GA-WOA-LSTMに基づく株式市場予測調査
1School of Science, Shenyang University of Chemical Technology, Shenyang, Liaoning, China.
PloS one
|August 27, 2025
まとめ
この研究では,遺伝子アルゴリズム (GA),クジラ最適化アルゴリズム (WOA),および長期短期記憶 (LSTM) を組み合わせたハイブリッドモデルを導入し,株式市場予測を改善しました. GA-WOA-LSTMモデルは,金融時系列の予測精度を高めています.
科学分野:
- 金融予測とタイムシリーズ分析
- コンピューティング・インテリジェンスと 機械学習のアプリケーション
- 経済モデリングと市場規制
背景:
- グローバル金融市場はますます複雑になり,投資,規制,計画のための正確な株式市場予測が必要になります.
- 伝統的な予測モデルは,非線形的な依存関係と金融時系列データに固有の長期パターンで苦労します.
研究 の 目的:
- 改善された株式市場予測のための新しいハイブリッド予測モデルGA-WOA-LSTMを提案し,評価する.
- 遺伝子アルゴリズム (GA),クジラ最適化アルゴリズム (WOA),長期短期記憶 (LSTM) の強みを活用し,優れた予測性能を実現する.
主な方法:
- グローバルハイパーパラメータ最適化のためのGA,ローカル検索精製のためのWOA,タイムシリーズモデリングのためのLSTMを統合したハイブリッドモデル.
- LSTMニューラルネットワークは,非線形依存性と長期的なパターンを捕捉する能力のために利用されました.
- モデルの性能は,トレーニングおよびテストデータセットの平均絶対誤差 (MAE),平均絶対パーセント誤差 (MAPE),平方根平均誤差 (RMSE) およびR2を使用して評価されました.
主要な成果:
- GA-WOA-LSTMモデルは,従来のベースラインモデルと比較して,予測精度が著しく高かった.
- 提案されたモデルは,トレーニングデータセットとテストデータセットの両方に優れた汎用性を示した.
- 主要なパフォーマンス指標 (MAE,MAPE,RMSE,R2) はハイブリッドアプローチの有効性を示した.
結論:
- GA-WOA-LSTMモデルは,金融時系列予測のための堅実で効果的な戦略を提供します.
- この研究は現実世界の金融市場での実用的な応用のための貴重な洞察を提供します.
- ディープラーニングと最適化アルゴリズムの統合により,株式市場の予測の正確性と信頼性が向上します.
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