1. Introducing ITSM 2000 By: Amir Heshmaty Far

2. S EVERAL FUNCTION IN ITSM to analyze and display the properties of time series data to compute and display the properties of time series models to combine these functions in order to fit models to data

3. P REPARING Y OUR D ATA FOR M ODELING Data file names should have the extension.TSM Data files should be located in the ITSM folder. For univariate analyses, all data should be stored in a single column--each value must be on a separate line. For multivariate analyses, the mvariate time series must be stored in m columns.

4. File>Project>Open File>Import File File>export press the red INFO button at the top of the ITSM window see the sample mean, sample variance, estimated standard deviation of the sample mean, and the current model (white noise with variance 1)

5. P LOTTING D ATA A time series graph is automatically plotted when you open a data file To see a histogram of the data press the rightmost yellow button at the top of the ITSM screen To insert any of the ITSM graphs into a text document, right-click on the graph concerned, select Copy to Clipboard

6. T RANSFORMING DATA Transformations are applied in order to produce data that can be successfully modeled as “ stationary time series.” they are used to eliminate trend and cyclic components and to achieve approximate constancy of level and variability with time a realization of a stationary time series using one or more of the transformations available for this purpose in ITSM.

7. B OX –C OX TRANSFORMATIONS Box–Cox transformations are performed by selecting Transform>Box-Cox specifying the value of the Box–Cox parameter λ The choice of λ can be made visually by watching the graph of the data Very often it is found that no transformation is needed or that the choice λ 0 is satisfactory.

8. C LASSICAL D ECOMPOSITON select Transform>Classical To remove a seasonal component and trend, check the Seasonal Fit and Polynomial Fit boxes choose between the alternatives Quadratic Trend and Linear Trend. An alternative approach is to use the option Regression, which allows the specification and fitting of polynomials of degree up to 10 and a linear combination

9. where Xt is the observation at time t, mt is a “trend component,” st is a “seasonal component,” and Yt is a “random noise component,” which is stationary with mean zero. The objective is to estimate the components mt and st and subtract them from the data to generate a sequence of residuals

10. Transform>Difference Differencing is a technique that can also be used to remove seasonal components and trends A linear trend can be eliminated by differencing at lag 1, and a quadratic trend by differencing twice at lag 1 (i.e., differencing once to get a new series, then differencing the new series to get a second new series)

11. S UBTRACTING THE M EAN subtract the sample mean of the transformed data from each observation Transform>Subtract Mean

12. F INDING A M ODEL FOR Y OUR D ATA After transforming the data Autofit: Model>Estimation>Autofit The Sample ACF and PACF Entering a Model Model>Specify Model>Estimation>Preliminary

13. If no model is entered or estimated, ITSM assumes the default ARMA(0,0), or white noise, model

14. P RELIMINARY E STIMATION eventual aim is to find a model with as small an AICC value as possible Smallness of the AICC value computed in the preliminary estimation phase is indicative of a good model, but should be used only as a rough guide Final decisions between models should be based on maximum likelihood estimation efficient parameter estimation by selecting Model>Estimation>Max likelihood

15. T ESTING Y OUR M ODEL Statistics>Residual Analysis>Histogram If the fitted model is appropriate, the histogram of the rescaled residuals should have mean close to zero If in addition the data are Gaussian, this will be reflected in the shape of the histogram Statistics>Residual Analysis>Plot Look for trends, cycles, and non constant variance, any of which suggest that the fitted model is inappropriate

16. If substantially more than 5% of the rescaled residuals lie outside the bounds ±1.96 or if there are rescaled residuals far outside these bounds, then the fitted model should not be regarded as Gaussian Compatibility of the distribution of the residuals with either the normal distribution or the t- distribution can be checked by inspecting the corresponding qq plots and checking for approximate linearity

17. Statistics>Residual Analysis>ACF/PACF the sample ACF and PACF of the observed residuals should lie within the bounds ±1.96/ √ n roughly 95% of the time These bounds are displayed on the graphs of the ACF and PACF. If substantially more than 5% of the correlations are outside these limits, or if there are a few very large values, then we should look for a better-fitting model

18. P REDICTION Model>Simulate…..generating One of the main purposes of time series modeling is the prediction of future observations. Once you have found a suitable model for your data, you can predict future values using the option Forecasting>ARMA number of forecasts required whether or not you wish to plot prediction bounds (assuming normality) the confidence level required, e.g., 95%.