Time Series Analysis in R
Time Series Analysis in R is used to see how an object behaves over a period of time. In R Programming Language, it can be easily done by the ts() function with some parameters. Time series takes the data vector and each data is connected with a timestamp value as given by the user. This function is mostly used to learn and forecast the behavior of an asset in business for a period of time. For example, sales analysis of a company, inventory analysis, price analysis of a particular stock or market, population analysis, etc.
Syntax: objectName <- ts(data, start, end, frequency)
- data – represents the data vector
- start – represents the first observation in time series
- end – represents the last observation in time series
- frequency – represents number of observations per unit time. For example, frequency=1 for monthly data.
Note: To know about more optional parameters, use the following command in the R console:
Examples of Time Series Analysis
Let’s take the example of the COVID-19 pandemic situation. Taking the total number of positive cases of COVID-19 cases weekly from 22 January 2020 to 15 April 2020 the world in data vector.
Multivariate Time Series Analysis
Multivariate Time Series is creating multiple time series in a single chart. Taking data of total positive cases and total deaths from COVID-19 weekly from 22 January 2020 to 15 April 2020 in a data vector.
Time Series Forecasting
Forecasting can be done on time series using some models present in R. In this example, Arima automated model is used. To know about more parameters of arima() function, use the below command.
In the below code, forecasting is done using the forecast library and so, installation of the forecast library is necessary.
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 2020.307 2547989 2491957 2604020 2462296 2633682 2020.326 2915130 2721277 3108983 2618657 3211603 2020.345 3202354 2783402 3621307 2561622 3843087 2020.364 3462692 2748533 4176851 2370480 4554904 2020.383 3745054 2692884 4797225 2135898 5354210
Below graph plots estimated forecasted values of COVID-19 if it continues to be widespread for the next 5 weeks.
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