Package 'nixtlar'

Electricity dataset

Description

Contains prices of different electricity markets.

Usage

electricity

Format

electricity

A data frame with 8400 rows and 3 columns:

unique_id

Unique identifiers of the electricity markets.

ds

Date in format YYYY:MM:DD hh:mm:ss.

y

Price for the given market and date.

Source

https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv

---

Electricity dataset with exogenous variables

Description

Contains prices of different electricity markets with exogenous variables.

Usage

electricity_exo_vars

Format

electricity_exo_vars

A data frame with 8400 rows and 12 columns:

unique_id

Unique identifiers of the electricity markets.

ds

Date in format YYYY:MM:DD hh:mm:ss.

y

Price for the given market and date.

Exogenous1

An external factor influencing prices. For all markets, some form of day-ahead load forecast.

Exogenous2

An external factor influencing prices. For "BE" and "FR" markets, the day-ahead generation forecast. For "NP", the day-ahead wind generation forecast. For "PJM", the day-ahead load forecast in a specific zone. For "DE", the aggregated day-ahead wind and solar generation forecasts.

day_0

Binary variable indicating weekday.

day_1

Binary variable indicating weekday.

day_2

Binary variable indicating weekday.

day_3

Binary variable indicating weekday.

day_4

Binary variable indicating weekday.

day_5

Binary variable indicating weekday.

day_6

Binary variable indicating weekday.

Source

https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv

---

Future values for the electricity dataset with exogenous variables

Description

Contains the future values of the exogenous variables of the electricity dataset (24 steps-ahead). To be used with electricity_exo_vars.

Usage

electricity_future_exo_vars

Format

electricity_future_exo_vars

A data frame with 120 rows and 11 columns:

unique_id

Unique identifiers of the electricity markets.

ds

Date in format YYYY:MM:DD hh:mm:ss.

Exogenous1

An external factor influencing prices. For all markets, some form of day-ahead load forecast.

Exogenous2

An external factor influencing prices. For "BE" and "FR" markets, the day-ahead generation forecast. For "NP", the day-ahead wind generation forecast. For "PJM", the day-ahead load forecast in a specific zone. For "DE", the aggregated day-ahead wind and solar generation forecasts.

day_0

Binary variable indicating weekday.

day_1

Binary variable indicating weekday.

day_2

Binary variable indicating weekday.

day_3

Binary variable indicating weekday.

day_4

Binary variable indicating weekday.

day_5

Binary variable indicating weekday.

day_6

Binary variable indicating weekday.

Source

https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short-future-ex-vars.csv

---

Infer frequency of a data frame.

Description

Infer frequency of a data frame.

Usage

infer_frequency(df, freq)

Arguments

df	A data frame with time series data.
freq	The frequency of the data as specified by the user; NULL otherwise.

Value

The inferred frequency.

Examples

df <- nixtlar::electricity
freq <- NULL
infer_frequency(df, freq)

---

Sequential version of 'nixtla_client_historic' This is a private function of 'nixtlar'

Description

Sequential version of 'nixtla_client_historic' This is a private function of 'nixtlar'

Usage

nixtla_client_historic(
  df,
  freq = NULL,
  id_col = NULL,
  time_col = "ds",
  target_col = "y",
  level = NULL,
  quantiles = NULL,
  finetune_steps = 0,
  finetune_loss = "default",
  clean_ex_first = TRUE,
  model = "timegpt-1"
)

Arguments

df	A tsibble or a data frame with time series data.
freq	Frequency of the data.
id_col	Column that identifies each series.
time_col	Column that identifies each timestep.
target_col	Column that contains the target variable.
level	The confidence levels (0-100) for the prediction intervals.
quantiles	Quantiles to forecast. Should be between 0 and 1.
finetune_steps	Number of steps used to finetune 'TimeGPT' in the new data.
finetune_loss	Loss function to use for finetuning. Options are: "default", "mae", "mse", "rmse", "mape", and "smape".
clean_ex_first	Clean exogenous signal before making the forecasts using 'TimeGPT'.
model	Model to use, either "timegpt-1" or "timegpt-1-long-horizon". Use "timegpt-1-long-horizon" if you want to forecast more than one seasonal period given the frequency of the data.

Value

'TimeGPT”s forecast for the in-sample period.

Examples

## Not run: 
  nixtlar::nixtla_set_api_key("YOUR_API_KEY")
  df <- nixtlar::electricity
  fcst <- nixtlar::nixtla_client_historic(df, id_col="unique_id", level=c(80,95))

## End(Not run)

---

Plot the output of the following nixtla_client functions: forecast, historic, anomaly_detection, and cross_validation.

Description

Plot the output of the following nixtla_client functions: forecast, historic, anomaly_detection, and cross_validation.

Usage

nixtla_client_plot(
  df,
  fcst = NULL,
  h = NULL,
  id_col = "unique_id",
  time_col = "ds",
  target_col = "y",
  unique_ids = NULL,
  max_insample_length = NULL,
  plot_anomalies = FALSE
)

Arguments

df	A tsibble or a data frame with time series data (insample values).
fcst	A tsibble or a data frame with the 'TimeGPT' point forecast and the prediction intervals (if available).
h	Forecast horizon.
id_col	Column that identifies each series.
time_col	Column that identifies each timestep.
target_col	Column that contains the target variable.
unique_ids	Time series to plot. If NULL (default), selection will be random.
max_insample_length	Max number of insample observations to be plotted.
plot_anomalies	Whether or not to plot anomalies.

Value

Plot with historical data and 'TimeGPT”s output (if available).

Examples

## Not run: 
  nixtlar::nixtla_set_api_key("YOUR_API_KEY")
  df <- nixtlar::electricity
  fcst <- nixtlar::nixtla_client_forecast(df, h=8, id_col="unique_id", level=c(80,95))
  nixtlar::timegpt_plot(df, fcst, h=8, id_col="unique_id")

## End(Not run)

---

Set base 'ULR' and 'API' key in global environment

Description

Set base 'ULR' and 'API' key in global environment

Usage

nixtla_client_setup(base_url = NULL, api_key = NULL)

Arguments

base_url	Custom base 'URL'. If NULL, defaults to "https://api.nixtla.io/".
api_key	The user's 'API' key. Get yours here: https://dashboard.nixtla.io/

Value

A message indicating the configuration status.

Examples

## Not run: 
  nixtlar::nixtla_client_setup(
    base_url = "Base URL",
    api_key = "Your API key"
  )

## End(Not run)

---

Set 'API' key in global environment

Description

This function will be deprecated in future versions. Please use nixtla_client_setup instead.

Usage

nixtla_set_api_key(api_key)

Arguments

api_key

The user's 'API' key. Get yours here: https://dashboard.nixtla.io/

Value

A message indicating the 'API' key has been set in the global environment.

Examples

## Not run: 
  nixtlar::nixtla_set_api_key("Your API key")

## End(Not run)

---

Validate 'API' key

Description

Validate 'API' key

Usage

nixtla_validate_api_key()

Value

TRUE if the API key is valid, FALSE otherwise.

Examples

## Not run: 
  nixtlar::nixtla_client_setup(api_key = "Your API key")
  nixtlar::nixtla_validate_api_key()

## End(Not run)

---