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Future Business Journal

Table 1 A brief overview of the literature of time series forecasting

From: Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model

Authors Sustainable regression for time series Forecasting of time series Sustainable forecasting of time series Forecasting of financial time series
Denby and Martin [31] Auto-regression sustainable model    
Rousseeuw and Yohai [84] S estimator    
Martin and Yohai [64] Auto-regression combinative sustainable model of moving average    
Karmarkar [52] Combinative model   Sustainable forecasting  
Wu [101]     Forecasting of financial data with neural network
Salibian -Barrera and Yohai [86] Algorithm of fast calculation of S estimator    
Marcellino et al. [63] Auto-regression model Repetitive and direct forecasting   
Hyndman and Ullah [47] LC model Forecasting of birth and death rates Sustainable forecasting  
Gagne and Duchesne [38]    Multivariate auto-regression model with exogenous variables  
Chao et al. [24] Updating auto-regression model Forecasting of flood time Sustainable recursive forecasting approach  
Authors Sustainable regression for time series Forecasting of time series Sustainable forecasting of time series Forecasting of financial time series
Croux et al. [28] Using of M estimator in expansive smoothing method and Holt-winters algorithm    
Yu et al. [105]     Artificial neural networks and PCA
Gelper et al. [39] Expansive smoothing method and Holt-winters algorithm   Sustainable forecasting  
Giordani and villani [41]   Combination of neural network and the combinative model of auto-regression of moving average   
Croux et al. [29] MM estimator without filter   Sustainable forecasting for non-static series  
Kharin [54]    Sustainable forecasting statistics  
Araujo [10]     Forecasting with automatic self-correction approach
Tzouras et al. [96]     Hurst exponent
Ying Wei [104]     Hybrid ANFIS model
Podsiadlo and Rybinski [76]     Rough sets with time-weighted rule voting
Ye et al. [103]     Genetic algorithm
Jiang et al. [49]     Permutation entropy
Pradeepkumar and Ravi [78]     Particle Swarm optimization trained quantile regression neural network
Stratimirovic et al. [91]     Wavelet analysis
Xu and Shang [102]     
     Multiscale entropy
Abbaszadeh et al. [1]     Using Lyapunov’s method for analysing of chaotic behavior on financial time series data
Moradi et al. [67]     Using L‐Co‐R algorithm for forecasting time series stock returns
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