![](https://private-user-images.githubusercontent.com/116647771/307558412-10d594c7-eae8-48de-9fce-bef725bf5c4f.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjI2MDkzOTcsIm5iZiI6MTcyMjYwOTA5NywicGF0aCI6Ii8xMTY2NDc3NzEvMzA3NTU4NDEyLTEwZDU5NGM3LWVhZTgtNDhkZS05ZmNlLWJlZjcyNWJmNWM0Zi5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwODAyJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDgwMlQxNDMxMzdaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT0yZTNhOTYxNDk3MDYzZTIwNWYxNzBmMzIxY2JkOWE4NDAzMzMyMjViZTA1ODU0NzJhOWZlM2ZjNzA1ODMyMTU4JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.0iPUIaMqwc3mR7cOYfwJvHogsqBXoFY0p48iGhvBTzQ)
- Stationarity of a Time Series Data Set
- Decomposition of Raw Data Set Using Ratio to Moving Average Method
- Mathematical Curves
- ARIMA Modeling
- Ljung Box Test
- Forecasting
- Stationarity Testing:
- Visualizing Raw Data & 1st Differences
- KPSS Test with p-value 0.01 implying rejection of H0 and non-stationarity in data
- Data Decomposition:
-
Used Additive Model
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Decomposed Components: Trend, Seasonality, Residual Series with Random/White Noise
- Individual Component Fitting:
- Trend: Fitted Cubic Curve (highest adjusted R^2 of 0.991 & least MSE)
- Seasonality: ACF and PACF Plots
- Random Noise: ARIMA Modelling, Ljung–Box test for goodness of fit
![](https://private-user-images.githubusercontent.com/116647771/307558242-1efbd625-00b0-4ea9-aaa9-482a50059e0a.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjI2MDkzOTcsIm5iZiI6MTcyMjYwOTA5NywicGF0aCI6Ii8xMTY2NDc3NzEvMzA3NTU4MjQyLTFlZmJkNjI1LTAwYjAtNGVhOS1hYWE5LTQ4MmE1MDA1OWUwYS5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwODAyJTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDgwMlQxNDMxMzdaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT0zZmRmZjU2YzBhOTUwYTJiNDhkMjY1YjQ0ZjQxNzk4MDQxZDhiYjc2MjdkM2NkZTg3M2NlODczYWU5MGVjNDU3JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.mHgWgD0tEsMc91TIa9hPmkdc-c54BdXGjhxEx07bSK0)
- The best Fit is AR(1) to forecast seasonality due to geometric decay in the ACF plot and sharp cut-off in the PACF Plot.
- Out of an overall YoY rise of 3.2 ± 0.5 mm, 1.8 ± 0.41 mm is due to Climate Change (~43%).
- The overall rise of 102.5 mm (approx. 4 inches) seen since 1993; the forecasted increase of approx. 109.6 mm till 2025 (i.e., ~ 6.9% rise in change in 4 years).
- Studying regional impact analysis with the inclusion of isostatic causes.
- Extending the study to support the formulation of natural calamity action plans and better the existing understanding of their occurrences