以往針對匯率的預測，傳統的計量方法會將資料本身的歷史資訊以線性估計方式建模。但隨著各國外匯交易往來頻繁，影響我國外匯價格的因素日趨複雜，線性估計模型的預測誤差亦不斷擴大，因此本文採用兩種不同的神經網路模型來預測我國外匯價格，參考 Bao et al.(2017) 所提出循環性類神經網路模型，藉由模型非線性估計的方法與自編碼器的降噪方法來達到更好的預測效果。
為進行有效的比較，本文比較傳統的Autoregressive Distributed Lag Model 計量模型與兩種神經網路模型架構類神經網路模型 (Fully Connected Neural Network) 與堆疊式自編碼器 (Stacked Autoencoder) 搭配循環神經網絡 (Recurrent Neural Network)，提供有系統的變數選擇，資料預先處理，資料轉換，模型建構，參數調整優化與樣本外預測評估。評估的方法採均方誤差來衡量模型樣本內與樣本外預測的優劣，接著本文分別估計上述三種模型 1 日、7 日、30 日短中期的預測結果並將其與隨機漫步模型比較。
結果顯示神經網路模型於樣本外預測皆優於於隨機漫步模型。另外自編碼器搭配循環神經網路模型以其優異的訊息傳遞與資訊降噪能力，更是在 7 日與 30 日的預測結果上遠優於其他模型。

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