I hope this link is useful

https://www.researchgate.net/publication/271683439_Forecasting_Exchange_Rates_A_Chaos-Based_Regression_Approach

Article Forecasting Exchange Rates: A Chaos-Based Regression Approach

One way is to use more than one model and compare error parameters. Using regression and assessing R sq. values, and later on the betas.

 I am not sure if I understand your question(), so i looked at some of your publications and they seem to focus on cross section or panel data.  I hope this is not too low of an answer to get you going.

Consider adding a variable, let's call it time, where time = 0 for your first observation and increases by one with each observation. If your data starts in 2004 - quarter 1 (20051), then  use 0 for 20051, use 1 for 20052, 2 for 20053 and so forth.  Then as you forecast beyond the latest data, your use the estimated coefficients including the time variable to estimate the value of the dependent variable.  Obviously, in addition to  the variable time, you will need variables that tend to reveal the value of the dependent variable well in advance or information that is given (for example, a dummy variable that takes on a value of 1 if it is a 4th quarter variable and 0 otherwise.  The quarters are known in advance and seasonal patterns might be able to be modeled by using up to three of these quarterly dummy variables.  By the way one cannot use 4 quarterly dummy variables, if the intercept is also estimated because each of these quarterly dummy coefficients becomes additive with the intercept to effectively modify the intercept.  See the dummy variable trap if you are interested.  

The simple mathematics of inserting known values for your variables into the equation with your coefficients allows forecasting in this really simple approach.  There are MUCH better way to forecast; however, it seemed like this might help to get you thinking of simple time series forecasting.  I hope it helps and if I missed the point of your question, please let me know. 

Rob

Thank you so much

Thanks..

If you are interested in univariate time series data predictions, have a look in PSF algorithm and it's R Packages available at :

CRAN: https://cran.r-project.org/web/packages/PSF/index.html

GitHub: https://github.com/neerajdhanraj/PSF

How to use: https://www.researchgate.net/publication/304131481_PSF_Introduction_to_R_Package_for_Pattern_Sequence_Based_Forecasting_Algorithm

and

https://www.researchgate.net/publication/304580701_Introduction_of_seasonality_concept_in_PSF_algorithm_to_improve_univariate_time_series_predictions

For further details contact me at: http://www.neerajbokde.com

Article PSF: Introduction to R Package for Pattern Sequence Based Fo...

Article Introduction of seasonality concept in PSF algorithm to impr...

Time Series is a sequence of well-defined data points measured at consistent time intervals over a period of time. Data collected on an ad-hoc basis or irregularly does not form a time series. Time series analysis is the use of statistical methods to analyze time series data and extract meaningful statistics and characteristics about the data

https://www.researchoptimus.com/article/what-is-time-series-analysis.php

Thanks everyone