025-12 のバックアップ(No.2)

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• 025-12 へ行く。
  • 1 (2020-08-24 (月) 14:44:36)
  • 2 (2020-08-24 (月) 16:09:49)
  • 3 (2020-10-07 (水) 08:15:24)

第25回研究会

評価関数の可視化による株価予測モデルの汎用性評価 †

著者 †

坂下好希(東京大学), 中井悠貴(早稲田大学), 瀬之口潤輔(東京工科大学)

概要 †

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 †

t-SNE, cost function, visualization, versatility evaluation, wavelet, reducing dimension

論文 †

(10月7日以降に公表いたします)