Package 'autoTS' reference manual

Title:	Automatic Model Selection and Prediction for Univariate Time Series
Description:	Offers a set of functions to easily make predictions for univariate time series. 'autoTS' is a wrapper of existing functions of the 'forecast' and 'prophet' packages, harmonising their outputs in tidy dataframes and using default values for each. The core function getBestModel() allows the user to effortlessly benchmark seven algorithms along with a bagged estimator to identify which one performs the best for a given time series.
Authors:	Vivien Roussez
Maintainer:	Vivien Roussez <[email protected]>
License:	GPL-3
Version:	0.9.11
Built:	2024-10-11 04:31:00 UTC
Source:	https://github.com/vivienroussez/autots

Creates additional dates and values when NA where removed and the TS is not complete

Description

Creates additional dates and values when NA where removed and the TS is not complete

Usage

complete.ts(dates, values, freq, complete = 0)
complete.ts(dates, values, freq, complete = 0)

Arguments

`dates`	A vector of dates that can be parsed by lubridate
`values`	A vector of same size as `dates`
`freq`	A chacracter string that indicates the frequency of the time series ("week", "month", "quarter", "day").
`complete`	A numerical value (or NA) to fill the missing data points

Value

A dataframe with 2 columns : date and val, with additional rows

Examples

library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"month")
values <- rnorm(length(dates))
complete.ts(dates,values,"month",complete = 0)
library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"month")
values <- rnorm(length(dates))
complete.ts(dates,values,"month",complete = 0)

Determine best algorithm

Description

Implement selected algorithms, train them without the last n observed data points (or n_test number of points), and compares the results to reality to determine the best algorithm

Usage

getBestModel(
  dates,
  values,
  freq,
  complete = 0,
  n_test = NA,
  graph = TRUE,
  algos = list("my.prophet", "my.ets", "my.sarima", "my.tbats", "my.bats", "my.stlm",
    "my.shortterm"),
  bagged = "auto",
  metric.error = my.rmse
)
getBestModel(
  dates,
  values,
  freq,
  complete = 0,
  n_test = NA,
  graph = TRUE,
  algos = list("my.prophet", "my.ets", "my.sarima", "my.tbats", "my.bats", "my.stlm",
    "my.shortterm"),
  bagged = "auto",
  metric.error = my.rmse
)

Arguments

`dates`	A vector of dates that can be parsed by lubridate
`values`	A vector of same size as `dates`
`freq`	A chacracter string that indicates the frequency of the time series ("week", "month", "quarter", "day").
`complete`	A numerical value (or NA) to fill the missing data points
`n_test`	number of data points to keep aside for the test (default : one year)
`graph`	A boolean, if TRUE, comparison of algorithms is plotted
`algos`	A list containing the algorithms (strings, with prefix "my.") to be tested
`bagged`	A string. "auto" will use all available algoriths, skipping algos parameter. Else, specified algos of the 'algo' parameter will be used
`metric.error`	a function to compute the error the each models. available functions : my.rmse and my.mae

Value

A list contraining a character string with the name of the best method, a gg object with the comparison between algorithms and a dataframe with predictions of all tried algorithms, a dtaframe containing the errors of each algorithms, the preparedTS object and the list of algorithms tested

Examples

library(autoTS)
dates <- seq(lubridate::as_date("2005-01-01"),lubridate::as_date("2010-12-31"),"quarter")
values <- 10+ 1:length(dates)/10 + rnorm(length(dates),mean = 0,sd = 10)

which.model <- getBestModel(dates,values,freq = "quarter",n_test = 4)

