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📊R Programming📊Statistical Methods

Financial Time Series Analysis in R

27 chapters · 1 free to read, 26 in the PDF · 2 files

Learn the fundamentals of analyzing a financial time series in R

This ebook provides an introduction to the financial times series data and how we can analyze the time series data in R.

  • You will learn about how to explore and build time-series data, calculate its key statistics, and plot time series charts.
  • You will also learn about how to use the important time series models such as White Noise, Random Walk, Autoregression, and Moving Average.
  • You will learn how to simulate these models in R and fit these models into financial time series data using the ARIMA functions.
  • Finally, you will learn about predictive modeling and how to use these models to predict the future.
  • We’ve provided various step-by-step examples using real financial time series data such as stock prices, and economic factors.

Chapters

  1. 1Financial Time Series DataRead free
  2. 2Exploring Time Series Data in RIn the PDF
  3. 3Plotting Time Series in RIn the PDF
  4. 4Handling Missing Values in Time SeriesIn the PDF
  5. 5Creating a Time Series Object in RIn the PDF
  6. 6Check if an object is a time series object in RIn the PDF
  7. 7Plotting Financial Time Series DataIn the PDF
  8. 8Characteristics of Time SeriesIn the PDF
  9. 9Stationary Process in Time SeriesIn the PDF
  10. 10Transforming a Series to StationaryIn the PDF
  11. 11Time Series Transformation in RIn the PDF
  12. 12Differencing and Log TransformationIn the PDF
  13. 13Autocorrelation in RIn the PDF
  14. 14Time Series ModelsIn the PDF
  15. 15ARIMA ModelingIn the PDF
  16. 16Simulate White NoiseIn the PDF
  17. 17Simulate Random WalkIn the PDF
  18. 18AutoRegressiveIn the PDF
  19. 19Estimating AutoRegressiveIn the PDF
  20. 20Forecasting with AutoRegressiveIn the PDF
  21. 21Moving AverageIn the PDF
  22. 22Estimating Moving AverageIn the PDF
  23. 23ARIMA Modelling in RIn the PDF
  24. 24ARIMA Modelling - Identify Model for a Time SeriesIn the PDF
  25. 25Forecasting with ARIMA Modeling in R - Case StudyIn the PDF
  26. 26Automatic Identification of Model Using auto.arima() Function in RIn the PDF
  27. 27Financial Time Series in R - Course ConclusionIn the PDF

Files

  • eBook - Financial Time Series Analysis with R

    PDF · Included in Financial Time Series Analysis with R

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  • Data and R Code

    ZIP · Included in Financial Time Series Analysis with R

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