Single Exponential Smoothning #1279
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Since I somehow accidentally deleted the last file in statsmodels#1274 but still have the code I decided to start from scratch and make the code in Pep8 style and focus on each individual Exponential smoothing (single double and triple) separately. This will address statsmodels#512 and pandas-dev/pandas#2143 This code works good and can forecast for x amount of periods ahead using a bootstrapping method. Improvements that are needed: . *An solver for the alpha value using mean square error something like`sum(y-wdata)/len(y)` *the Summary part needs RMS, Sum of squares residuals, AIC/AICc/BIC, Log-Likelihood, Swartz Criterion, average mean square error, Hanna-Quinn. Here is what Eviews uses http://www.eviews.com/EViews8/ev8ecets_n.html Here is a set of test data to try ``` ############Test###################### y = [47, 36, 12, 4, 84, 26, 37, 40, 72, 22] print 'Exp=', ExpSmoothing(y, .5) ###forcasting print 'forcast 10=', ExpSmoothing(y, .5, forecast = 10) ``` There is also working code in the answers to test against (without forcasting) here: http://stackoverflow.com/questions/12726432/using-python-for-time-series-analysis-and-forecasting
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Updated your previous file #1287 |
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Is this superseded by #1287? If so, I'd like to close this. If not, is this still being worked on? If so, it will need some tests and examples. At least a MWE vs. R or Stata or something else. I'm going to close it soon, if there's not any movement. |
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Since I somehow accidentally deleted the last file in #1274 but still have the code
I decided to start from scratch and make the code in Pep8 style and focus on each individual Exponential smoothing (single double and triple) separately.
This will address #512 and pandas-dev/pandas#2143
This code works good and can forecast for x amount of periods ahead using a bootstrapping method.
Improvements that are needed: .
*An solver for the alpha value using mean square error something like
sum(y-wdata)/len(y)*the Summary part needs RMS, Sum of squares residuals, AIC/AICc/BIC, Log-Likelihood, Swartz Criterion, average mean square error, Hanna-Quinn.
Here is what Eviews uses
http://www.eviews.com/EViews8/ev8ecets_n.html
Here is a set of test data to try
There is also working code in the answers to test against (without forcasting) here:
http://stackoverflow.com/questions/12726432/using-python-for-time-series-analysis-and-forecasting