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(This article was first published on <strong><a href="http://apicdn.viglink.com/api/click?format=go&key=949efb41171ac6ec1bf7f206d57e90b8&loc=https%3A%2F%2Fwww.r-bloggers.com%2Ffeed%2F&out=http%3A%2F%2Fr-posts.com%2Fnew-course-forecasting-in-r%2F"> R-posts.com</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><strong><a rel="nofollow" href="http://apicdn.viglink.com/api/click?format=go&key=949efb41171ac6ec1bf7f206d57e90b8&loc=https%3A%2F%2Fwww.r-bloggers.com%2Ffeed%2F&out=https%3A%2F%2Fwww.datacamp.com%2Fcourses%2Fforecasting-using-r%3Ftap_a%3D5644-dce66f%26tap_s%3D10907-287229" target="_blank">Forecasting in R</a></strong> is taught by Rob J. Hyndman, author of the forecast package</p>
<p>Forecasting involves making predictions about the future. It is required in many situations such as deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call center next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.</p>
<p> <span><strong>What You’ll Learn</strong></span><br>
<strong>Chapter 1:</strong> Exploring and Visualizing Time Series in R<br>
The first thing to do in any data analysis task is to plot the data.<br>
<strong>Chapter 2:</strong> Benchmark Methods and Forecast Accuracy<br>
In this chapter, you will learn general tools that are useful for many different forecasting situations.<br>
<strong>Chapter 3:</strong> Exponential Smoothing <br>
is framework generates reliable forecasts quickly and for a wide range of time series.<br>
<strong>Chapter 4:</strong> Forecasting with ARIMA Models<br>
ARIMA models provide another approach to time series forecasting.<br>
<strong>Chapter 5:</strong> Advanced Methods<br>
In this chapter, you will look at some methods that handle more complicated seasonality.</p>
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<p>You can start the free chapter for free of <strong><a rel="nofollow" href="http://apicdn.viglink.com/api/click?format=go&key=949efb41171ac6ec1bf7f206d57e90b8&loc=https%3A%2F%2Fwww.r-bloggers.com%2Ffeed%2F&out=https%3A%2F%2Fwww.datacamp.com%2Fcourses%2Fforecasting-using-r%3Ftap_a%3D5644-dce66f%26tap_s%3D10907-287229" target="_blank">Forecasting in R</a>.</strong></p>
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