Learn practical techniques for analyzing time-dependent data and building forecasting models. This course covers essential concepts such as decomposition, stationarity, autocorrelation, and applying statistical models like ARIMA and SARIMA for prediction.
Prerequisites Basic Python and Pandas
Level:
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.
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