| Type: | Package |
| Title: | Automatic Sequence Prediction by Expansion of the Distance Matrix |
| Version: | 1.3.0 |
| Author: | Giancarlo Vercellino |
| Maintainer: | Giancarlo Vercellino <giancarlo.vercellino@gmail.com> |
| Description: | Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned through random search. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Depends: | R (≥ 4.1) |
| Imports: | purrr (≥ 0.3.4), abind (≥ 1.4-5), ggplot2 (≥ 3.3.5), readr (≥ 2.0.1), stringr (≥ 1.4.0), lubridate (≥ 1.7.10), narray (≥ 0.4.1.1), imputeTS (≥ 3.2), scales (≥ 1.1.1), tictoc (≥ 1.0.1), modeest (≥ 2.4.0), moments (≥ 0.14), greybox (≥ 1.0.1), dqrng (≥ 0.3.0), entropy (≥ 1.3.1), Rfast (≥ 2.0.6), philentropy (≥ 0.5.0), fastDummies (≥ 1.6.3), fANCOVA (≥ 0.6-1) |
| URL: | https://rpubs.com/giancarlo_vercellino/tetragon |
| NeedsCompilation: | no |
| Packaged: | 2022-08-13 16:47:17 UTC; gvercellino |
| Repository: | CRAN |
| Date/Publication: | 2022-08-13 17:30:02 UTC |
tetragon
Description
Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned via grid or random search.
Usage
tetragon(
df,
seq_len = NULL,
smoother = F,
ci = 0.8,
method = NULL,
distr = NULL,
n_windows = 3,
n_sample = 30,
dates = NULL,
error_scale = "naive",
error_benchmark = "naive",
seed = 42
)
Arguments
df |
A data frame with time features as columns. They could be continuous variables or not. |
seq_len |
Positive integer. Time-step number of the projected sequence. Default: NULL (random selection between maximum boundaries). |
smoother |
Logical. Perform optimal smoothing using standard loess. Default: FALSE |
ci |
Confidence interval. Default: 0.8. |
method |
String. Distance method for calculating distance matrix among sequences. Options are: "euclidean", "manhattan", "maximum", "minkowski". Default: NULL (random selection among all possible options). |
distr |
String. Distribution used to expand the distance matrix. Options are: "norm", "logis", "t", "exp", "chisq". Default: NULL (random selection among all possible options). |
n_windows |
Positive integer. Number of validation tests to measure/sample error. Default: 3 (but a larger value is strongly suggested to really understand your accuracy). |
n_sample |
Positive integer. Number of samples for random search. Default: 30. |
dates |
Date. Vector with dates for time features. |
error_scale |
String. Scale for the scaled error metrics (only for continuous variables). 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 (only for continuous variables). 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: list of all explored models, complete with predictions, testing metrics and plots
history: a table with the sampled models, hyper-parameters, validation errors
best: results for the best model including:
predictions: min, max, q25, q50, q75, quantiles at selected ci, and a bunch of specific measures for each point fo predicted sequences
testing_errors: testing errors for one-step and sequence for each ts feature
plots: confidence interval plot for each time feature
time_log
Author(s)
Giancarlo Vercellino giancarlo.vercellino@gmail.com
See Also
Useful links:
Examples
tetragon(covid_in_europe[, c(2, 4)], seq_len = 40, n_sample = 2)
covid_in_europe data set
Description
A data frame with with daily and cumulative cases of Covid infections and deaths in Europe since March 2021.
Usage
covid_in_europe
Format
A data frame with 5 columns and 163 rows.
Source
www.ecdc.europa.eu