Version 3.05 (3 October 2011)
  * Fixed bug in ets() which prevent non-seasonal models being fitted to high frequency data.

Version 3.04 (23 September 2011)
  * Fixed bug when drift and xreg used together in auto.arima() or Arima().

Version 3.03 (2 September 2011)
  * Bug fix in dshw() which was using slightly incorrect seasonal estimates for the forecasts
  * Bug fix in forecast.StructTS due to change in structure of StructTS object.
	* Better error capture in tslm when seasonal dummies are specified for non-seasonal data.
  * Re-formatted some help files to prevent viewing problems with the pdf manual.

Version 3.02 (25 August 2011)
  * Bug fixes

Version 3.00 (24 August 2011)
	* Added Box-Cox parameter as argument to Arima(), ets(), arfima(), stlf(), rwf(), meanf(), splinef()
	* Added Box-Cox parameter as argument to forecast.Arima(), forecast.ets(), forecast.fracdiff(), forecast.ar(), forecast.StructTS, forecast.HoltWinters().
	* Removed lambda argument from plot.forecast() and accuracy().
  * Added BoxCox.lambda() function to allow automatic choice for Box-Cox parameter using Guerrero's method or the profile log likelihood method.
	* Modified BoxCox and InvBoxCox to return missing values when lambda < 0 and data < 0.
  * Add nsdiffs() function for selecting the number of seasonal differences.
  * Modified selection of seasonal differencing in auto.arima().
	* Better error message if seasonal factor used in tslm() with non-seasonal data.
	* Added PI argument to forecast.ets() to allow only point forecasts to be computed.
	* Added include.constant argument to Arima(). 
	* Added subset.ts() function.
	* Upgraded seasonplot() function to allow colors and to fix some bugs.
	* Fixed fitted values returned by forecast.HoltWinters()
	* Modified simulate.Arima() because of undocumented changes in filter() function in stats package.
	* Changed residuals returned by splinef() to be ordinary residuals. The standardized residuals are now returned as standardizedresiduals.
	* Added dshw() function for double-seasonal Holt-Winters method based on Taylor (2003).
  * Fixed further bugs in the decompose() function that caused the results to be incorrect with odd frequencies.

Version 2.19 (4 June 2011)
  * Added xreg information to the object returned by auto.arima().
	* Added Acf(), Pacf(), ma() and CV() functions.
	* Fixed bugs in re-fitting ARIMA models to new data.

Version 2.18 (19 May 2011)
  * Fixed bug in seasonplot() where year labels were sometimes incorrect.
	
Version 2.17 (6 April 2011)
  * Modified simulate.Arima() to handle seasonal ARIMA models.
  * Modified ets() to handle missing values. The largest continuous section of data is now modelled.
  * Improved plot.forecast() to handle missing values at the end of the observed series.
  * Added replacement decompose() to avoid truncation of seasonal term and seasonally adjusted series.
  * Fixed bug in seasadj() to handle multiplicative decomposition, and to avoid missing values at ends.
  
Version 2.16 (6 March 2011)
  * Changed the way missing values are handled in tslm()
    
Version 2.15 (5 March 2011)
  * Added fourier(), fourierf(), tslm()
  * Improved forecast.lm() to allow trend and seasonal terms.

Version 2.14 (4 March 2011)
  * Added forecast.lm()
  * Modified accuracy() and print.forecast() to allow non time series forecasts.
  * Fixed visibility of stlf().

Version 2.13 (16 February 2011)
  * Fixed bug in accuracy() when only 1 forecast is specified.
  * Added forecast.stl() and stlf() functions
  * Modified forecast.ts() to use stlf() if frequency > 12.
  * Made BoxCox() and InvBoxCox() robust to negative values
  * Fixed bug in simulate.Arima() when future=TRUE. There was a bias in the sample paths.
    
Version 2.12 (19 January 2011)
  * Added naive() and snaive() functions.
  * Improved handling of seasonal data with frequency < 1.
  * Added lambda argument to accuracy().
    
Version 2.11 (5 November 2010)
  * If MLE in arfima() fails (usually because the series is non-stationary), the LS estimate is now returned.
    
Version 2.10 (4 November 2010)
  * Fixed bug in arfima() where the MA parameters were of the wrong sign if estim="mle" chosen.
  * arfima() now allowed to have a sequence of missing values at the start of the series and end of the series (but not within the series)

Version 2.09 (15 October 2010)
  * Fixed bug in forecast.fracdiff() which caused an error when h=1.
  * Added shadebars to plot.forecast().
  * Fixed bug in plot.forecast() to allow plotting when h=1.

