Time Series ARIMA model Using R | Stationarity | Non Stationarity

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Time series modelling is a popular way for forecasting data. In this video you will learn how to build a ARIMA model using R. ARIMA is known as Auto Regressive Integrated Moving Average which consists of AR, MA components.

You will learn for both stationary and non-stationary series. We have taken time series data of stock price and return to demonstrate

  1. Biswajit Pani
  2. data science
  3. machine learning
  4. analytics
  5. analytics university
  6. data mining
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Комментарии
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Can you explain the output from the adf test? For instance, what is the lag order and what does the dickey-fuller statistic mean?
Thank you!

ryleehall
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Nice explanations. One question, can`t both the arima for returns and prices be done simultaneously?

anoapenshapiro
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Great video!! I was stuck on how to do a stationarity test and you explained it so well. The only thing is I have panel time series data so it’s very complicated

anastasiakarpinskaia
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AMAZINGLY EXPLAINED AND REALLY HELPFUL CONTENT

pranavtayal