What are the relation and differences between time series and linear regression? What are the relation and differences between time series and linear regression?
I have a strong grasp of linear regression, and a beginner's grasp on time series analysis; I know the Box-Jenkins method and understand the concepts. To solidify this understanding, I would like to compare and contrast the two methods to understand if time series analysis is an extension of linear regression.
Maybe the best way to answer this question is to compare and contrast the model assumptions of each method. Does time series analysis share all assumptions of linear regression, with a few extra assumptions added in (related to autocorrelation, stationarity, etc.)?
Note: This question has been asked here but the answers go off-topic and discuss the flaws of a Cornell professor's understanding of time series analysis. I do not have enough reputation to comment on that thread.
 A: In the context of Statistics, linear regression is solved by maximizing the likliehood that the error of a model linear in basis is the mean of a Normal Distribution. During maximization we assume the observations are independently and identically distributed, clearly not a reasonable assumption for times series data.
A: 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 share, that would be extremely helpful. Thanks everyone!
