Course Outline
Fundamentals of Time Series Analysis
- Overview of time series data structures
- Key components: trend, seasonality, and noise
- Configuration of Google Colab for time series workflows
Exploratory Data Analysis in Time Series
- Techniques for visualizing time series data
- Decomposition of time series into core components
- Identification of seasonality and underlying trends
Applying ARIMA Models for Forecasting
- Conceptual understanding of ARIMA (AutoRegressive Integrated Moving Average)
- Selection of optimal parameters for ARIMA models
- Coding ARIMA models using Python
Getting Started with Prophet for Forecasting
- Introduction to Prophet as a time series tool
- Implementation of Prophet models within Google Colab
- Incorporating holidays and special events into forecasts
Advanced Forecasting Strategies
- Management of missing data in time series
- Techniques for multivariate time series forecasting
- Enhancing forecasts using external regressors
Model Evaluation and Optimization
- Standard performance metrics for evaluating forecasts
- Tuning ARIMA and Prophet model parameters
- Application of cross-validation and backtesting methods
Practical Applications in Real World Scenarios
- Examination of time series forecasting case studies
- Practical exercises utilizing real-world datasets
- Pathways for advancing time series analysis in Python
Conclusion and Future Directions
Requirements
- Intermediate proficiency in Python programming
- Basic understanding of statistical concepts and data analysis methodologies
Target Audience
- Data analysts
- Data scientists
- Professionals engaged in handling time series data
Testimonials (3)
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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.
