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

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

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