## Introduction of Valuable time Combination of Foretelling of | Feature five | ARIMA Time Show In preparation Theory

Introduction of Time Series Forecasting | Part 4 | ARIMA Time Series Forecasting Theory

Hi guys… in this video I have talked about the theory of ARIMA (Auto regressive integrated moving average) time series forecasting methodology. I have tried to explain its component like ACF, PACF and lagged difference with the help of simple example to that you can understand their functioning in ARIMA process.

Theory of Arima time series forecasting methodology

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## An Introduction Into Foretelling of Using STATA

A brief introduction into STATA done for my CAT 125 Digital Media Storytelling Project.

Nonlinear Trend Curves:
Exponential trend (v={a}*exp({b}*t))
Logarithmic (v={a}+{b}*ln(t))
Power curve (v={a}*t^{b})
Reciprocal (v={a}+{b}/t)
Log reciprocal (v={a}*exp({b}/t))
Modified exponential (v={a}+{b}*exp({c}*t))
Gompertz (v={a}*exp({b}*exp({c}*t)))
Logistic (v={a}/(1+{b}*exp({c}*t)))

STATA Resource Page: https://www.stata.com/features/documentation/
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## Introduction of Valuable time Combination of Projecting | Step 7 (seven) | ARIMA Forecasting real life Example in R Hi guys.. in this part 6 of time series forecasting video series I have taken a real life example of rain fall in india and predicted the future years rains with by producing the arima model and then using the forecast package, I predicted the next few years rain fall values.

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