A Type 2 fuzzy time series model for stock index forecasting
By K. Huarng, Hui-Kuang Yu. 2005
250 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/6ccb5aac7e9b1fb70750e75d8ade2f23766560c6
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In Economics and Econometrics
In Management Science and Operations Research
Citations in the corpus
- Application of data mining techniques in stock markets: A survey (2010, methodology): “But their method requires extra observations to form type-2 fuzzy relations for each observation and thus require larger number of data than the conventional type-I methods (Huarng and Yu, 2005).”
- Ratio-based lengths of intervals to improve fuzzy time series forecasting (2006, methodology): “As a result, fuzzy time series models seem to be more appropriate when attempting to model these types of problems [12].”
- Financial time series forecasting with machine learning techniques: a survey (2010, unknown): “Time-frame Number Publications Day 31 [1], [2], [3], [4], [6], [7], [8], [9], [10], [13], [14], [17], [19], [20], [21], [22], [24], [27], [28], [31], [32], [33], [34], [35], [36], [37], [40], [41], [42], [44], [45] Week 3 [18], [23], [43] Month 3 [26], [38], [39] Multiple / Other 9 [5], [11], [12], ”