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I am developing an app for time series analysis that should support the following:

  • Exponential Smoothing (Holt-Winters)
  • Box-Jenkins
  • curve fitting (straight line, quadratic, exponential, growth)
  • multiple regression
  • Croston's intermittent demand model and discrete data models
  • new product forecasting (Bass diffusion)

Are you familiar with open source stat packages other than R, that supports these models? (python?)

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Add features that test the normality of the errors which would include 1) Intervention Detection to disclose Pulses/Level Shifts/Seasonal Pulses 2) non-constant variance of the errors due to changes in parameters over time or due to break-points in variance suggesting the need for power transformations and/or Garch .You should also be looking for automatic detection of lead and lag structureSs (PDL) around user-specified causal/regressor series.Additionally being able to sort out the need for ARIMA versus fixed predictors e.g.day-of-the-week would along with optimal ARIMA structure. –  IrishStat Mar 24 '12 at 19:58
    
Did you find any packages for these models? –  user14394 Sep 26 '12 at 14:35
    
R is really going to be your best bet here for open source. –  Zach Sep 26 '12 at 16:06
    
@lilile Yes AUTOBOX . Available from Automatic Forecasting Systems .They have 30 day demo available at autobox.com/30day.exe . –  IrishStat Sep 26 '12 at 19:03
    
@lililie I should have mentioned I am one of the developers of this program. –  IrishStat Sep 26 '12 at 20:10
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