SAS Time Series Exploration:
We use Time series exploration to get a statistical insight about the data which helps estimating the best time series model to fit and forecast.
The exploration task helps detecting stationarity through plotting, ACF, PACF and Unit Root Test results.
Analyses settings:– Time Series
– Series Histogram
– Seasonal Cycles
– Auto-correlation Analysis.
– Decomposition Analysis.
– Statistical analysis:
|Input Data Set|
|Length of Seasonal Cycle||1|
|Number of Observations Read||17|
|Time Series Descriptive Statistics|
|Number of Observations||17|
|Number of Missing Observations||0|
– Auto-Correlation analysis results:
ACF and PACF help detecting:
– trends: If there is a trend, the ACF will always be falling slowly, meaning Y’s of all lags will be correlated to the Yt.
– seasonality: When there is a seasonality of m, the ACF of lag m will have a spike.
– stationarity: For non-stationary, the ACF is falling slowing and PACF only has Lag1 spike of near 1 value.