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## (R Tutorials for Citizen Data Scientist)

## Statistics with R for Business Analysts – Time Series Analysis

Time series is a series of data points in which each data point is associated with a timestamp. A simple example is the price of a stock in the stock market at different points of time on a given day. Another example is the amount of rainfall in a region at different months of the year. R language uses many functions to create, manipulate and plot the time series data. The data for the time series is stored in an R object called **time-series object**. It is also a R data object like a vector or data frame.

The time series object is created by using the **ts()** function.

### Syntax

The basic syntax for **ts()** function in time series analysis is −

timeseries.object.name <- ts(data, start, end, frequency)

Following is the description of the parameters used −

**data**is a vector or matrix containing the values used in the time series.**start**specifies the start time for the first observation in time series.**end**specifies the end time for the last observation in time series.**frequency**specifies the number of observations per unit time.

Except the parameter “data” all other parameters are optional.

### Example

Consider the annual rainfall details at a place starting from January 2012. We create an R time series object for a period of 12 months and plot it.

# Get the data points in form of a R vector. rainfall <- c(799,1174.8,865.1,1334.6,635.4,918.5,685.5,998.6,784.2,985,882.8,1071) # Convert it to a time series object. rainfall.timeseries <- ts(rainfall,start = c(2012,1),frequency = 12) # Print the timeseries data. print(rainfall.timeseries) # Give the chart file a name. png(file = "rainfall.png") # Plot a graph of the time series. plot(rainfall.timeseries) # Save the file. dev.off()

When we execute the above code, it produces the following result and chart −

Jan Feb Mar Apr May Jun Jul Aug Sep 2012 799.0 1174.8 865.1 1334.6 635.4 918.5 685.5 998.6 784.2 Oct Nov Dec 2012 985.0 882.8 1071.0

The Time series chart −

## Different Time Intervals

The value of the **frequency** parameter in the ts() function decides the time intervals at which the data points are measured. A value of 12 indicates that the time series is for 12 months. Other values and its meaning is as below −

**frequency = 12**pegs the data points for every month of a year.**frequency = 4**pegs the data points for every quarter of a year.**frequency = 6**pegs the data points for every 10 minutes of an hour.**frequency = 24*6**pegs the data points for every 10 minutes of a day.

## Multiple Time Series

We can plot multiple time series in one chart by combining both the series into a matrix.

# Get the data points in form of a R vector. rainfall1 <- c(799,1174.8,865.1,1334.6,635.4,918.5,685.5,998.6,784.2,985,882.8,1071) rainfall2 <- c(655,1306.9,1323.4,1172.2,562.2,824,822.4,1265.5,799.6,1105.6,1106.7,1337.8) # Convert them to a matrix. combined.rainfall <- matrix(c(rainfall1,rainfall2),nrow = 12) # Convert it to a time series object. rainfall.timeseries <- ts(combined.rainfall,start = c(2012,1),frequency = 12) # Print the timeseries data. print(rainfall.timeseries) # Give the chart file a name. png(file = "rainfall_combined.png") # Plot a graph of the time series. plot(rainfall.timeseries, main = "Multiple Time Series") # Save the file. dev.off()

When we execute the above code, it produces the following result and chart −

Series 1 Series 2 Jan 2012 799.0 655.0 Feb 2012 1174.8 1306.9 Mar 2012 865.1 1323.4 Apr 2012 1334.6 1172.2 May 2012 635.4 562.2 Jun 2012 918.5 824.0 Jul 2012 685.5 822.4 Aug 2012 998.6 1265.5 Sep 2012 784.2 799.6 Oct 2012 985.0 1105.6 Nov 2012 882.8 1106.7 Dec 2012 1071.0 1337.8

The Multiple Time series chart −

Time Series Analysis in R using Neural Networks | Data Science with R

## Statistics with R for Business Analysts – Time Series Analysis

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

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