Exploring Time Series Analysis with R: A Practical Guide
Time has been a very crucial factor ever since researchers used instruments to record data. When it comes to time series analysis, time is also a significant variable here. Time series analysis refers to the process of analyzing a series of data points at any given time at consistent or fixed intervals. The main purpose is to show or evaluate how the specific variable changes over time. Many organizations use time series analysis to see how systemic patterns change or how trends drive demands in a particular scenario. In this tutorial, we will learn everything that a beginner needs to know about time series analysis using R.
Time Series Analysis And R
R language has many functions and packages that beginners can use to create and plot the data in a time series analysis graph. The data is stored in the time-series object, an R object for time-series analysis.
Syntax for Time Series In R
Let’s look at the basic syntax for the time-series function in a time-series analysis. The time series function is represented as ts().
- Consider the data for annual rainfall at any place starting from, let’s say, January 2012. Let us create an R time-series object for about 12 months.
- Plot the various data points in the form of an R vector.
rainfall < -c (x1, x2, x3,…..,x12)
- Convert it into the time series object in R, as shown below:
Time Series object= rainfall.timeseries
Create using the “ts” function.
Start argument set to c(2012,1) with frequency = 12.
- Then, print the time series data using the “print” function.
- For easier reading, try giving the chart file a name for simplicity.
- Finally, plot a graph of the series in R using the plot function.
- Save the file so that the data is not lost.
When executed, the final chart looks something like this, with the rainfall data against the x and y-axis of the time series chart.
Various R Packages For Time Series Analysis
There are many R Packages that beginners can use for time series analysis. Let’s have a look at a few of them:
- The “tseries” package in R provides many functions for decomposing the data in a time series.
- The “forecast” package best suits neural networks or exponential smoothing.
- For users wanting a more flexible framework, they can opt for “rugarch”.
- If simple is the style you are looking for, Facebook’s “prophet” might be your option.
Why Is R Best Suited For Time Series Analysis?
● R has a large global community with lots of resources and packages.
● Provides flexibility when it comes to tools and functions.
● Easy to use for beginners and experienced programmers.
● The language has great visualization capabilities for time series analysis.
● It’s an open-source programming language.
Time series analysis using R is great for understanding trends and how data changes over time. We hope that this tutorial has helped you gain a foothold in learning about time series analysis and how R language is the perfect tool for it. Head to the Education Nest if you want more free guides and tutorials from the best experts in the field!