently. They can perform more experiments and data analysis and prediction.