Course Outline
Introduction to Time Series Analysis
- Overview of time series data.
- Key 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).
- Selecting appropriate 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.
- Managing 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 proficiency in Python programming.
- Familiarity with fundamental statistics and data analysis methods.
Audience
- Data analysts.
- Data scientists.
- Professionals working with time series data.
Testimonials (3)
How to use open satellites data for real applications
Tshering Dorji - Druk Holding and Investments
Course - Advanced Geographic Information Systems (GIS)
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.
