Lessons

- 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

# Check if an object is a time series object in R

In R, objects can be of different class such as vector, list, dataframe, ts, etc. When you load a dataset into R, it may not necessarily be a time series object. We can use the `is.ts()`

function to test if the given object is a time series (ts) object or not.

Below we perform this test on all the objects we've created so far in this course.

```
> is.ts(msft_ts)
[1] TRUE
> is.ts(GDP_data)
[1] TRUE
> is.ts(sp_vector)
[1] FALSE
> is.ts(sp_ts)
[1] TRUE
>
```

As you can see, all objects return TRUE except `sp_vector`

which is a vector.