One of the assumptions of linear regression says that the errors must be independent i.e., the residuals must not depend on each other.
Let's say we are using linear regression to model the temperature on a given day. If it is 13:00 and 20 degrees, the temperature at 13:15 will be similar and thus depends on the time before it - it cannot suddenly fall to -20 in the space of 15 minutes. Likewise, the temperature at 20:00 will be more closely related to the temperature at 19:50 than 13:00.
Does the linear regression assumption 'independence of errors' mean that you cannot perform it on time series data?