Time Series Analysis and Forecasting

• Time Series Decomposition

  Decompose time series data into trend, seasonality, and residual components.

• Stationarity Testing

  Understand and test for stationarity using statistical tests like ADF.

• Autocorrelation Analysis

  Analyze autocorrelation and partial autocorrelation functions (ACF/PACF) to identify model parameters.

• ARIMA Modeling

  Build and evaluate Autoregressive Integrated Moving Average (ARIMA) models for forecasting.

• Seasonal ARIMA (SARIMA)

  Implement SARIMA models to handle seasonal patterns in time series data.

• Model Evaluation

  Evaluate the performance of forecasting models using appropriate metrics.

There are no prerequisite courses for this course.

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