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edited Feb 16, 2018 at 21:13

From Ordinary Regression to Time Series Regression:

The time series regression model is an extension of the ordinary regression model in which the following conditions exist:

 Variables are observed in time.

 Autocorrelation is allowed.

 The target variable can be influenced by past values of inputs.

Source: DePaul University lecture slides for CSC 425

I think this answer is lacking in complete details, but is not wrong. @IrishStat gave a link to a document that covers the differences well. Together, these answer the first part of the original question.

I am still looking for answers to the latter half: does time series analysis share the assumptions of linear regression, plus some? For example, linear regression has multiple assumptions about X regressors such as no multicollinearity, linear relationship (correlation) to Y, the X regressors and model residuals are uncorrelated, etc. Do all of these still apply in time series analysis? If we could make a complete list of assumptions that these two methods sharemake a complete list of assumptions that these two methods share, that would be extremely helpful. Thanks everyone!

From Ordinary Regression to Time Series Regression:

The time series regression model is an extension of the ordinary regression model in which the following conditions exist:

 Variables are observed in time.

 Autocorrelation is allowed.

 The target variable can be influenced by past values of inputs.

Source: DePaul University lecture slides for CSC 425

I am still looking for answers to the latter half: does time series analysis share the assumptions of linear regression, plus some? For example, linear regression has multiple assumptions about X regressors such as no multicollinearity, linear relationship (correlation) to Y, the X regressors and model residuals are uncorrelated, etc. Do all of these still apply in time series analysis? If we could make a complete list of assumptions that these two methods share, that would be extremely helpful. Thanks everyone!

From Ordinary Regression to Time Series Regression:

The time series regression model is an extension of the ordinary regression model in which the following conditions exist:

 Variables are observed in time.

 Autocorrelation is allowed.

 The target variable can be influenced by past values of inputs.

Source: DePaul University lecture slides for CSC 425

I am still looking for answers to the latter half: does time series analysis share the assumptions of linear regression, plus some? For example, linear regression has multiple assumptions about X regressors such as no multicollinearity, linear relationship (correlation) to Y, the X regressors and model residuals are uncorrelated, etc. Do all of these still apply in time series analysis? If we could make a complete list of assumptions that these two methods share, that would be extremely helpful. Thanks everyone!

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created Feb 16, 2018 at 20:00

Renel Chesak

From Ordinary Regression to Time Series Regression:

The time series regression model is an extension of the ordinary regression model in which the following conditions exist:

 Variables are observed in time.

 Autocorrelation is allowed.

 The target variable can be influenced by past values of inputs.

Source: DePaul University lecture slides for CSC 425

I am still looking for answers to the latter half: does time series analysis share the assumptions of linear regression, plus some? For example, linear regression has multiple assumptions about X regressors such as no multicollinearity, linear relationship (correlation) to Y, the X regressors and model residuals are uncorrelated, etc. Do all of these still apply in time series analysis? If we could make a complete list of assumptions that these two methods share, that would be extremely helpful. Thanks everyone!