Analyzing Time-Series Data: What Model to Use?

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SUMMARY

The discussion focuses on analyzing time-series data using statistical models to determine the impact of data points across 36 time periods. Chi-Square analysis is deemed unsuitable due to the absence of expected frequencies, while regression analysis is suggested as a more appropriate method for prediction and forecasting. The proposed regression model is defined as y(i, t) = a + b y(i, t-1), where y(i,t) represents the observations in each period. Additional resources for SAS users are provided for further exploration of outlier detection in regression analysis.

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Data analysts, statisticians, and researchers focused on time-series analysis and forecasting who seek to understand the impact of various data points over time.

colby2152
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I want to test a series of 36 time periods and 10 data points for each one of those periods. The goal is to see which data points have the biggest effect on change from one period to the next.

I thought Chi-Square was the way to go, but I do not have any expected frequencies here. Regression analysis is more of a prediction/forecasting model? What statistical model should I use here?
 
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A decent stats package will provide outlier detection as part of their basic regression subroutine.

For SAS, see:http://www2.sas.com/proceedings/sugi24/Infovis/p161-24.pdf and http://www2.tltc.ttu.edu/westfall/images/5349/outliers_what_to_do.htm

Your regression model could be y(i, t) = a + b y(i, t-1) where y(i,t) (i = 1, ..., 10) corresponds to the 10 different observations in period t. (Similarly for t-1.)
 
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