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A Regression analysis and Time Series decomposition

  1. Nov 28, 2016 #1
    If we can use Regression analysis to forecast, why do we use “Time Series Decomposition”?
    What's the difference between the 2?

    Thanks
     
  2. jcsd
  3. Nov 28, 2016 #2

    BvU

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    Hi Cloudi, :welcome:

    Maybe you want to be a bit more specific: forecasting is difficult, especially when it concerns the future. From a brief search (I can't really pinpoint time series decomposition -- it seems to be pretty general) I suspect regression analysis when applied to temporal data is one form of time series deco.
     
  4. Nov 28, 2016 #3
    Well I just started with regression, and there was this general question without specifics. I am just at the basics of the regression and multiple-regression now.
     
  5. Nov 28, 2016 #4

    MarneMath

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    Well, your question is a bit to broad to have a meaningful answer. There are many different methods for regression that each have their own limits and assumptions. For example, if you try a linear regression and your data has auto-correlation, this may skew your results. Decomposition of a time series is a useful way to discover and use seasonality and periodic information into your model. It's a fairly well known result that trying to add seasonality into your data via a dummy variable may actual detrend your results, so in that case a linear regression may not be the best method.
     
  6. Nov 28, 2016 #5

    FactChecker

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    Time series analysis addresses timing issues directly. Suppose that the Y value of one step is partially (or wholly) dependent on the prior step value of Y. The current values of input variables Xi will not explain all of the current Y value. In fact, you might have to go back a very long way to see where those prior Y influences got started. They may even be initial conditions with no explanation at all in terms of X values. And the influence of a prior Y value might skip several steps before they enter in. The same is true of prior X values, which might not take effect till several steps later. Time series analysis addresses those timing concerns directly and quantifies them. Addressing these issues with regression would be very difficult.
     
  7. Nov 28, 2016 #6
    Thank you both, that helps.
     
  8. Nov 28, 2016 #7

    chiro

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    Hi iCloud.

    Time series looks at a variety of correlation values in a very structured way while regression typically [and I say typically] doesn't include that information.

    When you do statistics you do inference on parameters and in time series, the parameters you use typically involve some sort of correlation vector where-as regression looks at estimating coefficients for some model in terms of a population expectation [so a mean as a function of the values of different variables].
     
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