Version 2.08 (22 September 2010)
  * Added pp test option for auto.arima() and ndiffs().
	* Fixed bug in simulate.ets() which was causing problems when forecasting from some ETS models including ETS(M,M,N).

Version 2.07 (9 September 2010)
  * Fixed bug in simulate.Arima(). Previous sample paths when d=2 and future=TRUE were incorrect.
  * Changed way color is implemented in plot.forecast() to avoid colour changes when the graphics window is refreshed.

Version 2.06 (29 July 2010)
  * Added MLE option for arfima().
  * Added simulate.Arima(), simulate.ar() and simulate.fracdiff()

Version 2.05 (11 May 2010)
  * Added arfima() and a forecast method to handle ARFIMA models from arfima() and fracdiff().
  * Added residuals and fitted methods for fracdiff objects.
	
Version 2.04 (16 April 2010)
  * Fixed bug in auto.arima() that occurred rarely.

Version 2.03 (23 December 2009)
  * Added an option to auto.arima() to allow drift terms to be excluded from the models considered.

Version 2.02 (23 December 2009)
  * Fixed bug in auto.arima() that occurred when there was an xreg but no drift, approximation=TRUE and stepwise=FALSE.

Version 2.01 (14 September 2009)
  * Fixed bug in time index of croston() output.
  * Added further explanation about models to croston() help file.

Version 2.00 (7 September 2009)
  * Package removed from forecasting bundle

Version 1.26 (29 August 2009)
  * Added as.data.frame.forecast(). This allows write.table() to work for forecast objects.

Version 1.25 (22 July 2009)
  * Added argument to auto.arima() and ndiffs() to allow the ADF test to be used instead of the KPSS test in selecting the number of differences.
  * Added argument to plot.forecast() to allow different colors and line types when plotting prediction intervals.
  * Modified forecast.ts() to give sensible results with a time series containing fewer than four observations.

Version 1.24 (9 April 2009)
  * Fixed bug in dm.test() to avoid errors when there are missing values in the residuals.
  * More informative error messages when auto.arima() fails to find a suitable model.

Version 1.23 (22 February 2009)
  * Fixed bugs that meant xreg terms in auto.arima() sometimes caused errors when stepwise=FALSE.

Version 1.22 (30 January 2009)
  * Fixed bug that meant regressor variables could not be used with seasonal time series in auto.arima().

Version 1.21 (16 December 2008)
  * Fixed bugs introduced in v1.20.

Version 1.20 (14 December 2008)
  * Updated auto.arima() to allow regression variables.
  * Fixed a bug in print.Arima() which caused problems when the data were inside a data.frame.
  * In forecast.Arima(), argument h is now set to the length of the xreg argument if it is not null.

Version 1.19 (7 November 2008)
  * Updated Arima() to allow regression variables when refitting an existing model to new data.

Version 1.18 (6 November 2008)
  * Bug fix in ets(): models with frequency less than 1 would cause R to hang.
  * Bug fix in ets(): models with frequency greater than 12 would not fit due to parameters being out of range.
  * Default lower and upper bounds on parameters ,  and  in ets() changed to 0.0001 and 0.9999 (instead of 0.01 and 0.99).

Version 1.17 (10 October 2008)
  * Calculation of BIC did not account for reduction in length of series due to differencing. Now fixed in auto.arima() and in print.Arima().
  * tsdiag() now works with ets objects.

Version 1.16 (29 September 2008)
  * Another bug fix in auto.arima(). Occasionally the root checking would cause an error. The condition is now trapped.

Version 1.15 (16 September 2008)
  * Bug fix in auto.arima(). The series wasn’t always being stored as part of the return object when stepwise=FALSE.

Version 1.14 (1 August 2008)
  * The time series stored in M3 in the Mcomp package did not contain all the components listed in the help file. This problem has now been fixed.

Version 1.13 (16 June 2008)
  * Bug in plot.ets() fixed so that plots of non-seasonal models for seasonal data now work.
  * Warning added to ets() if the time series contains very large numbers (which can cause numerical problems). Anything up to 1,000,000 should be ok, but any larger and it is best to scale the series first.
  * Fixed problem in forecast.HoltWinters() where the lower and upper limits were interchanged.

