本論文聚焦於金融時間序列資料的異常檢測，並以此作為股票進出場策略的基礎。傳統的股價預測方法如LSTM模型在實際交易中容易因市場波動而頻繁觸發買賣信號，導致交易成本高昂和潛在虧損。本研究採用TimesNet模型，利用快速傅立葉轉換將一維資料轉換成二維，再經由卷積神經網路進行特徵提取，最終轉換回一維資料以保留時間和頻率等級資訊，透過這一編碼器解碼器之深度學習架構計算重構誤差以判斷是否出現異常上漲或下跌的波段。在標籤方面本研究採用了一種標記時間連續上漲或下跌趨勢的新方法，提高了模型的預測績效和交易策略的實用性。通過對19檔台灣股票的回測分析，我們發現其中有11檔股票實現了正報酬，證明了該模型具有一定的獲利能力。結果指出，TimesNet模型能夠在一定程度上預測異常交易點，搭配後續的交易策略能夠為投資者提供更加穩健的當沖方式，展示了深度學習技術在金融市場預測中的應用潛力。未來的研究將著重於擴展數據來源和豐富特徵及增加模型的複雜度，以提高預測的準確性和穩定性，進一步優化模型並結合其他技術指標和市場分析，為投資者提供更精確的交易決策參考。

This paper focuses on anomaly detection in financial time series data as a basis for stock trading long and short strategies. Traditional stock price prediction methods, such as LSTM models, are prone to frequent buy and sell signals due to market volatility in actual trading, leading to high transaction costs and potential losses. This study adopts the TimesNet model, which uses the Fast Fourier Transform to convert one-dimensional data into two-dimensional data, then employs a convolutional neural network for feature extraction, and finally transforms it back to one-dimensional data to retain time and frequency-level information. This encoder-decoder deep learning architecture calculates reconstruction errors to determine whether there are abnormal rising or falling segments. The study introduces a new method for labeling continuous rising or falling trends over time, improving the model's predictive performance and the practicality of trading strategies. Through backtesting analysis of 19 Taiwanese stocks, we found that 11 stocks achieved positive returns, demonstrating the model's profitability. The results indicate that the TimesNet model can predict abnormal trading points to a certain extent, and coupled with subsequent trading strategies, it can provide investors with a more stable day trading approach, showcasing the application potential of deep learning technology in financial market forecasting. Future research will focus on expanding data sources, enriching features, and increasing model complexity to improve prediction accuracy and stability, further optimizing the model and integrating other technical indicators and market analysis to provide more precise trading decision references for investors.

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