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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/147073


    Title: 以長短期記憶模型分析及預測房價指數
    Long Short-Term Memory Analyses of House Price Index
    Authors: 洪丞佑
    Hong, Cheng-You
    Contributors: 何靜嫺
    洪丞佑
    Hong, Cheng-You
    Keywords: LSTM
    機器學習
    理論啟發的機器學習
    房價指數
    股票加權指數的交互作用
    房地產市場上的情緒指標
    LSTM
    Machine Learning
    Theory-inspired Machine Learning
    House Price Index
    Stock Index’s Interaction
    Emotional Indicator On The Real Estate Market
    Date: 2023
    Issue Date: 2023-09-01 15:34:30 (UTC+8)
    Abstract: 房市一直是個很熱門的話題,在預測房價指數上多數是傳統理論與機器學習各自分開進行的。在台灣,很少有將傳統理論與機器學習結合使用,進行房價指數的預測。本篇論文探討了LSTM中特徵縮放的選擇,並建構理論啟發的LSTM,將其與LSTM與VAR模型進行比較。另外,在資料的選擇上,我們也考慮了一般民眾、投資客等對房市的情緒指標,並使其成為其中一個解釋變數。我們的研究表明,第一,在我們的數據集中,當出現異常值是之前的房價指數,預測上將會出現延遲問題,此時選擇StandardScaler可能是一個不錯的選擇。第二,在理論啟發的LSTM中,我們透過更清晰的區分短期與長期影響,可以達到類似StandardScaler的效果,使得使用MinmaxScaler的LSTM的延遲問題與準確度將得到部分改善。第三,我們的結果表明,我們的情緒指標會有效的影響房價指數,因此應該作為衡量房地產市場情緒的重要指標。
    The housing market has always been a hot topic, and when it comes to predicting house price index, most approaches involve separate applications of traditional theories and machine learning. In Taiwan, there are few attempts to combine traditional theories and machine learning to predict house price index. This paper explores the choice of feature scaling in LSTM and constructs a theory-inspired LSTM, which is compared with LSTM and VAR models in predicting house price index. In addition, in data selection, we also considered sentiment or emotional indicators for the housing market among the general public, investors, and others, and included it as one of the explanatory variables. Our results first show that, in our dataset, when there are outliers in previous house price index, there may be a delay problem in prediction. In such cases, choosing StandardScaler may be a good option. Second, in the theory-inspired LSTM, we achieve a similar effect to StandardScaler by clearly distinguishing between short-term and long-term influences. This can partially improve the delay issue and accuracy of using MinmaxScaler in LSTM. Third, our results indicate that our emotional indicator has a significant impact on house price index and should be considered an important measure of sentimental or emotional motivation in the real estate market.
    Reference: 陳明吉與曾琬婷 (2008),「台灣不動產市場從眾行為之檢視」,《管理與系統》,15,591-615。
    黃偉德(2021)。台灣房地產市場輿論與從眾行為之房價泡沫分析。碩士論文。國立清華大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/g5vmyd。
    楊長霖(2017)。深度學習於台灣房價指數趨勢預測模式建立之研究-應用NNLSTM演算法。﹝碩士論文。國立臺灣科技大學臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/4v83d4。
    戴梓栩(2016)。總體經濟變數對臺灣房地產市場之影響。﹝碩士論文。國立中山大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/w2j863。
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    Description: 碩士
    國立政治大學
    經濟學系
    110258042
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110258042
    Data Type: thesis
    Appears in Collections:[經濟學系] 學位論文

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