Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting
What you will learn
Encounter special types of time series like White Noise and Random Walks.
Learn about accounting for “unexpected shocks” via moving averages.
Start coding in Python and learn how to use it for statistical analysis.
Comprehend the need to normalize data when comparing different time series.
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- Dive deep into the foundational concepts of time series data, understanding its inherent sequential nature and the challenges it presents for traditional statistical methods.
- Master the application of key time series models, progressing from foundational concepts like stationarity and autocorrelation to advanced techniques such as Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) models.
- Explore the intricacies of Vector Autoregression (VAR) models for analyzing and forecasting multiple interdependent time series, uncovering complex relationships and lead-lag effects.
- Grasp the theoretical underpinnings and practical implementation of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to effectively capture and forecast time-varying volatility in financial and economic data.
- Leverage the power of automated forecasting tools like Auto ARIMA to efficiently identify optimal model orders and generate robust predictions, saving valuable time and effort.
- Gain proficiency in using Python libraries such as statsmodels, pandas, and scikit-learn for efficient data manipulation, visualization, and model building in time series analysis.
- Develop the ability to diagnose and address common time series issues, including seasonality, trend, autocorrelation, and heteroskedasticity, through appropriate data transformations and model selection strategies.
- Understand the principles of model evaluation and selection, learning to use metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) to assess forecasting accuracy.
- Implement techniques for incorporating external regressors and exogenous variables into time series models to enhance forecasting performance and capture the impact of influencing factors.
- Explore advanced forecasting horizons and scenario analysis, enabling you to predict future trends under various hypothetical conditions.
- Acquire the skills to build custom forecasting pipelines, from data preprocessing and feature engineering to model deployment and ongoing monitoring.
- Understand the importance of causal inference within time series contexts and explore methods for identifying causal relationships between variables over time.
- Learn to visualize time series data effectively using libraries like Matplotlib and Seaborn to identify patterns, anomalies, and trends.
- Gain practical experience with real-world datasets across various domains, such as finance, economics, marketing, and operations, to solidify your understanding.
- PROS:
- Provides hands-on coding experience with practical Python implementations.
- Covers a comprehensive range of time series models from basic to advanced.
- Emphasizes practical application and real-world problem-solving.
- Develops critical skills for data-driven decision-making in dynamic environments.
- CONS:
- Requires a solid foundation in basic statistical concepts and Python programming.
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