資料增強是深度學習中的關鍵技術和議題。深度學習依賴大量且多樣化的資料來訓練模型，透過人工技術對原始資料進行微幅變化以增加資料量是常用的方法。在時間序列資料方面，資料增強同樣適用，但針對時間序列的資料增強技術目前並不常見，本文嘗試將股票資訊作為時間序列數據，使用時間序列生成模型（TSGM）進行數據增強，並對幾種常見的時間序列數據增強方法進行了比較。我們利用長短期記憶網路、卷積神經網路和孿生對比學習進行預測，在比較它們的結果後，我們發現以股票走勢預測方面來說，對比學習的效果相對突出，而且每種模型在經過資料增強後，預測表現都有所提升。

Data augmentation is a key technique and topic in deep learning. Deep learning relies on large and diverse datasets to train models. Using manual techniques to slightly alter the original data to increase its size is a common approach. In time series data, data augmentation is also applicable, although it's not widely used. In this article, we used stock information as time series data and applied time series generative modeling (TSGM) for data augmentation. We compared several common time series data augmentation methods. To make predictions, we used long short-term memory network (LSTM), convolutional neural network (CNN) and Siamese contrastive learning. After comparing their results, we found that, for stock trend prediction, contrastive learning was relatively effective. Every model showed improved prediction performance after data augmentation.

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