Journal ArticleDOI
Fuzzy based trend mapping and forecasting for time series data
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TLDR
The study demonstrates the superiority of fuzzy based methods for non-stationary, non-linear time series with improved prediction with lesser MAPE (mean average percentage error) for all the series tested.Abstract:
The study demonstrates the superiority of fuzzy based methods for non-stationary, non-linear time series. Study is based on unequal length fuzzy sets and uses IF-THEN based fuzzy rules to capture the trend prevailing in the series. The proposed model not only predicts the value but can also identify the transition points where the series may change its shape and is ready to include subject expert's opinion to forecast. The series is tested on three different types of data: enrolment for Alabama university, sales volume of a chemical company and Gross domestic capital of India: the growth curve. The model is tested on both kind of series: with and without outliers. The proposed model provides an improved prediction with lesser MAPE (mean average percentage error) for all the series tested.read more
Citations
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Proceedings ArticleDOI
A survey on forecasting of time series data
TL;DR: A detailed survey of the various techniques applied for forecasting different types of time series dataset is provided and gives the reader an idea about the various researches that take place within forecasting using the time series data.
Journal ArticleDOI
High-order fuzzy-neuro expert system for time series forecasting
Pritpal Singh,Bhogeswar Borah +1 more
TL;DR: A new model based on hybridization of fuzzy time series theory with artificial neural network (ANN) is presented, which is validated by forecasting the stock exchange price in advance and uses the high-order fuzzy relationships in order to obtain more accurate forecasting results.
Journal ArticleDOI
Designing fuzzy time series forecasting models: A survey
Mahua Bose,Kalyani Mali +1 more
TL;DR: This paper summarizes and reviews past twenty five year's contribution in the area of Fuzzy time series forecasting and highlights the papers published in different journals of Elsevier during 1993-2018.
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A brief review of modeling approaches based on fuzzy time series
TL;DR: This article reviews and summarizes previous research works in the FTS modeling approach from the period 1993–2013 (June), and provides a brief introduction to SC techniques.
Journal ArticleDOI
Probabilistic Forecasting of Sensory Data With Generative Adversarial Networks – ForGAN
TL;DR: This work argues how to evaluate ForGAN in opposition to regression methods, and investigates probabilistic forecasting of ForGAN, which utilizes the power of the conditional generative adversarial network to learn the data generating distribution and compute Probabilistic forecasts from it.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI
Forecasting enrollments with fuzzy time series—part II
Qiang Song,Brad S. Chissom +1 more
TL;DR: The forecast of the enrollments of the University of Alabama is carried out and a fuzzy time series model is developed using historical data, which is tested on the basis of its robustness andvantages and problems.
Journal ArticleDOI
Fuzzy time series and its models
Qiang Song,Brad S. Chissom +1 more
TL;DR: The definition of fuzzy time series is given, some properties of fuzzyTime series are explored, and procedures to develop fuzzy timeseries models are discussed.
Journal ArticleDOI
Forecasting enrollments based on fuzzy time series
TL;DR: A new method to forecast university enrollments based on fuzzy time series based on simplified arithmetic operations rather than the complicated max-min composition operations presented in Song and Chissom (1993a).
Journal ArticleDOI
Effective lengths of intervals to improve forecasting in fuzzy time series
TL;DR: Empirical analyses show that distribution- and average-based lengths are simple to calculate and can greatly improve forecasting results; in particular, they are superior to the randomly chosen lengths used in previous studies.