Time Series Analysis & Forecasting Certification

Module 1: Introduction to Time Series Analysis

• Time series fundamentals
• Time-dependent data
• Forecasting concepts
• Time series applications
• Forecasting workflow
• Business use cases

Module 2: Understanding Time Series Data

• Time indexes
• Frequency
• Observations
• Regular time series
• Irregular time series
• Temporal data structures

Module 3: Python for Time Series Analysis

• Python analytics ecosystem
• Pandas
• NumPy
• Matplotlib
• Statsmodels
• Time series workflows

Module 4: Time Series Data Preparation

• Date-time conversion
• Time indexing
• Sorting observations
• Missing timestamps
• Missing values
• Data validation

Module 5: Time Series Components

• Trend
• Seasonality
• Cyclical patterns
• Irregular components
• Level
• Component interpretation

Module 6: Time Series Visualization

• Line plots
• Seasonal plots
• Rolling statistics
• Period comparisons
• Distribution analysis
• Pattern visualization

Module 7: Time Series Decomposition

• Decomposition concepts
• Additive models
• Multiplicative models
• Trend extraction
• Seasonal extraction
• Residual analysis

Module 8: Stationarity

• Stationarity concepts
• Mean stability
• Variance stability
• Differencing
• Transformations
• Stationarity testing

Module 9: Autocorrelation Analysis

• Autocorrelation
• Lag concepts
• ACF
• Partial autocorrelation
• PACF
• Lag interpretation

Module 10: Moving Average Forecasting

• Moving averages
• Rolling windows
• Simple moving average
• Weighted moving average
• Smoothing
• Forecast generation

Module 11: Exponential Smoothing

• Simple exponential smoothing
• Holt's method
• Holt-Winters method
• Trend modelling
• Seasonal modelling
• Smoothing parameters

Module 12: Autoregressive Models

• Autoregression
• Lagged variables
• AR models
• Model order
• Parameter estimation
• Forecasting

Module 13: ARIMA Models

• ARIMA fundamentals
• AR components
• Differencing
• MA components
• Model identification
• ARIMA forecasting

Module 14: Seasonal ARIMA

• Seasonal patterns
• SARIMA concepts
• Seasonal differencing
• Seasonal parameters
• Model configuration
• Seasonal forecasting

Module 15: Forecast Model Diagnostics

• Residual analysis
• Residual autocorrelation
• Error distribution
• Model assumptions
• Diagnostic plots
• Model refinement

Module 16: Forecast Accuracy & Evaluation

• Train-test splitting
• Time-based validation
• MAE
• MSE
• RMSE
• MAPE

Module 17: Multivariate Time Series

• Multiple variables
• Cross-variable relationships
• Lagged predictors
• VAR concepts
• External regressors
• Multivariate forecasting

Module 18: Machine Learning for Forecasting

• Supervised forecasting
• Lag features
• Rolling features
• Regression models
• Tree-based models
• Forecasting pipelines

Module 19: Advanced Forecasting Approaches

• Gradient boosting
• XGBoost concepts
• Prophet concepts
• Neural forecasting concepts
• Model comparison
• Hybrid forecasting

Module 20: Time Series Anomaly Detection

• Anomaly concepts
• Point anomalies
• Seasonal anomalies
• Statistical detection
• Residual-based detection
• Monitoring applications

Module 21: Forecasting Strategy & Deployment

• Forecast horizons
• Prediction intervals
• Rolling forecasts
• Model updating
• Forecast monitoring
• Production considerations

Module 22: Practical Forecasting Project

• Problem definition
• Data preparation
• Exploratory time series analysis
• Model development
• Forecast evaluation
• Forecast presentation