Version 1.12 (22 April 2008)
  * Objects are now coerced to class ts in ets(). This allows it to work with zoo objects.
  * A new function dm.test() has been added. This implements the Diebold-Mariano test for predictive accuracy.
  * Yet more bug-fixes for auto.arima().

Version 1.11 (8 February 2008)
  * Modifications to auto.arima() in the case where ML estimation does not work for the chosen model. Previously this would return no model. Now it returns the model estimated using CSS.
  * AIC values reported in auto.arima() when trace=TRUE and approximation=TRUE are now comparable to the final AIC values.
  * Addition of the expsmooth package.

Version 1.10 (21 January 2008)
  * Fixed bug in seasadj() so it allows multiple seasonality
  * Fixed another bug in print.Arima()
  * Bug fixes in auto.arima(). It was sometimes returning a non-optimal model, and occasionally no model at all. Also, additional stationarity and invertibility testing is now done.

Version 1.09 (11 December 2007)
  * A new argument “restrict” has been added to ets() with default TRUE. If set to FALSE, then the unstable ETS models are also allowed.
  * A bug in the print.Arima() function was fixed.

Version 1.08 (21 November 2007)
  * AICc and BIC corrected. Previously I had not taken account of the sigma^2 parameter when computing the number of parameters.
  * arima() function changed to Arima() to avoid the clash with the arima() function in the stats package.
  * auto.arima now uses an approximation to the likelihood when selecting a model if the series is more than 100 observations or the seasonal period is greater than 12. This behaviour can be over-ridden via the approximation argument.
  * A new function plot.ets() provides a decomposition plot of an ETS model.
  * predict() is now an alias for forecast() wherever there is not an existing predict() method.
  * The argument conf has been changed to level in all forecasting methods to be consistent with other R functions.
  * The functions gof() and forecasterrors() have been replaced by accuracy() which handles in-sample and out-of-sample forecast accuracy.
  * The initialization method used for a non-seasonal ETS model applied to seasonal data was changed slightly.
  * The following methods for ets objects were added: summary, coef and logLik.
  * The following methods for Arima objects were added: summary.

Version 1.07 (25 July 2007)
  * Bug fix in summary of in-sample errors. For ets models with multiplicative errors, the reported in-sample values of MSE, MAPE, MASE, etc., in summary() and gof() were incorrect.
  * ARIMA models with frequency greater than 49 now allowed. But there is no unit-root testing if the frequency is 50 or more, so be careful!
  * Improvements in documentation.

Version 1.06 (15 June 2007)
  * Bug fix in auto.arima(). It would not always respect the stated values of max.p, max.q, max.P and max.Q.
  * The tseries package is now installed automatically along with the forecasting bundle, whereas previously it was only suggested.

Version 1.05 (28 May 2007)
  * Introduced auto.arima() to provide a stepwise approach to ARIMA modelling. This is much faster than the old best.arima().
  * The old grid-search method used by best.arima() is still available by using stepwise=FALSE when calling auto.arima().
  * Automated choice of seasonal differences introduced in auto.arima().
  * Some small changes to the starting values of ets() models.
  * Fixed a bug in applying ets() to new data using a previously fitted model.

Version 1.04 (30 January 2007)
  * Added include.drift to arima()
  * Fixed bug in seasonal forecasting with ets()

Version 1.03 (20 October 2006)
  * Fixed some DOS line feed problems that were bothering unix users.

Version 1.02 (12 October 2006)
  * Added AICc option to ets() and best.arima().
  * Corrected bug in calculation of fitted values in ets models with multiplicative errors.

Version 1.01 (25 September 2006)
  * Modified ndiffs() so that the maximum number of differences allowed is 2.

Version 1.0 (31 August 2006)
  * Added MASE to gof().
  * croston() now returns fitted values and residuals.
  * arima() no longer allows linear trend + ARMA errors by default. Also, drift in non-stationary models can be turned off.
  * This version is the first to be uploaded to CRAN.

Version 0.99992 (8 August 2006)
  * Corrections to help files. No changes to functionality.

Version 0.99991 (2 August 2006)
  * More bug fixes. ets now converges to a good model more often.

