Stack Overflow
Stack Overflow

Questions tagged [fable-r]

R package for tidy time series forecasting (fable: Forecasting Models for Tidy Time Series)

fable: Forecasting Models for Tidy Time Series

Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models.

These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.

136 questions
0
votes
0 answers

How do I fix error in ets when forecasting?

dput(Training[1:10, ]) Hopefully I've done this right, still all incredibly new and intimidating! using the following packages: (tidyverse) (fable) (feasts) (fpp3) (knitr) (rmarkdown) structure(list(Week = structure(c(17532, 17539, 17546, 17553,…
Joseph Droogan
  • 1
  • 1
0
votes
0 answers

Forecasting future observations based off of lowest RMSE models

I asked this question over at RStudio community and received no answer so I figured I'd give it a go here. My question pertains to what budugulo asked here Select models with lowest RMSE but I'm wondering how I can go further and use the models with…
cap
  • 1
0
votes
1 answer

R fable examples not running

I am trying to get started with the fable package in R for forecasting. I am using the basic examples from their website: I am trying to run the following example but it doesn't ever run, it runs eternally. I have upgraded my version of RStudio and…
TheGoat
  • 2,587
  • 3
  • 25
  • 58
0
votes
1 answer

Forecasting with fable package, Erro: `as_tsibble()` doesn't know how to handle the numeric class yet

I have been using the fable package for time series analysis and during the forecasting I have been getting this error: Erro: as_tsibble() doesn't know how to handle the numeric class yet. My data is very big and it is a tsibble: bcUI Date …
macs2021
  • 85
  • 1
  • 7
0
votes
1 answer

Hierarchical forecast with xreg late in series using fable

I am using the great fable package and am trying to create a hierarchical forecast using arima and ets models, and reconciling with td, mo, bu, and min trace to compare and see what is the best approach. My series has some effects late in the series…
Hunter Clark
  • 181
  • 13
0
votes
1 answer

Given a tsibble with more than one key, is tidyverts able to box_cox() each time series using a respective lambda_guerrero value per time series?

My question is: if I had a tsibble with more than one key (n_keys > 1), and either one or more key variables (key_vars >= 1), is the tidyverts suite able to perform a box_cox transformation on each time series (one box_cox transformation per time…
John Paul B
  • 1
0
votes
2 answers

Forecast and fable package returning slightly different parameters when forecasting using same dataset

This can be considered as a follow-up question for this, as part of the exploration exercise on studying the difference between forecast and fable package. I used the same dataset for forecasting future value as…
lokheart
  • 23,743
  • 39
  • 98
  • 169
0
votes
1 answer

How can I implement both hourly and weekly seasonality in Fable - R Package using season() function?

I have to create hourly forecasts for 2000 different time series. And I have strong hourly and weekly seasonality in my series. To deal with hourly seasonality I used season("day") option. However, I suppose season("week") would create 168 weekly…
omzeybek
  • 305
  • 2
  • 14
0
votes
0 answers

How to add forecasts together using fable (r)?

I am trying to add my forecasts together for each region and each quarter. I've tried to summarize on a specific variable, but that didn't work. I've also tried to groupby region too, but I believe for this to work, you need to aggregate somehow.…
QMan5
  • 713
  • 1
  • 4
  • 20
0
votes
1 answer

Time series forecasting in R; plotting "events" and generating new forecasting plots with specified date range after initial forecast

I have created a function which allows me to carry out time series forecasting using the fable package. The idea of the function was to analyse observed vs predicted values after a particular date/event. Here is a mock data frame which generates a…
metaltoaster
  • 380
  • 2
  • 15
0
votes
1 answer

How do i unquote a character column name for fable::aggregate_key?

I am trying to use the aggregate_key function in the fable package to create a hierarchical time series in a shiny flexdashboard. The below code works fine as long as I can hard code in the column name…
Hunter Clark
  • 181
  • 13
0
votes
1 answer

Forecast fan does not begin at the end of the actual line using fable package

I have constructed the following fanplot using the fable package in R. I'm wondering whether anyone has advice as to why my forecast fan's point of origin is not from the actual line (the outer points at the beginning are quite far from the actual…
sa90210
  • 525
  • 2
  • 12
0
votes
1 answer

Getting null results from the accuracy function in fabletools package

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,…
Mushroom
  • 17
  • 3
0
votes
1 answer

BoxCox.lambda not returning same results as fable features

I am comparing the results of the automatic lambda selection function BoxCox.lambda from the forecast package VS the fable package automatic lambda selection features As you can see below, the two functions did not return the same results. Moreover,…
Bssm16
  • 3
  • 1
0
votes
2 answers

Comparing AICc of ARIMA models produced in fable

When using fable to produce a set of different ARIMA models using different combinations of xregs, if different models choose a different d and D parameters then the AICc are no longer comparable, right? In that case, should I just find the max d…
Tyler Smith
  • 288
  • 1
  • 7
1 2 3
…
9 10