fable-r

问题 Do you ever look back a your old questions and feel a bit embarrassed? I just did, and now I do. And I'll probably feel the same about this one at some point. I'm trying to move my forecasting work to fable . In the process I'm trying to use a tsibble . Previously with a ts object I just set the start year and frequency. Now the tsibble is looking for a date object. However I have data which is biannual (fall and spring semester). And the variable is irregular (which I would like to keep).

问题 I am trying to understand the different between two forecasting package forecast and fable , as the two editions of the same book (second edition and third edition seems to imply that the two packages are equivalent. library(dplyr) raw <- c(44.4082519001086, 47.1074380165289, 43.5633367662204, 43.1003584229391, 42.5828970331588, 38.3217993079585, 38.5751520417029) # raw <- c(raw,rev(raw)) forecast.df <- ts(raw) forecast::autoplot(forecast.df) + forecast::autolayer(forecast::holt(forecast.df

问题 I am trying to understand the different between two forecasting package forecast and fable , as the two editions of the same book (second edition and third edition seems to imply that the two packages are equivalent. library(dplyr) raw <- c(44.4082519001086, 47.1074380165289, 43.5633367662204, 43.1003584229391, 42.5828970331588, 38.3217993079585, 38.5751520417029) # raw <- c(raw,rev(raw)) forecast.df <- ts(raw) forecast::autoplot(forecast.df) + forecast::autolayer(forecast::holt(forecast.df

问题 I have a time series that looks like this t value 1 12 2 12 3 0 4 0 5 0 6 0 7 0 I expect acf1 to equal 0.443, but instead the accuracy function produces null. The code is as following: df = data.frame("t" = 1:7, "value" = c(12, 12, 0, 0, 0, 0, 0)) tsb = df %>% as_tsibble(index = t) md = tsb %>% model(arima = ARIMA(value ~ PDQ(period = 4), stepwise = F)) fc = md %>% forecast(h = 4) accuracy(fc, tsb) Why is this happening? 回答1: The ACF1 column from accuracy() is the first auto-correlation of

问题 I just started to use {fable} and the {tidyverts} family of tools and so far it's going quite well. I'm currently interested in producing long term probabilistic forecasts from daily data (with a monthly or quarterly resolution being fine or preferred). My understanding is, that temporal aggregation could help reduce model uncertainty and also propagate known daily influences (especially holiday effects) to the e.g. quarterly level and improve accuracy that way. For the daily data I plan on

问题 I just started to use {fable} and the {tidyverts} family of tools and so far it's going quite well. I'm currently interested in producing long term probabilistic forecasts from daily data (with a monthly or quarterly resolution being fine or preferred). My understanding is, that temporal aggregation could help reduce model uncertainty and also propagate known daily influences (especially holiday effects) to the e.g. quarterly level and improve accuracy that way. For the daily data I plan on

问题 I have a mable object that is like so: models # A mable: 1 x 3 ets arima nnetar <model> <model> <model> 1 <ETS(M,Ad,M)> <ARIMA(2,1,2)(0,0,2)[12]> <NNAR(14,1,10)[12]> I just want the models descriptions so I can place them in a plot. So I ran the following code: model_desc <- models %>% gather() %>% select(key, value) %>% set_names("model","model_desc") %>% mutate(model_desc_char = model_desc %>% as.character()) as_tibble() %>% select(model, model_desc) This still gives me back a tibble where

问题 I have a mable object that is like so: models # A mable: 1 x 3 ets arima nnetar <model> <model> <model> 1 <ETS(M,Ad,M)> <ARIMA(2,1,2)(0,0,2)[12]> <NNAR(14,1,10)[12]> I just want the models descriptions so I can place them in a plot. So I ran the following code: model_desc <- models %>% gather() %>% select(key, value) %>% set_names("model","model_desc") %>% mutate(model_desc_char = model_desc %>% as.character()) as_tibble() %>% select(model, model_desc) This still gives me back a tibble where