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(This article was first published on <strong><a href="https://www.r-exercises.com/2018/06/20/intro-to-time-series-analysis-part-2-exercises/"> R-exercises</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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In the exercises below, we will explore more in Time Series analysis.The previous exercise is <a rel="nofollow" href="https://www.r-exercises.com/2018/06/06/intro-to-time-series-analysis-part-1/" target="_blank">here</a>,Please follow this in sequence<br>
Answers to these exercises are available <a rel="nofollow" href="https://www.r-exercises.com/2018/06/20/intro-to-time-series-analysis-part-2-solutions/" target="_blank">here</a>.</p>
<p><strong>Exercise 1</strong></p>
<p>load the AirPassangers data,check its class and see the start and end of the series . </p>
<p><strong>Exercise 2</strong><br>
check the cycle of the TimeSeries AirPassangers . </p>
<p><strong>Exercise 3</strong></p>
<p>create the lagplot using the gglagplot from the forecast package,check how the relationship changes as the lag increases	</p>
<p><strong>Exercise 4</strong></p>
<p>Also plot the correlation for each of the lags , you can see when the lag is above 6 the correlation drops and again climbs up in 12 and again drops in 18 .<br>
<strong>Exercise 5</strong></p>
<p>Plot the histogram of the AirPassengers using gghistogram from forecast</p>
<p><strong>Exercise 6</strong></p>
<p>Use tsdisplay to plot autocorrelation , timeseries and partial autocorrelation together in a same plot</p>
<p><strong>Exercise 7</strong></p>
<p>Find the outliers in the timeseries . </p>
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