Difference between revisions of "SMHS TimeSeriesAnalysis"
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==[[SMHS| Scientific Methods for Health Sciences]] - Time Series Analysis == | ==[[SMHS| Scientific Methods for Health Sciences]] - Time Series Analysis == | ||
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| + | <b>Questions</b> | ||
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| + | • Why are trends, patterns or predictions from models/data important? | ||
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| + | • How to detect, model and utilize trends in longitudinal data? | ||
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| + | Time series analysis represents a class of statistical methods applicable for series data aiming to extract meaningful information, trend and characterization of the process using observed longitudinal data. These trends may be used for time series forecasting and for prediction of future values based on retrospective observations. Note that classical linear modeling (e.g., regression analysis) may also be employed for prediction & testing of associations using the values of one or more independent variables and their affect the value of another variable. However, time series analysis allows dependencies (e.g., seasonal effects to be accounted for). | ||
Revision as of 10:39, 5 May 2016
Scientific Methods for Health Sciences - Time Series Analysis
Questions
• Why are trends, patterns or predictions from models/data important?
• How to detect, model and utilize trends in longitudinal data?
Time series analysis represents a class of statistical methods applicable for series data aiming to extract meaningful information, trend and characterization of the process using observed longitudinal data. These trends may be used for time series forecasting and for prediction of future values based on retrospective observations. Note that classical linear modeling (e.g., regression analysis) may also be employed for prediction & testing of associations using the values of one or more independent variables and their affect the value of another variable. However, time series analysis allows dependencies (e.g., seasonal effects to be accounted for).
