Which is actually not surprising, since all. (note frequency=12!), then ets(foo) again gives my an (M, A, M) model. For instance, if I do this:įoo <- ts(read.table("",header=TRUE),frequency=12,start=1949) My suspicion is that when you simply use the sequence of numbers you linked to, you did not specify that these are monthly data. The built-in dataset AirPassengers seems to contain the same numbers as the one you link to. When I type ets(AirPassengers), I get an (M, A, M) model, so seasonality is modeled multiplicatively. For the R function, you can inspect the code. Differences could arise from ets() choosing a different model, but even if it also uses an AAA model, smoothing parameters could be optimized differently (possibly even with differences in numerical optimization routines), or initializations could be different. This is different from R's ets(), which calculates an equivalent state space model. Best to read their help center first and show how far you have come on your own.Įxcel's FORECAST.ETS appears to do triple exponential smoothing with additive trend, seasonality and error. After 21 years of traveling the world and racking up the miles because travel on an AAirpass accrued miles, as well Rothstein was stripped of the pass in 2008 for speculative bookings, according to. If you still have questions, I'd recommend you ask a specific question in the R tag at StackOverflow. Rothstein, a successful stockbroker, purchased the AAirpass for a fee of 250,000, and a companion pass for an additional 150,000. Stepping through source code line by line is an extremely helpful exercise. have you looked at the source code of ets()? By simply typing ets, it appears like the actual model fitting is done by a function etsmodel that is not publicly exposed. $$\widehat$ - from our regression models.This is not really an answer, admittedly. If we select the length of the moving average as an odd number, for example $l = 3$, then: We will provide an example of the moving-average method for decomposition. Plot(cbind(ap, tsSmooth(fit)), plot.type = "single") Plot(cbind(ap, fitted(fit)), plot.type = "single") # full ML fit is the same if the series is reversed, CSS fit is notĪttributes(ap0) <- attributes(AirPassengers)Īrima(ap0, c(0, 1, 1), seasonal = list(order = c(0, 1, 1), period = 12))Īrima(ap0, c(0, 1, 1), seasonal = list(order = c(0, 1, 1), period = 12), Update(fit, x = window(log10(AirPassengers), start = 1954)) # The classic 'airline model', by full ML # These are quite slow and so not run by example(AirPassengers) (1976) *Time SeriesĪnalysis, Forecasting and Control.* Third Edition. Monthly Airline Passenger Numbers 1949-1960 DataFrame ( tmp_acor, index = col_index, columns = range ( 1, len ( tmp_acor ) + 1 )) show () # Return the statistics: col_index = return pd. annotate ( round ( Z, 4 ), xy = ( X, Y ), xytext = ( - 5, 5 ), ha = 'left', textcoords = 'offset points' ) plt. size ) + 1 for X, Y, Z in zip ( x, p_vals, p_vals ): plt. axhline ( y = 0.05, color = 'blue', linestyle = '-' ) # Annotate the p-value points above and to the left of the vertex x = np. add_subplot ( 313 ), linestyle = '', marker = 'o', title = "p-values for Ljung-Box statistic", legend = False ) #Add the horizontal 0.05 critical value line plt. figure ( figsize = figsize ) #Plot the p-values: p_vals. Series ( tmp_acor ) #Start the index from 1 instead of 0 (because Ljung-Box test is for lag values from 1 to k) p_vals. acorr_ljungbox ( tmp_data, lags = lags, boxpierce = True )) # get the p-values p_vals = pd. Series ( y ) #The Ljung-Box test results for the first k lags: tmp_acor = list ( sm_stat. Def tsdiag ( y, figsize = ( 14, 8 ), title = "", lags = 10 ): #The data: tmp_data = pd.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |