Following is a guideline to determine an accurate model to predict a time series.
1. Select the model on the basis of objective i.e. if the objective is to predict the future behavior of a variable based on the past behavior of the same variable, use Time series model and if the objective is to predict the future behavior of a variable based on assumed casual relationship with other variables Cross sectional model should be used.
2. When time-series model is used, plot the series to detect Covariance Stationarity in the data. Trends in the time series data include:
· A linear trend
· An exponential trend
· Structural change i.e. a significant shift in mean or variance of the time series during the sample period.
3. When there is no seasonality or structural change found in the data, linear trend or exponential trend is appropriate to use i.e.
i. Use linear trend model when the data plot on a straight line with an upward or downward slope.
ii. Use log-linear trend model when the plot of the data exhibits a curve.
iii. Estimate the regression model.
iv. Compute the residuals
v. Use Durbin-Watson statistic to test serial correlation in the residual.
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