邏輯回歸及二因子時間模糊序列模式用以預測天氣｜國立臺灣科技大學博碩士論文系統

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研究生：	林政勳 Zheng-Xun Lin
論文名稱：	邏輯回歸及二因子時間模糊序列模式用以預測天氣 Temperature Prediction Using Logistic Regression and Two-factor Fuzzy Time Series Model
指導教授：	蘇順豐 Shun-Feng Su
口試委員:	莊鎮嘉 none 李仁鐘 none 王乃堅 none
學位類別：	碩士 Master
系所名稱：	電資學院 - 電機工程系 Department of Electrical Engineering
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	49
中文關鍵詞：	模糊時間序列、邏輯斯回歸、學習式向量量化、預測、氣溫
外文關鍵詞：	fuzzy time series, logistic regression, learning vector quantization, prediction, temperature
相關次數：	點閱：246 下載：1
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本研究提出了一個新的氣溫預測方法，該方法將學習的方法加入到了[1]中
所提出的二因子時間模糊序列的模型中，並利用氣溫值以及雲密度做為該方法中
的兩個因子。本研究假設了現在的天氣與過去的天氣有一定的相似關係，若在此
情況下，加入過去的歷史資料進行學習，有助於提升預測的正確率。在模糊時間
序列的模型提出以後，許多研究者以此為基礎提出新的模型，並應用在各個不同
的領域上，而模糊時間序列的預測模型，主要的步驟可以分成論域的定義及切割、
模糊集合的定義、建立模糊邏輯關係和解模糊化。本研究結合了模糊時間序列以
及一個監督式學習的分類方法，邏輯斯回歸，此方法先從歷史資料中得出模糊邏
輯關係，再將模糊邏輯關係做為訓練集合，利用此訓練集合及邏輯斯回歸分類方
法建立一個分類器模型。而在本研究中，邏輯斯回歸的方法將與學習式向量量化
法以及在[1]中的方法進行比較，比較過後，邏輯斯回歸方法可得到較好的正確
率及穩定性。

In this study, a novel forecasting method for the temperature is proposed. The
proposed method combines a learning idea into the two-factor fuzzy time series model
proposed in the literature [1]. The considered factors for temperature prediction are
two factors, the values of temperature and the cloud density. The idea is that when
more historical data are used, the accuracy of forecasting model may increase. It is
assumed that the weather temperatures similar to past temperature histories. After the
fuzzy time series for prediction was proposed, based on it, many researchers proposed
different models used in many various fields. The important ingredients of fuzzy time
series forecasting model are definition and partition of the universe of discourse,
definition of fuzzy sets, establishment of fuzzy logical relationship and
defuzzification. The proposed model combine the fuzzy time series with the method
of classification of Logistic Regression (LR). The method of LR is to extract fuzzy
logical relationships from historical data and can be regarded as a learning mechanism.
From this study, it can be found that the method of LR can get more accuracy and
stability than LVQ and another method in [1].

中文摘要......................................................................................................................... I
Abstract .......................................................................................................................... II
致謝............................................................................................................................... III
Contents ........................................................................................................................IV
Figure list ......................................................................................................................VI
Table list .......................................................................................................................VII
Chapter 1 Introduction ................................................................................................ 1
1.1 Background and Motivation ...................................................................... 1
1.2 Objective .................................................................................................... 3
1.3 Organization of the Thesis ......................................................................... 4
Chapter 2 Literature Review....................................................................................... 5
2.1 Concepts of Fuzzy Time Series ................................................................. 5
2.2 Forecasting Models of Fuzzy Time Series................................................. 6
2.3 Algorithm B* from Temperature Prediction Using Fuzzy Time Series..... 7
Chapter 3 Model Development................................................................................. 12
3.1 Define and Partition Universe of Discourse ............................................ 12
3.2 Define Fuzzy Sets and Fuzzification ....................................................... 13
3.3 Forecasting ............................................................................................... 15
3.3.1 Logistic Regression Algorithm .................................................... 15
3.3.2 Learning Vector Quantization Algorithm ..................................... 19
3.3.3 Logistic Regression and Learning Vector Quantization Algorithm
for Forecasting ............................................................................................. 21
3.4 Defuzzification ......................................................................................... 23
V
3.5 Calculate the Forecasted Data .................................................................. 24
Chapter 4 Experiment and Evaluation ...................................................................... 25
4.1 Experiment on Proposed Model............................................................... 25
4.2 Evaluation and Comparisons ................................................................... 38
Chapter 5 Conclusion and Future Work ................................................................... 46
5.1 Conclusions .............................................................................................. 46
5.2 Future Work ............................................................................................. 47
References .................................................................................................................... 48

                                

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