過去傳統計量模型使用的模型時，需要先對變數之間的關係，有著深厚的經 驗，了解不同變數之間的因果關係，並且傳統計量模型在預測的時候，大多會 忽略時間序列中間隔很久的重要事件，只考慮到短天期的變數變動情形。
為了可以充分利用多變量時間序列中有用的資訊，並且進一步提高對於預 測台灣加權股價指數的準確度，本文使用深度學習中的循環神經網路，對股價 指數進行多變量的預測，希望能從眾多變數中提取出有效的資訊，並且解決過 去時間序列中忽略間隔較久的重要事件。
台灣加權股價指數被選為本次研究的對象，並且選取循環神經網路以及向 量自我迴歸共 2 個模型，分別使用這兩個模型對股價指數進行預測，並比較這 兩者在預測指數上的表現。最後結果顯示，在我們過去歷史資料中，循環神經 網路的準確度明顯優於傳統的向量自我迴歸模型。

In the past, the models used in traditional financial econometric models need to have a deep experience in the relationship between variables, and understand the causal relationship between different variables. In traditional financial econometric models, most of them ignore long time distance or delays in time series, just focus on the variable change in short period.
In order to make the most information in multivariate time series and to further improve the accuracy of forecasting Taiwan's weighted stock price index, this paper uses the recurrent neural network in deep learning to perform multivariate forecasting on the stock price index in hopes of being able to extracts valid information from variable and resolves important events that were ignored at long time distance in past time series.
The Taiwanese weighted stock price index was selected as the subject of this study, and two models of recurrent neural network and vector autoregression were selected. These two models were used to predict the stock price index and compare the two in the forecast index. which performed. The final result shows that in our past historical data, the accuracy of the recurrent neural network is significantly better than the traditional Vector Autoregression model.

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