| Type: | Package |
| Title: | Empirical Extrapolation of Time Feature Patterns |
| Version: | 1.2.3 |
| Description: | An application for the empirical extrapolation of time features selecting and summarizing the most relevant patterns in time sequences. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| Depends: | R (≥ 4.1) |
| Imports: | purrr (≥ 1.0.1), ggplot2 (≥ 3.4.2), readr (≥ 2.1.4),lubridate (≥ 1.9.2), imputeTS (≥ 3.3), fANCOVA (≥ 0.6-1),scales (≥ 1.2.1), tictoc (≥ 1.2), modeest (≥ 2.4.0), moments(≥ 0.14.1), greybox (≥ 1.0.8), Rfast (≥ 2.0.7), fastDummies(≥ 1.6.3), entropy (≥ 1.3.1), philentropy (≥ 0.7.0) |
| URL: | https://rpubs.com/giancarlo_vercellino/naive |
| NeedsCompilation: | no |
| Packaged: | 2023-06-20 14:11:14 UTC; gianc |
| Author: | Giancarlo Vercellino [aut, cre, cph] |
| Maintainer: | Giancarlo Vercellino <giancarlo.vercellino@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2023-06-20 14:30:04 UTC |
naive
Description
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 | A data frame with time features on columns (all numerics or all categories, but not both). In case of missing values, automatic missing imputation through kalman filter will be performed. |
seq_len | Positive integer. Time-step number of the forecasting sequence. Default: NULL (random selection within boundaries). |
ci | Confidence interval for prediction. Default: 0.8 |
smoother | Logical. Flag to TRUE for loess smoothing (only for numeric series). Default: FALSE. |
cover | Positive numeric. The quantile cover around the location parameter (between 0 and 1). Default: NULL (random selection within boundaries). |
stride | Positive integer. Shift between subsequent sequences. Default: NULL (random selection within boundaries). |
method | String. Distance method using during the comparison of time sequences. Possible options are: "euclidean", "manhattan", "minkowski". Default: NULL (random selection). |
location | String. Statistic used to center the cover parameter. Possible options are: "mean", "mode" (parzen method), "median". Default: NULL (random selection). |
n_windows | Positive integer. Number of validation windows to test prediction error. Default: 10. |
n_samp | Positive integer. Number of sample selected during random search. Default: 30. |
dates | Date. Vector with dates for time features. |
error_scale | String. Scale for the scaled error metrics. Two options: "naive" (average of naive one-step absolute error for the historical series) or "deviation" (standard error of the historical series). Default: "naive". |
error_benchmark | String. Benchmark for the relative error metrics. Two options: "naive" (sequential extension of last value) or "average" (mean value of true sequence). Default: "naive". |
seed | Positive integer. Random seed. Default: 42. |
Value
This function returns a list including:
exploration: collection of all the models explored with random search
history: a table with the explored models' hyper-parameters and validation errors
best_model: best combination resulting from the average prediction score across different ranks and features, including:
quant_preds: min, max, q25, q50, q75, quantiles at selected ci, mean, sd, mode, skewness, kurtosis, IQR to range, above to below median range, upside probability and divergence for each point fo predicted sequences
errors: testing errors for each time feature averaged across validation windows
plots: standard plot with confidence interval for each time feature
time_log
Author(s)
Giancarlo Vercellinogiancarlo.vercellino@gmail.com
Maintainer: Giancarlo Vercellinogiancarlo.vercellino@gmail.com [copyright holder]
See Also
Useful links:
Examples
{naive(time_features[, 2:3, drop = FALSE], seq_len = 30, n_samp = 1, n_windows = 5)}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_featuresFormat
A data frame with 6 columns and 1336 rows.
Source
finance.yahoo.com