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In this course, you will :
- Distinguish between time series and cross-sectional data.
- Understand the fundamental assumptions of time series data and how to use them to your advantage.
- Creating a time series from a data set.
- Begin learning to code in Python and how to use it for statistical analysis.
- Perform time-series analysis in Python and interpret the results based on the data.
- Examine the critical distinctions between related series such as prices and returns.
- Recognize the importance of normalising data when comparing different time series.
- Discover unusual time series such as White Noise and Random Walks.
- Discover what "autocorrelation" is and how to account for it.
- Learn how to use moving averages to account for "unexpected shocks."
- Discuss the role of residuals in model selection in time series.
- Understand stationarity and how to test for it.
- Recognize the concept of integration and understand when, why, and how to use it correctly.
- Recognize the significance of volatility and how we can quantify it.
- Forecast the future based on historical patterns.
Syllabus :
- Setting Up the Environment
- Introduction to Time Series in Python
- Creating a Time Series Object in Python
- Working with Time Series in Python
- Picking the Correct Model
- Modeling Autoregression: The AR Model
- Adjusting to Shocks: The MA Model
- Past Values and Past Errors: The ARMA Model
- Modeling Non-Stationary Data: The ARIMA Model
- Measuring Volatility: The ARCH Model
- An ARMA Equivalent of the ARCH: The GARCH Model
- Auto ARIMA
- Forecasting
- Business Case
