025-12

2020-10-07 (水) 08:15:33 (58d) | Topic path: Top / 025-12

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), 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

file12_SIG-FIN-25.pdf

添付ファイル: file12_SIG-FIN-25.pdf 140件 [詳細]
トップ   編集 凍結 差分 バックアップ 添付 複製 名前変更 リロード   新規 一覧 単語検索 最終更新   ヘルプ   最終更新のRSS