General Interface for GP Forecaster Time Series Models
Source:R/parsnip-gp_forecaster.R
gp_forecaster.Rd
gp_forecaster()
is a way to generate a specification of a Gaussian Process (GP) Forecaster model
before fitting and allows the model to be created using
different packages. Currently the only package is gluonts
.
Usage
gp_forecaster(
mode = "regression",
id,
freq,
prediction_length,
lookback_length = NULL,
epochs = NULL,
batch_size = NULL,
num_batches_per_epoch = NULL,
learn_rate = NULL,
learn_rate_decay_factor = NULL,
learn_rate_min = NULL,
patience = NULL,
clip_gradient = NULL,
penalty = NULL,
scale = NULL
)
Arguments
- mode
A single character string for the type of model. The only possible value for this model is "regression".
- id
A quoted column name that tracks the GluonTS FieldName "item_id"
- freq
A
pandas
timeseries frequency such as "5min" for 5-minutes or "D" for daily. Refer to Pandas Offset Aliases.- prediction_length
Numeric value indicating the length of the prediction horizon
- lookback_length
Number of steps to unroll the RNN for before computing predictions (default: NULL, in which case context_length = prediction_length)
- epochs
Number of epochs that the network will train (default: 5).
- batch_size
Number of examples in each batch (default: 32).
- num_batches_per_epoch
Number of batches at each epoch (default: 50).
- learn_rate
Initial learning rate (default: 10-3).
- learn_rate_decay_factor
Factor (between 0 and 1) by which to decrease the learning rate (default: 0.5).
- learn_rate_min
Lower bound for the learning rate (default: 5x10-5 ).
- patience
The patience to observe before reducing the learning rate, nonnegative integer (default: 10).
- clip_gradient
Maximum value of gradient. The gradient is clipped if it is too large (default: 10).
- penalty
The weight decay (or L2 regularization) coefficient. Modifies objective by adding a penalty for having large weights (default 10-8 ).
- scale
Scales numeric data by
id
group using mean = 0, standard deviation = 1 transformation. (default: FALSE)
Details
These arguments are converted to their specific names at the time that
the model is fit. Other options and arguments can be set using
set_engine()
. If left to their defaults here (see above),
the values are taken from the underlying model functions.
If parameters need to be modified, update()
can be used in lieu of recreating
the object from scratch.
The model can be created using the fit() function using the following engines:
GluonTS GP Forecaster: "gluonts_gp_forecaster" (the default)
Engine Details
The standardized parameter names in modeltime
can be mapped to their original
names in each engine:
modeltime | GaussianProcessEstimator |
id | NA |
freq | freq |
prediction_length | prediction_length |
lookback_length | context_length (= prediction_length) |
epochs | epochs (5) |
batch_size | batch_size (32) |
num_batches_per_epoch | num_batches_per_epoch (50) |
learn_rate | learning_rate (0.001) |
learn_rate_decay_factor | learning_rate_decay_factor (0.5) |
learn_rate_min | minimum_learning_rate (5e-5) |
patience | patience (10) |
clip_gradient | clip_gradient (10) |
penalty | weight_decay (1e-8) |
scale | scale_by_id (FALSE) |
Other options can be set using set_engine()
.
Engine
gluonts_gp_forecaster
The engine uses gluonts.model.gp_forecaster.GP ForecasterEstimator()
.
Default values that have been changed to prevent long-running computations:
epochs = 5
: GluonTS uses 100 by default.cardinality = 1
: GluonTS requires user to provide. You can change this viaset_engine()
Required Parameters
The gluonts
implementation has several Required Parameters,
which are user-defined.
1. ID Variable (Required):
An important difference between other parsnip models is that each time series (even single time series) must be uniquely identified by an ID variable.
The ID feature must be of class
character
orfactor
.This ID feature is provided as a quoted expression during the model specification process (e.g.
gp_forecaster(id = "ID")
assuming you have a column in your data named "ID").
2. Frequency (Required):
The GluonTS models use a Pandas Timestamp Frequency freq
to generate
features internally. Examples:
freq = "5min"
for timestamps that are 5-minutes apartfreq = "D"
for Daily Timestamps
The Pandas Timestamps are quite flexible. Refer to Pandas Offset Aliases.
3. Prediction Length (Required):
Unlike other parsnip models, a prediction_length
is required
during the model specification and fitting process.
Fit Details
The following features are REQUIRED to be available in the incoming data for the fitting process.
Fit:
fit(y ~ date + id, data)
: Includes a target feature that is a function of a "date" and "id" feature. The ID feature must be pre-specified in the model_specification.Predict:
predict(model, new_data)
wherenew_data
contains both a column named "date" and "id".
ID Variable
An ID feature must be included in the recipe or formula fitting
process. This assists with cataloging the time series inside GluonTS
ListDataset.
The column name must match the quoted feature name specified in the
gp_forecaster(id = "id")
expects a column inside your data named "id".
Date and Date-Time Variable
It's a requirement to have a date or date-time variable as a predictor.
The fit()
interface accepts date and date-time features and handles them internally.
Examples
# \donttest{
library(tidymodels)
library(tidyverse)
library(timetk)
# ---- MODEL SPEC ----
# - Important: Make sure *required* parameters are provided
model_spec <- gp_forecaster(
# User Defined (Required) Parameters
id = "id",
freq = "M",
prediction_length = 24,
# Hyper Parameters
epochs = 1,
num_batches_per_epoch = 4
) %>%
set_engine("gluonts_gp_forecaster")
model_spec
#> GP Forecaster Model Specification (regression)
#>
#> Main Arguments:
#> id = id
#> freq = M
#> prediction_length = 24
#> epochs = 1
#> num_batches_per_epoch = 4
#>
#> Computational engine: gluonts_gp_forecaster
#>
# ---- TRAINING ----
# Important: Make sure the date and id features are included as regressors
# and do NOT dummy the id feature.
model_fitted <- model_spec %>%
fit(value ~ date + id, m750)
#> Error in pkg.env$gluonts$mx.kernels$`_rbf_kernel`$RBFKernelOutput(): attempt to apply non-function
model_fitted
#> Error in eval(expr, envir, enclos): object 'model_fitted' not found
# ---- PREDICT ----
# - IMPORTANT: New Data must have id and date features
new_data <- tibble(
id = factor("M750"),
date = as.Date("2015-07-01")
)
predict(model_fitted, new_data)
#> Error in eval(expr, envir, enclos): object 'model_fitted' not found
# }