Version 0.9999 (1 August 2006)
  * Mostly bug fixes.
  * A few data sets have been moved from fma to forecast as they are not used in my book.
  * ets is now considerably slower but gives better results. Full optimization is now the only option (which is what slows it down). I had too many problems with poor models when partial optimization was used. I’ll work on speeding it up sometime, but this is not a high priority. It is fast enough for most use. If you really need to forecast 1000 series, run it overnight.
  * In ets, I’ve experimented with new starting conditions for optimization and it seems to be fairly robust now.
  * Multiplicative error models can no longer be applied to series containing zeros or negative values. However, the forecasts from these models are not constrained to be positive.

Version 0.999 (27 July 2006)
  * The package has been turned into three packages forming a bundle. The functions and a few datasets are still in the forecast package. The data from Makridakis, Wheelwright and Hyndman (1998) is now in the fma package. The M-competition data is now in the Mcomp package. Both fma and Mcomp automatically load forecast.
  * This is the first version available on all operating systems (not just Windows).
  * pegels has been replaced by ets. ets only fits the model; it doesn’t produce forecasts. To get forecasts, apply the forecast function to the ets object.
  * ets has been completely rewritten which makes it slower, but much easier to maintain. Different boundary conditions are used and a different optimizer is used, so don’t expect the results to be identical to what was done by the old pegels function. To get something like the results from the old pegels function, use forecast(ets(…)).
  * simulate.ets() added to simulate from an ets model.
  * Changed name of cars to auto to avoid clash with the cars data in the datasets package.
  * arima2 functionality is now handled by arima() and pegels2 functionality is now handled by ets.
  * best.arima now allows the option of BIC to be used for model selection.
  * Croston’s method added in function croston().
  * ts.display renamed as tsdisplay
  * mean.f changed to meanf, theta.f changed to thetaf, rw.f changed to rwf, seasonaldummy.f to seasonaldummyf, sindex.f to sindexf, and spline.f to splinef. These changes are to avoid potential problems if anyone introduces an “f” class.

Version 0.994 (4 October 2004)
  * Fixed bug in arima which caused predict() to sometimes fail when there was no xreg term.
  * More bug fixes in handling regression terms in arima models.
  * New print.Arima function for more informative output.

Version 0.993 (20 July 2004)
  * Added forecast function for structural time series models obtained using StructTS().
  * Changed default parameter space for pegels() to force admissibility.
  * Added option to pegels() to allow restriction to models with finite forecast variance. This restriction is imposed by default.
  * Fixed bug in arima.errors(). Changes made to arima() meant arima.errors() was often returning an error message.
  * Added a namespace to the package making fewer functions visible to the user.

Version 0.99 (21 May 2004)
  * Added automatic selection of order of differencing for best.arima.
  * Added possibility of linear trend in arima models.
  * In pegels(), option added to allow parameters of an exponential smoothing model to be in the “admissible” (or invertible) region rather than within the usual (0,1) region.
  * Fixed some bugs in pegels.
  * Included all M1 and M3 data and some functions to subset and plot them.
  * Note: This package will only work in R1.9 or later.

Version 0.98 (23 August 2003)
  * Added facilities in pegels.
      o It is now possible to specify particular values of the smoothing parameters rather than always use the optimized values. If none are specified, the optimal values are still estimated as before.
      o It is also possible to specify upper and lower bounds for each parameter separately.
  * New function: theta.f. This implements the Theta method which did very well in the M3 competition.
  * A few minor problems with pegels fixed and a bug in forecast.plot that meant it didn’t work when the series contained missing values.

Version 0.972 (11 July 2003)
  * Small bug fix: pegels did not return correct model when model was partially specified.

Version 0.971 (10 July 2003)
  * Minor fixes to make sure the package will work with R v1.6.x. No changes to functionality.

Version 0.97 (9 July 2003)
  * Fully automatic forecasting based on the state space approach to exponential smoothing has now been added. For technical details, see Hyndman, Koehler, Snyder and Grose (2002).
  * Local linear forecasting using cubic smoothing splines added. For technical details, see Hyndman, King, Pitrun and Billah (2002).

Version 0.96 (15 May 2003)
  * Many functions rewritten to make use of methods and classes. Consequently several functions have had their names changed and many arguments have been altered. Please see the help files for details.
  * Added functions forecast.Arima and forecat.ar
  * Added functions gof and seasadj
  * Fixed bug in plot.forecast. The starting date for the plot was sometimes incorrect.
  * Added residuals components to rw.f and mean.f.
  * Made several changes to ensure compatibility with Rv1.7.0.
  * Removed a work-around to fix a bug in monthplot command present in R v<=1.6.2.
  * Fixed the motel data set (columns were swapped)