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Course Outline
Introduction to Time Series Analysis
- Overview of time series data relevant for government analytics
- Key components of time series: trend, seasonality, and noise
- Configuring Google Colab for time series analysis tasks
Exploratory Data Analysis for Time Series
- Visualizing time series data
- Decomposing time series components
- Identifying seasonality and trends
ARIMA Models for Time Series Forecasting
- Understanding ARIMA (AutoRegressive Integrated Moving Average) methodology
- 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 applications
- Implementing Prophet models in Google Colab
- Addressing holidays and special events in forecasting
Advanced Forecasting Techniques
- Addressing 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 procedures
Real-world Applications of Time Series Analysis
- Case studies of time series forecasting for government initiatives
- Practical exercises with real-world datasets
- Next steps for time series analysis in Python
Summary and Next Steps
Requirements
- Intermediate proficiency in Python programming
- Understanding of fundamental statistical concepts and analytical methods
Target Participants
- Data analysts
- Data scientists
- Professionals handling time series data for government initiatives
21 Hours
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Equipped with examples
