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Course Outline
Introduction to Time Series Analysis
- Overview of time series data
- Components of time series: trend, seasonality, noise
- Setting up Google Colab for time series analysis
Exploratory Data Analysis for Time Series
- Visualizing time series data
- Decomposing time series components
- Detecting seasonality and trends
ARIMA Models for Time Series Forecasting
- Understanding ARIMA (AutoRegressive Integrated Moving Average)
- Choosing parameters for ARIMA models
- Implementing ARIMA models in Python
Introduction to Prophet for Time Series Forecasting
- Overview of Prophet for time series forecasting
- Implementing Prophet models in Google Colab
- Handling holidays and special events in forecasting
Advanced Forecasting Techniques
- Handling missing data in time series
- Multivariate time series forecasting
- Customizing forecasts with external regressors
Evaluating and Fine-tuning Forecast Models
- Performance metrics for time series forecasting
- Fine-tuning ARIMA and Prophet models
- Cross-validation and backtesting
Real-world Applications of Time Series Analysis
- Case studies of time series forecasting
- Practical exercises with real-world datasets
- Next steps for time series analysis in Python
Summary and Next Steps
Requirements
- Intermediate knowledge of Python programming
- Familiarity with basic statistics and data analysis techniques
Audience
- Data analysts
- Data scientists
- Professionals working with time series data
21 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
