デンマークの観光業界で採用された一般的線形回帰GARMAモデル
Hongxuan Yan1, Xingyu Yan2,3, Luoyi Sun2
1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, China.
PloS one
|August 22, 2025
まとめ
この研究は,高度な統計モデルを使用して,観光データにおける季節的パターンを明らかにしています. 一般化された線形回帰GARMA (GLRGARMA) モデルは,観光の予測を改善するために,長期記憶の特徴を最もよく捉えます.
科学分野:
- タイムシリーズ分析
- 観光経済
- 統計モデリング
背景:
- 観光データにはしばしば複雑な季節性と長期記憶効果があります.
- これらのパターンを理解することは,正確な予測と資源管理に不可欠です.
- 既存のモデルは,観光の微妙な時間的動態を完全に捉えることができないかもしれません.
研究 の 目的:
- 観光のタイムシリーズデータにおける季節性特性を調査する.
- 長期記憶と季節的な特徴を捉えるための高度な統計モデルを提案し評価する.
- 観光データ分析と予測の最適モデルを特定する.
主な方法:
- デンマークの観光データ分析,ホテルの部屋賃貸に焦点を当てた.
- 長期記憶を特定するために,自動相関関数 (ACF) と周期図グラフを使用します.
- 汎用線形回帰GARMA (GLRGARMA) とSARMA (GLRSARMA) のモデルを開発し比較する.
- モデルの柔軟性を高めるため,一般 Poisson (GP) の分布を組み込む.
- サンプル内とサンプル外での予測のためのベイジアンアプローチを使用します.
主要な成果:
- ゲゲンバウアの長期記憶と季節的な特徴は,観光データで明確に識別されました.
- GLRGARMAモデルは,ゲゲンゲンバウアーロングメモリでタイムシリーズの処理において優れた性能を示した.
- 周期的なスポンジ効果を持つ説明変数を加えたことで,モデルの性能が著しく改善されました.
- モデル選択基準は,GLRGARMAモデルの優位性を確認しました.
結論:
- GLRGARMAモデルは,ゲゲンゲンバウアーロングメモリで観光のタイムシリーズデータを分析するのに非常に有効です.
- 季節性と長期記憶の正確なモデル化は,強力な観光予測に不可欠です.
- 周期的な効果を持つ説明変数は,観光モデルの予測力を大幅に高めることができます.
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