Fuzzy weighted local approximation for financial time series modelling and forecasting

2002 ◽  
Author(s):  
P. Pellizzari ◽  
C. Pizzi
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Local Approximation ◽  
Financial Time ◽  
Time Series Modelling ◽  
Modelling And Forecasting

Regularised gradient boosting for financial time-series modelling

2017 ◽  
Vol 14 (3) ◽  
pp. 367-391 ◽  
Author(s):  
Alexandros Agapitos ◽  
Anthony Brabazon ◽  
Michael O’Neill
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Gradient Boosting ◽  
Financial Time ◽  
Time Series Modelling

scholarly journals Dynamics of Heteroscedasticity Modelling and Forecasting of Tax Revenue in a Developing Economy: A Review

2020 ◽  
Vol 5 (3) ◽  
pp. 34-41
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Tax Revenue ◽  
Developing Economy ◽  
Financial Time ◽  
Non Linear ◽  
Exchange Data ◽  
Modelling And Forecasting

Tax revenue modelling and forecasting is very crucial for revenue collection and tax administration management. The dynamics of heteroscedasticity in the financial time series (tax revenue) in the domain of technique used to model and predict tax revenue in the emerging economy threw us to this investigation. The reviews are categorized into two the tax revenue and stock exchange index. Five factors were considered in this studies modelling, forecasting, linear model, nonlinear model and heteroscedasticity, it is on this note that we syntheses over 75 studies from the literature to consider the pattern of reporting tax revenue and stock market index. Thus, from the reviewed literature, we inferred that the pattern of reporting tax revenue data and the analytical techniques employed by most of these studies are responsible for the instability (volatility) in the financial time series forecasting. Also, results revealed that linear models are mostly applied to tax revenue data with fewer non-linear models, while combination and single non-linear models were mostly used for stock exchange data. Thus, we recommend the combination of linear and nonlinear models for both tax revenue and stock exchange data which can minimize the error of heteroscedasticity in the forecasting of tax revenue in a developing economy.

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