# Creating a Time Series Object in R

In R, we can use the `ts()`

function to create a time series object.

### Usage

Below is a simplified format of the `ts`

function. For complete details use `?ts`

in your R console.

```
ts(data = NA, start = 1, end = numeric(), frequency = 1, deltat = 1, names = )
```

**data:**a vector or matrix of the observed time-series values.**start:**the time of the first observation.**end:**the time of the last observation, specified in the same way as start.**frequency:**the number of observations per unit of time.**deltat:**the fraction of the sampling period between successive observations; e.g., 1/12 for monthly data. Only one of frequency or deltat should be provided.**names:**a character vector of names for the series in a multiple series: defaults to the colnames of data, or Series 1, Series 2, ....

While you can have data containing dates and corresponding values in an R object of any other class such as a dataframe, creating objects of ts class offers many benefits such as the time index information. Also, when you plot a ts object, it automatically creates a plot over time.

Let's look at how we can create a time series object using the `ts`

class.

### Dataset

For this example, we will use a dataset that consists of monthly prices of the S&P Composite Index for the last three years starting from July 2014 to June 2017. We load the original data in a data vector called `sp_vector`

.

```
sp_vector <- c(1973.1,1961.53,1993.23,1937.27,2044.57,2054.27,2028.18,2082.2,2079.99,2094.86,2111.94,2099.29,2094.14,2039.87,1944.41,2024.81,2080.62,2054.08,1918.6,1904.42,2021.95,2075.54,2065.55,2083.89,2148.9,2170.95,2157.69,2143.02,2164.99,2246.63,2275.12,2329.91,2366.82,2359.31,2395.35,2433.99)
```

### Print and Plot the Vector

We can first try to directly print and plot the vector to observe the dataset.

```
> print(sp_vector)
[1] 1973.10 1961.53 1993.23 1937.27 2044.57 2054.27 2028.18 2082.20 2079.99 2094.86 2111.94
[12] 2099.29 2094.14 2039.87 1944.41 2024.81 2080.62 2054.08 1918.60 1904.42 2021.95 2075.54
[23] 2065.55 2083.89 2148.90 2170.95 2157.69 2143.02 2164.99 2246.63 2275.12 2329.91 2366.82
[34] 2359.31 2395.35 2433.99
>
```

```
> plot(sp_vector)
>
```

As you can see, the plot does not contain any time index information.

### Creating Time Series Object

We can use the `ts()`

function to convert this vector into a time series object. Our data set contains monthly stock prices from July 2017 to June 2017. There are 12 observations per year starting from July 2014. So, we will set the frequency to 12 and the set start argument to c(2014,7) where the first value is the year and the second value is the month index (July).

```
> sp_ts <- ts(sp_vector,start=c(2014,7),frequency=12)
```

We can print to check that we have the correct time series.

```
> print(sp_ts)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2014 1973.10 1961.53 1993.23 1937.27 2044.57 2054.27
2015 2028.18 2082.20 2079.99 2094.86 2111.94 2099.29 2094.14 2039.87 1944.41 2024.81 2080.62 2054.08
2016 1918.60 1904.42 2021.95 2075.54 2065.55 2083.89 2148.90 2170.95 2157.69 2143.02 2164.99 2246.63
2017 2275.12 2329.91 2366.82 2359.31 2395.35 2433.99
>
```

### Plot the Time Series

We can now use the `plot.ts()`

function to plot the time series.

```
> plot.ts(sp_ts)
```

Note that it automatically recognized that it is a time series and plots time on the x-axis.

#### Course Downloads

Get smart about tech at work.

As a non-technical professional, learn how software works with simple explanations of tech concepts. Learn more...

- Financial Time Series Data
- Exploring Time Series Data in R
- Plotting Time Series in R
- Handling Missing Values in Time Series
- Creating a Time Series Object in R
- Check if an object is a time series object in R
- Plotting Financial Time Series Data (Multiple Columns) in R
- Characteristics of Time Series
- Stationary Process in Time Series
- Transforming a Series to Stationary
- Time Series Transformation in R
- Differencing and Log Transformation
- Autocorrelation in R
- Time Series Models
- ARIMA Modeling
- Simulate White Noise (WN) in R
- Simulate Random Walk (RW) in R
- AutoRegressive (AR) Model in R
- Estimating AutoRegressive (AR) Model in R
- Forecasting with AutoRegressive (AR) Model in R
- Moving Average (MA) Model in R
- Estimating Moving Average (MA) Model in R
- ARIMA Modelling in R
- ARIMA Modelling - Identify Model for a Time Series
- Forecasting with ARIMA Modeling in R - Case Study
- Automatic Identification of Model Using auto.arima() Function in R
- Financial Time Series in R - Course Conclusion

# Data Science for Finance Bundle: 43% OFF

**Data Science for Finance Bundle**for just $29 $51.