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Title: 透過時間序列的波動特徵分群協助資料分類 -以公司危機事件為例
Achieve Efficient Data Classification by Time Series Wave Decomposition Pattern Clustering: Financial Distress as an Example
Authors: 陳郁婷
Chen, Yu Ting
Contributors: 胡毓忠
Hu,Yuh Jong
Chen,Yu Ting
Keywords: R
Time Series
financial distress
Date: 2016
Issue Date: 2016-09-02 01:32:04 (UTC+8)
Abstract: 本研究透過時間序列拆解方法分析股價報酬率因數,取出趨勢波動特徵進行分群演算,將分群結果視為特徵值,進行更進一步資料分類。時間序列波形特徵,可對該序列做未來趨勢預測。本研究則將趨勢波形做為資料分群的特徵值,藉以輔助分類。本研究案例為財務危機公司,區分具實質財務危機或非實質財務危機,並整合公司其它財務與非財務相關分析。使用R 語言時間序列拆解工具找出趨勢波形並進行分群。採用Spark平行化計算架構的節點擴充運算能力與叢集式容錯處理以及RDD 的高效能運算。本研究並採用隨機決策森林的組合式(Ensemble)學習演算法進行公司危機型態的分類預測系統實驗。
The purpose of the study was to analyze rate of return factor by Time Series Wave Decomposition, to take Trend wave features to proceed clustering, then taking the clustering result as feature to achieve efficient data classification. Time Series Wave feature can be a predictor for future trend; however, this study took Time Series Wave as a classification feature and took Financial Distress company as an example to distinguish the financial distress to integrate relative financial analysis factors. Adopting Spark process data in parallel in standalone cluster mode with Resilient Distributed Dataset (RDD) to improve the computing performance. The study adopted random forest ensemble machine learning to proceed financial distress company classification prediction.
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