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Time Series Forecasting in SAS

What's main difference between time series and classic regression analysis?

In time series analysis, the target/dependent variable is usually a function of own past. If we want to apply proc reg, then we need to apply lag function, that is, one of the independent variable would be : lag_y=lag(y).

Sometimes those lag effects get complex since we may need to lag different units, e.g. lag 7 days to get weekly seasonality, lag 12 to get monthly seasonality; or even more complicated when the difference (Y_t-Y_t-1) is also an independent variable.

To find out the appropriate lag effects and potential difference variables, we need to apply some procedures that is particular useful for time series, including: Proc ARIMA, Proc Timeseries, Proc AutoReg, Proc FORECAST, Proc ESM(Exponential smoothing models), Proc SPECTRA(Spectral analysis).

Preparing Data for Forecasting

    ◊ Read the raw transactional data.
    ◊ Convert the transactional data to time series data by
            accumulating the data to equally spaced time points.
    ◊ Plot the data.
        -- Identify data pathologies.
        -- Suggest forecasting approaches.
    ◊ Address data pathologies.
        -- Transform skewed data.
        -- Impute missing values.
        -- Detect unusual observations (outliers).

Example: We are using some stock data to demonstrate the data diagnostic process.

• Time Series in SAS
Slides presentation for "Time Series in SAS".


• Partial correlation network analysis & Bootstrap Resampling
Pdf slides presentation for
"Partial correlation network analysis & Bootstrap Resampling in SAS" .



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