### Custom set of algorithm (including for bagged estimator)
which.model <- getBestModel(dates,values,freq = "quarter",n_test = 4,
                            algos = list("my.prophet","my.ets"),bagged = "custom")
### Use MAE instead of RMSE

which.model <- getBestModel(dates,values,freq = "quarter",n_test = 3,
                            algos = list("my.prophet","my.ets"),
                            bagged = "custom",metric.error = my.mae)

library(autoTS)
dates <- seq(lubridate::as_date("2005-01-01"),lubridate::as_date("2010-12-31"),"quarter")
values <- 10+ 1:length(dates)/10 + rnorm(length(dates),mean = 0,sd = 10)

which.model <- getBestModel(dates,values,freq = "quarter",n_test = 4)

### Custom set of algorithm (including for bagged estimator)
which.model <- getBestModel(dates,values,freq = "quarter",n_test = 4,
                            algos = list("my.prophet","my.ets"),bagged = "custom")
### Use MAE instead of RMSE

which.model <- getBestModel(dates,values,freq = "quarter",n_test = 3,
                            algos = list("my.prophet","my.ets"),
                            bagged = "custom",metric.error = my.mae)

Determines the decimal frequency of a time series from a character string

Description

Determines the decimal frequency of a time series from a character string

Usage

getFrequency(freq.alpha)
getFrequency(freq.alpha)

Arguments

freq.alpha

A character string that indicates the frequency of the time series ("week", "month", "quarter", "day").

Value

The decimal version of the frequency (useful for the forecast package functions).

Examples

getFrequency("week")
getFrequency("week")

Fit BATS algorithm and make the prediction

Description

Fit BATS algorithm and make the prediction

Usage

my.bats(prepedTS, n_pred)
my.bats(prepedTS, n_pred)

Arguments

`prepedTS`	A list created by the `prepare.ts()` function
`n_pred`	Int number of periods to forecast forward (eg n_pred = 12 will lead to one year of prediction for monthly time series)

Value

A dataframe with 4 columns : date, average prediction, upper and lower 95

Examples

library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.bats(my.ts,n_pred=4)

library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.bats(my.ts,n_pred=4)

Fit ETS algorithm and make the prediction

Description

Fit ETS algorithm and make the prediction

Usage

my.ets(prepedTS, n_pred)
my.ets(prepedTS, n_pred)

Arguments

`prepedTS`	A list created by the `prepare.ts()` function
`n_pred`	Int number of periods to forecast forward (eg n_pred = 12 will lead to one year of prediction for monthly time series)

Value

A dataframe with 4 columns : date, average prediction, upper and lower 95

Examples

 library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.ets(my.ts,n_pred=4)

library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.ets(my.ts,n_pred=4)

Custom (internal) function for MAE

Description

Custom (internal) function for MAE

Usage

my.mae(true, predicted)
my.mae(true, predicted)

Arguments

`true`	num vector of actual values
`predicted`	num vector of predicted values

Value

Num value with MAE

Make predictions with selected algorithms

Description

Fit selected algorithms, make the predictions and combine the results along with observed data in one final dataframe.

Usage

my.predictions(
  bestmod = NULL,
  prepedTS = NULL,
  algos = list("my.prophet", "my.ets", "my.sarima", "my.tbats", "my.bats", "my.stlm",
    "my.shortterm"),
  n_pred = NA
)
my.predictions(
  bestmod = NULL,
  prepedTS = NULL,
  algos = list("my.prophet", "my.ets", "my.sarima", "my.tbats", "my.bats", "my.stlm",
    "my.shortterm"),
  n_pred = NA
)

Arguments

`bestmod`	A list produced by the `getBestModel()` function (optional if `prepredTS` is provided)
`prepedTS`	A list created by the `prepare.ts()` function (optional if `bestmod` provided)
`algos`	A list containing the algorithms to be implemented. If `bestmod` is supplied, this value is ignored, and taken from the best model object Using this option will overwrite the provided list of algorithms to implement them all
`n_pred`	Int number of periods to forecast forward (eg n_pred = 12 will lead to one year of prediction for monthly time series)

Value

A dataframe containing : date, actual observed values, one column per used algorithm, and a column indicating the type of measure (mean prediction, upper or lower bound of CI)

