Module tsa

Modules§

arima: ARIMA (AutoRegressive Integrated Moving Average) models
decomposition: Time series decomposition methods
forecast: Forecasting evaluation and prediction intervals
garch: GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models
prelude
results: Unified result structures for time series analysis
statespace: State space models and Kalman filter
stationarity: Stationarity tests for time series
timeseries: Time series data structure with datetime indexing
utils: Utility functions for time series analysis

ADFTest: Augmented Dickey-Fuller test
ARIMA: ARIMA model
ARIMAResults: ARIMA model results
DecompositionResults: Decomposition results
ForecastMetrics: Forecast evaluation metrics
GARCH: GARCH model
GARCHOrder: GARCH model order
GARCHResults: GARCH model results
HodrickPrescottFilter: Hodrick-Prescott filter for separating trend from cycle
KPSSTest: KPSS test for stationarity
KalmanFilter: Kalman filter implementation
ModelComparison: Model comparison results
MovingAverageDecomposition: Classical decomposition using moving averages
PPTest: Phillips-Perron test
PredictionInterval: Prediction interval
PredictionIntervals: Prediction intervals for multiple forecasts
ResidualDiagnostics: Residual diagnostics
SARIMABuilder: SARIMA model builder
SARIMAOrder: Seasonal ARIMA model order
STLDecomposition: STL (Seasonal-Trend decomposition using LOESS)
StateSpaceModel: State space model specification
TSAResults: Unified time series analysis results
TimeSeries: Time series data structure
TimeSeriesCV: Cross-validation for time series
X12ARIMA: X-12-ARIMA seasonal adjustment (simplified implementation)