- 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

# Financial Time Series in R - Course Conclusion

This course provided an overview of the fundamentals of time series analysis and how we can perform time series analysis in R. We reviewed some of the most important concepts of time series analysis and looked at the process involved in modeling a time series using the ARIMA models.

Time series analysis is a complex subject and this course focused on building a strong foundation for someone interested in time series analysis and forecasting. The skills you learned here will go a long way in building time series models in real-life scenarios. If you are interested in further building your expertise in time series analysis, there is practically end-less material available for you to explore. It will help to take up lots of sample time series data sets and try to forecast them. Some of the topics you should research next include how to go about validating your model, smoothing and decomposition methods, modeling seasonal time series (SARIMA - Seasonal ARIMA), ARCH models and spectral analysis.

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