Times series analysis vs. machine learning? Just a general question. If you have time series data, when is it better to use time series techniques (aka, ARCH, GARCH, etc) over machine/statistical learning techniques (KNN, regression)? If there is a similar question that exists on crossvalidated, please point me towards it--looked and couldn't find one.
 A: As @Tom Minka pointed out most ML techniques assume iid inputs. There are a few solutions though:


*

*One can use all the past time series samples within the system 'Memory' as one feature vector, ie: x=[x(t-1),x(t-2),...x(t-M)]. However, this has 2 issues: 1) depending on your binning, you may have a huge feature vector 2- some methods require features within the feature vector to be independent, which isn't the case here. 

*There exist many ML techniques which are specifically designed for such time-series data, for example Hidden Markov Models, which have been used very successfully for seizure detection, speech processing, etc...

*Finally, an approach I have taken is to use 'feature extraction' techniques to convert a dynamic regression problem (which has the element of time) into a static one. For example, the Principal Dynamics Mode (PDM) approach maps the input past feature vector ([x(t-1),x(t-2),...x(t-M)]) onto a static one ([v(1),v(2),..v(L)]) by convolving the past with a system-specific linear filterbank (the PDMs),  see Marmarelis, 2004 book or Marmarelis, Vasilis Z. "Modeling methodology for nonlinear physiological systems." Annals of biomedical engineering 25.2 (1997): 239-251... 
A: Francis Diebold recently posted "ML and Metrics VI: A Key Difference Between ML and TS Econometrics" on his blog. I am providing a shortened version of it, so all credit goes to him. (Emphasis in bold is mine.)

[S]tatistical machine learning (ML) and time series econometrics (TS)
  have lots in common. But there's also an interesting difference: ML's
  emphasis on flexible nonparametric modeling of conditional-mean
  nonlinearity doesn't play a big role in TS. <...> 
[T]here's very
  little evidence of important conditional-mean nonlinearity in the
  covariance-stationary (de-trended, de-seasonalized) dynamics of most
  economic time series. <...> Indeed I can think of only one type of
  conditional-mean nonlinearity that has emerged as repeatedly important
  for (at least some) economic time series: Hamilton-style
  Markov-switching dynamics.
[Of course there's a non-linear elephant in the room:  Engle-style
  GARCH-type dynamics. They're tremendously important in financial
  econometrics, and sometimes also in macro-econometrics, but they're
  about conditional variances, not conditional means.]
So there are basically only two important non-linear models in TS, and
  only one of them speaks to conditional-mean dynamics. And crucially,
  they're both very tightly parametric, closely tailored to specialized
  features of economic and financial data.

Thus the conclusion is:

ML emphasizes approximating non-linear conditional-mean functions in
  highly-flexible non-parametric fashion. That turns out to be doubly
  unnecessary in TS: There's just not much conditional-mean
  non-linearity to worry about, and when there occasionally is, it's
  typically of a highly-specialized nature best approximated in
  highly-specialized (tightly-parametric) fashion.

I recommend reading the whole original post here.
A: Typical machine learning methods assume that your data is independent and identically distributed, which isn't true for time series data.  Therefore they are at a disadvantage compared to time series techniques, in terms of accuracy.  For examples of this, see the previous questions Ordering of time series for machine learning and Random forest is overfitting.
A: As said in other answers, ML methods required the i.i.d assumption and therefore are unsuitable to be used naively for time series. Not-naive ways have been suggested by DankMasterDan.
There is a further option, which is to use supervised predictors inside time-series forecasting. A good overview is found in the Sktime documentation: Forecasting, supervised regression, and pitfalls in confusing the two.
