- 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

# Exploring Time Series Data in R

Let's look at a few commands that we will frequently use while exploring time series data.

### length()

The `length()`

function tells us the number of elements in out time series dataset.

```
> length(msft_ts)
[1] 252
>
```

### head()

The `head()`

function displays the top n elements of the dataset. This is useful while exploring large datasets.

```
> head(msft_ts,n=10)
[1] 54.80 55.05 54.05 52.17 52.33 52.30 52.78 51.64 53.11 50.99
>
```

### tail()

The `tail()`

function displays the last n elements of the dataset. This is useful while exploring large datasets.

```
> tail(msft_ts,n=10)
[1] 62.30 63.62 63.54 63.54 63.55 63.24 63.28 62.99 62.90 62.14
>
```

### Example 2 - Quarterly GDP Data

Let's take one more example, this time quarterly GDP data. We will load this data from Quandl. The following command will load the quarterly GDP data from Quandl for the years 2014 to 2016

```
> GDP_data = Quandl("FRED/GDP", start_date="2014-01-01", end_date="2016-12-31",type="ts")
```

Let's explore this data.

### print()

We earlier saw that we can also use the `print()`

function to display the time series.

```
> print(GDP_data)
Qtr1 Qtr2 Qtr3 Qtr4
2014 17025.2 17285.6 17569.4 17692.2
2015 17783.6 17998.3 18141.9 18222.8
2016 18281.6 18450.1 18675.3 18869.4
>
```

As we can see, this data is presented as yearly data with 4 observations in each year.

### start() and end()

```
> start(GDP_data)
[1] 2014 1
> end(GDP_data)
[1] 2016 4
>
```

### frequency()

This tells us number of observations per unit of time.

```
> frequency(GDP_data)
[1] 4
>
```

The data is quarterly, so the lag between successive observations is 1 quarter. The dataset `GDP_data`

has been set up so that the unit of time is 1 year (frequency=4).

### deltat()

This is the fraction of the sampling period between successive observations; e.g., 1/12 for monthly data, and 1/4 for quarterly data. The function `deltat`

uses this time unit to compute the lag by the formula ∆t= 1/frequency). Only one of `frequency`

or `deltat`

should be provided.

```
> deltat(GDP_data)
[1] 0.25
>
```

### time()

The functions time and cycle create time series of the times at which the observations in a time series are taken and their "seasons".

```
> time(GDP_data)
Qtr1 Qtr2 Qtr3 Qtr4
2014 2014.00 2014.25 2014.50 2014.75
2015 2015.00 2015.25 2015.50 2015.75
2016 2016.00 2016.25 2016.50 2016.75
>
```

```
> cycle(GDP_data)
Qtr1 Qtr2 Qtr3 Qtr4
2014 1 2 3 4
2015 1 2 3 4
2016 1 2 3 4
>
```

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