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Duration 21 hours
Course Outline
Introduction to Time Series Analysis
- Fundamental concepts of temporal data structures
- Core elements of time series: underlying trends, seasonal patterns, and stochastic variance
- Configuring Google Colab environments for statistical analysis
Exploratory Analysis of Temporal Data
- Techniques for visualizing historical data sequences
- Methodologies for decomposing complex time series components
- Identification of cyclical patterns and long-term directional shifts
ARIMA Methodologies for Temporal Projections
- Theoretical basis of AutoRegressive Integrated Moving Average (ARIMA) models
- Criteria for selecting optimal parameter configurations
- Programming ARIMA implementations using Python
Prophet Framework for Temporal Projections
- Overview of the Prophet algorithm and its application to forecasting
- Deployment of Prophet models within the Google Colab environment
- Incorporating holidays and exceptional events into predictive models
Advanced Forecasting Methodologies
- Strategies for managing incomplete data points in temporal sequences
- Predictive modeling with multiple interacting variables
- Integrating exogenous variables to refine forecast accuracy
Assessment and Calibration of Predictive Models
- Standard metrics for evaluating the performance of temporal forecasts
- Optimization of ARIMA and Prophet model parameters
- Validation techniques including cross-validation and historical backtesting
Practical Applications in Public Sector Operations
- Analytical case studies demonstrating forecasting in operational contexts
- Applied exercises utilizing empirical datasets relevant to government
- Future directions for temporal analysis within Python ecosystems
Conclusions and Subsequent Development Steps
Requirements
- Proficiency in Python programming at an intermediate level
- Working knowledge of fundamental statistical and data analysis methodologies
Target Audience
- Data analysts
- Data scientists
- Public sector professionals engaged in the management of temporal data for government decision-making
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Equipped with examples
