R Tutorial : ARIMA Models in R
Key Takeaways
Uses R to model ARIMA time series
Original Description
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Hi, and welcome to DataCamp's course on ARIMA modeling with R!
My name is David Stoffer. I am Professor of Statistics at the University of Pittsburgh. I am the coauthor of two texts on time series analysis. One of them, "Time Series Analysis and Its Applications: With R Examples" is the basis of this course. The text has a companion R package called astsa which stands for Applied Statistical Time Series Analysis. This package will be used throughout the course.
Now, to get started, let's explore the nature of time-series data.
Here we have the Johnson & Johnson quarterly earnings per share series. It has some common features of time series data, upward trend, seasonality in that the 2nd and 3rd quarters usually up, while the 4th quarter is usually down. In addition, there is heteroscedasticity because, as the value of the asset grows, small percent changes become large absolute changes.
The second series is the annual global temperature deviations. The data are deviations from the average temperature between 1960 and 1980. You will notice that the data have a generally positive trend, but the trend is not always positive. Unlike the Johnson and Johnson data, this series does not have a seasonal component and it is homoscedastic.
The third series is the S&P 500 weekly returns. The S&P 500 is a US stock index based on 500 large corporations. Returns are the percent change per time period. Unlike the other series, this series does not have any trend or seasonality. In fact, it seems like there are not any patterns in the series (except that once in a while, the variance is big). This is an example of a particular kind of process called noise.
Next, we will describe some models that can be used to analyze the types of time series data we have seen.
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