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Seasonal Adjustment

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Seasonal Adjustment & Trend-Cycle estimation

Download the seasonally adjusted and trend-cycle series for India's important macroeconomic indicators in an excel file from the links below.
Economic Indicators (Monthly): Feb 2019
Seasonally Adjusted GDP (In Rs. Million): Q3 2019-20

Seasonal adjustment and trend-cycle estimation of the original economic time series are a critical input to meaningful business cycle study, turning points detection, and forecasting.
Seasonally adjusted data helps to better understand the short term movements of the original time series by removing the seasonal effects.
And trend-cycle estimation captures the long term trend and cyclical movements of the original series. It is estimated by removing and/or adjusting short term volatility due to the presence of seasonal effects, irregular components, and outliers in the original series.
Still, no Govt. statistical body publishes these data.
This is a serious attempt to provide monthly seasonally adjusted and trend-cycle estimation for important economic indicators of the Indian economy.
The most commonly used software seasonal adjustment and trend-cycle estimation is X-13ARIMA-SEATS developed by US Economic Bureau. Though the software is developed to handle US economic data in its default form but it provides some levers for customization to utilize it in the analysis of time-series data for other countries.
Using this feature of the software, Christoph Sax has developed a package “seasonal” for R. R is a powerful statistical software used by economists, data scientists and others for econometric modeling and big data analysis. The package “seasonal” is R-interface to X-13ARIMA-SEATS. The historical and future dates of Indian festival like Diwali is in-built in the package.
I am thankful to Christoph for accepting my request to give an option in the package to introduce the Diwali effect for Indian economic indicators.  I am overwhelmed by his acknowledgment in the package documentation for my minor contributions. 
See the case study in the following link detailing steps taken to generate seasonally adjustment and trend-cycle series using “seasonal” package:-
Index of Industrial Production (IIP)