Package 'naive'

Compatibility wrapper for empirical extrapolation

Description

Calls 'naive_fit()' using the v1-compatible argument list.

Empirical Extrapolation of Time Feature Pattern

Usage

naive(
  df,
  seq_len = NULL,
  ci = 0.8,
  smoother = FALSE,
  cover = NULL,
  stride = NULL,
  method = NULL,
  location = NULL,
  n_windows = 10,
  n_samp = 30,
  dates = NULL,
  error_scale = "naive",
  error_benchmark = "naive",
  seed = 42
)

Arguments

df	Numeric or categorical data frame.
seq_len	Forecast horizon.
ci	Prediction interval coverage.
smoother	Compatibility argument.
cover	Similarity-window coverage.
stride	Window stride.
method	Distance method.
location	Location statistic.
n_windows	Number of validation windows.
n_samp	Number of candidate configurations.
dates	Optional dates.
error_scale	Compatibility metric scale.
error_benchmark	Compatibility metric benchmark.
seed	Random seed.

Author(s)

Maintainer: Giancarlo Vercellino [email protected]

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Empirical extrapolation of time-feature patterns

Description

Empirical extrapolation of time-feature patterns

Usage

naive_fit(
  df,
  seq_len = NULL,
  ci = 0.8,
  n_windows = 10,
  n_samp = 30,
  seed = 42,
  ...
)

Arguments

df	Numeric or categorical data frame.
seq_len	Forecast horizon.
ci	Prediction interval coverage.
n_windows	Number of validation windows.
n_samp	Number of candidate configurations.
seed	Random seed.
...	Compatibility arguments: 'cover', 'stride', 'method', 'location', 'dates', 'error_scale', 'error_benchmark', and 'smoother'.

Value

An object of class 'naive_forecast'.

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Generate an empirical forecast

Description

Generate an empirical forecast

Usage

naive_forecast(
  df,
  horizon,
  ci = 0.8,
  cover = 0.5,
  stride = 1,
  method = "euclidean",
  location = "median",
  dates = NULL,
  seed = 42
)

Arguments

df	Numeric or categorical data frame.
horizon	Forecast horizon.
ci	Prediction interval coverage.
cover	Similarity-window coverage.
stride	Window stride.
method	Distance method.
location	Location statistic.
dates	Optional dates.
seed	Random seed.

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Calculate basic forecast errors

Description

Calculate basic forecast errors

Usage

naive_metrics(actual, predicted)

Arguments

actual	Actual values.
predicted	Predicted values.

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time features example: IBM, AAPL, AMZN, GOOGL and MSFT Close Prices

Description

A data frame with with daily with daily prices for some Big Tech Companies since March 2017.

Usage

time_features

Format

A data frame with 6 columns and 1336 rows.

Source

finance.yahoo.com

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