Examples

library(lubridate)
library(dplyr)
dates <- seq(lubridate::as_date("2000-01-01"),lubridate::as_date("2010-12-31"),"quarter")
values <- 10+ 1:length(dates)/10 + rnorm(length(dates),mean = 0,sd = 10)
### Stand alone usage
prepare.ts(dates,values,"quarter") %>%
  my.predictions(prepedTS = .,algos = list("my.prophet","my.ets"))
### Standard input with bestmodel

getBestModel(dates,values,freq = "quarter",n_test = 6) %>%
  my.predictions()


library(lubridate)
library(dplyr)
dates <- seq(lubridate::as_date("2000-01-01"),lubridate::as_date("2010-12-31"),"quarter")
values <- 10+ 1:length(dates)/10 + rnorm(length(dates),mean = 0,sd = 10)
### Stand alone usage
prepare.ts(dates,values,"quarter") %>%
  my.predictions(prepedTS = .,algos = list("my.prophet","my.ets"))
### Standard input with bestmodel

getBestModel(dates,values,freq = "quarter",n_test = 6) %>%
  my.predictions()

Fit prophet algorithm and make the prediction

Description

Fit prophet algorithm and make the prediction

Usage

my.prophet(prepedTS, n_pred)
my.prophet(prepedTS, n_pred)

Arguments

`prepedTS`	A list created by the `prepare.ts()` function
`n_pred`	Int number of periods to forecast forward (eg n_pred = 12 will lead to one year of prediction for monthly time series)

Value

A dataframe for "next year" with 4 columns : date, average prediction, upper and lower 95

Examples

library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.prophet(my.ts,n_pred=4)
library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.prophet(my.ts,n_pred=4)

Custom (internal) function for RMSE

Description

Custom (internal) function for RMSE

Usage

my.rmse(true, predicted)
my.rmse(true, predicted)

Arguments

`true`	num vector of actual values
`predicted`	num vector of predicted values

Value

Num value with RMSE

Fit SARIMA algorithm and make the prediction

Description

Fit SARIMA algorithm and make the prediction

Usage

my.sarima(prepedTS, n_pred)
my.sarima(prepedTS, n_pred)

Arguments

`prepedTS`	A list created by the `prepare.ts()` function
`n_pred`	Int number of periods to forecast forward (eg n_pred = 12 will lead to one year of prediction for monthly time series)

Value

A dataframe with 4 columns : date, average prediction, upper and lower 95

Examples

 library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.sarima(my.ts,n_pred=4)


library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.sarima(my.ts,n_pred=4)

Fit short term algorithm and make the prediction

Description

Fit short term algorithm and make the prediction

Usage

my.shortterm(prepedTS, n_pred, smooth_window = 2)
my.shortterm(prepedTS, n_pred, smooth_window = 2)

Arguments

`prepedTS`	A list created by the `prepare.ts()` function
`n_pred`	Int number of periods to forecast forward (eg n_pred = 12 will lead to one year of prediction for monthly time series). Note that this algorithm cannot predict further than one year
`smooth_window`	Int specifying the number of periods to consider for computing the evolution rate that will be applied for the forecast

Details

this algorithm uses data of the last year and makes the prediction taking into account the seasonality and the evolution of the previous periods' evolution

Value

A dataframe with 4 columns : date, average prediction, upper and lower 95

Examples

library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.shortterm(my.ts,n_pred=4)
library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.shortterm(my.ts,n_pred=4)

Fit STLM algorithm and make the prediction

Description

Fit STLM algorithm and make the prediction

Usage

my.stlm(prepedTS, n_pred)
my.stlm(prepedTS, n_pred)

Arguments

`prepedTS`	A list created by the `prepare.ts()` function
`n_pred`	Int number of periods to forecast forward (eg n_pred = 12 will lead to one year of prediction for monthly time series)

