A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches
In this article, a novel M-factors fuzzy time series (FTS) forecasting model is presented, which relies upon on the hybridization of two procedures, viz., granular computing and bio-inspired computing. In this investigation, granular computing is utilized to discretize M-factors time series data set to obtain granular intervals. These intervals are additionally used to fuzzify the time series data set. Based on fuzzified time series data set, M-factors fuzzy relations are set-up. These M-factors fuzzy relations are further utilized to acquire forecasting results. Moreover, a novel bio-inspired algorithm is proposed to enhance the forecasting accuracy. The main objective of this algorithm is to adjust the lengths of the intervals (granular and non-granular intervals) in the universe of discourse that are used in forecasting. The proposed model is verified and validated with various real world data sets. Various statistical and comparative analyzes signify that the proposed model can take far better decision with the M-factors time series data sets. Moreover, empirical analysis demonstrates that forecasting accuracy of the proposed model based on granular intervals is better than non-granular intervals.
A computational method of forecasting based on fuzzy time series
In this paper, a computational method of forecasting based on fuzzy time series have been developed to provide improved forecasting results to cope up the situation containing higher uncertainty due to large fluctuations in consecutive year's values in the time series data and having no visualization of trend or periodicity. The proposed model is of order three and uses a time variant difference parameter on current state to forecast the next state. The developed model has been tested on the historical student enrollments, University of Alabama to have comparison with the existing methods and has been implemented for forecasting of a crop production system of lahi crop, containing higher uncertainty. The suitability of the developed model has been examined in comparison with the other models to show its superiority.
An efficient time series forecasting model based on fuzzy time series
In this paper, we present a new model to handle four major issues of fuzzy time series forecasting, viz., determination of effective length of intervals, handling of fuzzy logical relationships (FLRs), determination of weight for each FLR, and defuzzification of fuzzified time series values. To resolve the problem associated with the determination of length of intervals, this study suggests a new time series data discretization technique. After generating the intervals, the historical time series data set is fuzzified based on fuzzy time series theory. Each fuzzified time series values are then used to create the FLRs. Most of the existing fuzzy time series models simply ignore the repeated FLRs without any proper justification. Since FLRs represent the patterns of historical events as well as reflect the possibility of appearances of these types of patterns in the future. If we simply discard the repeated FLRs, then there may be a chance of information lost. Therefore, in this model, it is recommended to consider the repeated FLRs during forecasting. It is also suggested to assign weights on the FLRs based on their severity rather than their patterns of occurrences. For this purpose, a new technique is incorporated in the model. This technique determines the weight for each FLR based on the index of the fuzzy set associated with the current state of the FLR. To handle these weighted FLRs and to obtain the forecasted results, this study proposes a new defuzzification technique. The proposed model is verified and validated with three different time series data sets. Empirical analyses signify that the proposed model have the robustness to handle one-factor time series data set very efficiently than the conventional fuzzy time series models. Experimental results show that the proposed model also outperforms over the conventional statistical models.
Intuitionistic Fuzzy Time Series: An Approach for Handling Nondeterminism in Time Series Forecasting
Atanassov introduced the notion of intuitionistic fuzzy set as generalization of the fuzzy set, which has been proved to be very useful tool in handling nondeterminacy (hesitation) in the system. In the implementation of fuzzy time series forecasting, nondeterminacy is always neglected without any reason. In this paper, we give the notion of intuitionistic fuzzy time series to handle the nondeterminism in time series forecasting. An intuitionistic fuzzy time series forecasting model is also proposed. The proposed intuitionistic fuzzy time series forecasting method uses intuitionistic fuzzy logical relations on time series data. Performance of the proposed method is verified by applying it on two time series data. The effectiveness of the proposed intuitionistic fuzzy time series forecasting method is verified by comparing the forecasted output with other intuitionistic-fuzzy-set-based fuzzy time series forecasting methods using root-mean-square error and average forecasting error.
neural network sensor network machine learning artificial neural network support vector machine deep learning time series data mining support vector vector machine wavelet transform data analysi deep neural network neural network model hidden markov model regression model deep neural anomaly detection gene expression data base generative adversarial network generative adversarial time series datum adversarial network experimental datum fourier series nearest neighbor support vector regression time series analysi missing datum data based moving average gene expression datum time series model series analysi lyapunov exponent series datum outlier detection dynamic time warping time series forecasting data mining algorithm panel datum time series prediction series model multivariate time series finite time unit root dynamic time linear and nonlinear series forecasting time warping distance measure financial time series series prediction integrated moving average experimental comparison multivariate time financial time dependent variable chaotic time series nonlinear time vegetation index nonlinear time series arima model fuzzy time large time anomaly detection method fuzzy time series chaotic time autoregressive integrated moving time series based air pollutant time series classification representation method fokker-planck equation series representation similarity analysi series classification univariate time series time series clustering unsupervised anomaly detection periodic pattern nearest neighbor classification time series dataset series data mining time series regression anomaly detection approach time series database series clustering observed time series forecasting time series local similarity long time series time series similarity series database fmri time series complex time indian stock market time series representation symbolic aggregate approximation complex time series forecasting time series data set series similarity fmri time time series anomaly large time series series data analysi series anomaly detection analyzing time series expression time series interrupted time series ucr time series time correction modeling time series clustering time series mining time series interrupted time series data based fourier series representation simple exponential smoothing early classification forecast time series time series subsequence sensor networks pose distributed index piecewise constant approximation quality time series mining time microarray time series incomplete time series massive time series large-scale time series analysing time series microarray time neural time series mri time neural time series data generated time series experiment visualizing time series called time series data set