臺灣博碩士論文加值系統

With the fast growth of information technology, the issue of how to predict through scientific computation and information analysis becomes crucial. In addition, more accurate and efficient forecast can support the decision-making. In this study, we propose a two-factor time-invariant forecasting model, which is more efficient and can controll uncertainty. Moreover, concerning the affect of the interval partitioning, we combine the forecasting model with fuzzy c-means algorithm to fuzzify the historical data. The data of daily average temperature and average cloud density from June to September, 1996 in Taipei are experimented for performance evaluation. A simple Monte Carlo simulation is performed to achieve the true performance of the model approximately. The proposed model achieves better forecasting performance when being compared in both modeling and forecasting accuracy with other extant researches.

ACKNOWLEGEMENT II
LIST OF TABLES V
LIST OF FIGURES VI
CHAPTER I INTRODUCTION 1
1.1 BACKGROUND AND MOTIVATION 1
1.2 OBJECTIVES 2
1.3 THESIS ORGANIZATION 3
CHAPTER II LITERATURE REVIEW 5
2.1 FUZZY THEORY 5
2.1.1 Fuzzy Set 5
2.1.2 Membership Function 6
2.2 FUZZY TIME SERIES 7
2.2.1 Fuzzy Relation 7
2.3 FUZZY TIME SERIES FORECASTING MODELS 10
2.3.1 Time-variant Models 11
2.3.2 Time-invariant Models 12
2.4 INTERVAL PARTITIONING 20
2.5 SUMMARY 21
CHAPTER III MODEL DEVELOPMENT 22
3.1 PARTITIONING WITH FUZZY C-MEANS ALGORITHM 23
3.2 DEFINE FUZZY SETS AND FUZZIFICATION 25
3.3 DETERMINISTIC FORECASTING ALGORITHM 26
3.4 DEFUZZIFICATION 28
3.5 A TWO-FACTOR EXPERIMENT OF WEATHER 30
CHAPTER IV EXPERIMENTATION AND PERFORMANCE ANALYSIS 35
4.1 MONTE CARLO SIMULATION 35
4.2 PERFORMANCE EVALUATION AND COMPARISONS 37
4.2.1 Modeling Accuracy 37
4.2.2 Forecasting Accuracy 40
4.3 MODEL EFFICIENCY 42
4.4 TAIFEX EXPERIMENT 44
CHAPTER V CONCLUSIONS AND FUTURE WORK 47
5.1 CONCLUSIONS 47
5.2 FUTURE WORK 48
REFERENCES 49

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