| Topic path: Top / 025-12


* 評価関数の可視化による株価予測モデルの汎用性評価 [#h9dd88fc]

** 著者 [#q4343a8f]
坂下好希(東京大学), 瀬之口潤輔(東京工科大学)

**概要 [#u6e5d008]
When predicting stock prices with a complex model using machine learning or artificial intelligence, overfitting sometimes occurs, and the prediction accuracy expected in actual operation cannot be obtained. In such a model, the cost function is presumed to be steep and multi-modal, while in a model that maintains stable prediction results, the cost function is considered to be gradual and single-peaked. In this study, we first compared the performance of several stock price prediction models, and then visualized the cost function for each model using t-SNE. As a result, the model using Lasso regression, which had the highest performance, showed a gradual unimodal cost function, while the linear regression, which had relatively low performance, showed a steep and multi-modal shape. Visualizing the cost function using t-SNE can be an important index for evaluating the stability and versatility of a stock price prediction model.

**キーワード [#yaebe5a6]
t-SNE, cost function, visualization, versatility evaluation, wavelet, reducing dimension

**論文 [#b23ad785]

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