Value

A dataframe with 4 columns : date, average prediction, upper and lower 95

Examples

library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.stlm(my.ts,n_pred=4)

library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.stlm(my.ts,n_pred=4)

Fit TBATS algorithm and make the prediction

Description

Fit TBATS algorithm and make the prediction

Usage

my.tbats(prepedTS, n_pred)
my.tbats(prepedTS, n_pred)

Arguments

`prepedTS`	A list created by the `prepare.ts()` function
`n_pred`	Int number of periods to forecast forward (eg n_pred = 12 will lead to one year of prediction for monthly time series)

Value

A dataframe with 4 columns : date, average prediction, upper and lower 95

Examples

library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.tbats(my.ts,n_pred=4)

library(lubridate)
library(dplyr)
dates <- seq(as_date("2000-01-01"),as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"quarter",complete = 0)
my.tbats(my.ts,n_pred=4)

Format 2 vectors in a proper object usable by all algorithms

Description

Format 2 vectors in a proper object usable by all algorithms

Usage

prepare.ts(dates, values, freq, complete = 0)
prepare.ts(dates, values, freq, complete = 0)

Arguments

`dates`	A vector of dates that can be parsed by lubridate
`values`	A vector of same size as `dates`
`freq`	A chacracter string that indicates the frequency of the time series ("week", "month", "quarter", "day").
`complete`	A numerical value (or NA) to fill the missing data points

Details

Creates a list with the time series in a dataframe and a ts object, and the frequency stored in decimal and litteral values. The result is meant to be put in the prophet or forecast functions

Value

A list containing : a dataframe, a ts vector for the time series, and 2 scalars for its frequency

Examples

library(lubridate)
library(dplyr)
library(ggplot2)
dates <- seq(lubridate::as_date("2000-01-01"),lubridate::as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"month",complete = 0)
plot(my.ts$obj.ts)
ggplot(my.ts$obj.df,aes(dates,val)) + geom_line()
library(lubridate)
library(dplyr)
library(ggplot2)
dates <- seq(lubridate::as_date("2000-01-01"),lubridate::as_date("2010-12-31"),"quarter")
values <- rnorm(length(dates))
my.ts <- prepare.ts(dates,values,"month",complete = 0)
plot(my.ts$obj.ts)
ggplot(my.ts$obj.df,aes(dates,val)) + geom_line()

Demo graphical user interface

Description

A shiny application that allows the user to load a properly formated CSV file, benchmark the algorithms, make a prediction and download the results. Requires additional packages shiny, shinycssloaders, tidyr and plotly to be installed

Usage

runUserInterface()
runUserInterface()

Examples

autoTS::runUserInterface()

autoTS::runUserInterface()

Package 'autoTS'

Help Index

Creates additional dates and values when NA where removed and the TS is not complete

Description

Usage

Arguments

Value

Examples

Determine best algorithm

Description

Usage

Arguments

Value

Examples

Determines the decimal frequency of a time series from a character string

Description

Usage

Arguments

Value

Examples

Fit BATS algorithm and make the prediction

Description

Usage

Arguments

Value

Examples

Fit ETS algorithm and make the prediction

Description

Usage

Arguments

Value

Examples

Custom (internal) function for MAE

Description

Usage

Arguments

Value

Make predictions with selected algorithms

Description

Usage

Arguments

Value

Examples

Fit prophet algorithm and make the prediction

Description

Usage

Arguments

Value

Examples

Custom (internal) function for RMSE

Description

Usage

Arguments

Value

Fit SARIMA algorithm and make the prediction

Description

Usage

Arguments

Value

Examples

Fit short term algorithm and make the prediction

Description

Usage

Arguments

Details

Value

Examples

Fit STLM algorithm and make the prediction

Description

Usage

Arguments

Value

Examples

Fit TBATS algorithm and make the prediction

Description

Usage

Arguments

Value

Examples

Format 2 vectors in a proper object usable by all algorithms