| Type: | Package |
| Title: | Variational Seq2Seq Model with Lambda Transformer for Time Series Analysis |
| Version: | 1.1 |
| Author: | Giancarlo Vercellino |
| Maintainer: | Giancarlo Vercellino <giancarlo.vercellino@gmail.com> |
| Description: | Time series analysis based on lambda transformer and variational seq2seq, built on 'Torch'. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Depends: | R (≥ 3.6) |
| Imports: | car, purrr, abind, ggplot2, readr, stringr, lubridate, narray, fANCOVA, imputeTS, modeest, scales, tictoc, bizdays, torch |
| NeedsCompilation: | no |
| Packaged: | 2022-02-20 19:33:50 UTC; gvercellino |
| Repository: | CRAN |
| Date/Publication: | 2022-02-20 20:00:02 UTC |
lambdaTS: Variational Seq2Seq Lambda Transformer Model for Time Series Analysis
Description
Time series analysis based on Lambda Transformer and Variational Seq2Seq, built on 'Torch'.
Usage
lambdaTS(
data,
target,
future,
past = future,
ci = 0.8,
deriv = 1,
yjt = TRUE,
shift = 0,
smoother = FALSE,
k_embed = 30,
r_proj = ceiling(k_embed/3) + 1,
n_heads = 1,
n_bases = 1,
activ = "linear",
loss_metric = "elbo",
optim = "adam",
epochs = 30,
lr = 0.01,
patience = epochs,
verbose = TRUE,
sample_n = 100,
seed = 42,
dev = "cpu",
starting_date = NULL,
dbreak = NULL,
days_off = NULL,
min_set = future,
holdout = 0.5,
batch_size = 30
)
Arguments
data |
A data frame with ts on columns and possibly a date column (not mandatory) |
target |
String. Time series names to be jointly analyzed within the seq2seq model |
future |
Positive integer. The future dimension with number of time-steps to be predicted |
past |
Positive integer. The past dimension with number of time-steps in the past used for the prediction. Default: future |
ci |
Confidence interval. Default: 0.8 |
deriv |
Positive integer. Number of differentiation operations to perform on the original series. 0 = no change; 1: one diff; 2: two diff, and so on. |
yjt |
Logical. Performing Yeo-Johnson Transformation on data is always advisable, especially when dealing with different ts at different scales. Default: TRUE |
shift |
Vector of positive integers. Allow for target variables to shift ahead of time. Zero means no shift. Length must be equal to the number of targets. Default: 0. |
smoother |
Logical. Perform optimal smooting using standard loess. Default: FALSE |
k_embed |
Positive integer. Number of Time2Vec embedding dimensions. Minimum value is 2. Default: 30 |
r_proj |
Positive integer. Number of dimensions for the reduction space (to reduce quadratic complexity). Must be largely less than k_embed size. Default: ceiling(k_embed/3) + 1 |
n_heads |
Positive integer. Number of heads for the attention mechanism. Computationally expensive, use with care. Default: 1 |
n_bases |
Positive integer. Number of normal curves to build on each parameter. Computationally expensive, use with care. Default: 1 |
activ |
String. The activation function for the linear transformation of the attention matrix into the future sequence. Implemented options are: "linear", "leaky_relu", "celu", "elu", "gelu", "selu", "softplus", "bent", "snake", "softmax", "softmin", "softsign", "sigmoid", "tanh", "tanhshrink", "swish", "hardtanh", "mish". Default: "linear". |
loss_metric |
String. Loss function for the variational model. Two options: "elbo" or "crps". Default: "crps". |
optim |
String. Optimization methods available are: "adadelta", "adagrad", "rmsprop", "rprop", "sgd", "asgd", "adam". Default: "adam". |
epochs |
Positive integer. Default: 30. |
lr |
Positive numeric. Learning rate. Default: 0.01. |
patience |
Positive integer. Waiting time (in epochs) before evaluating the overfit performance. Default: epochs. |
verbose |
Logical. Default: TRUE |
sample_n |
Positive integer. Number of samples from the variational model to evalute the mean forecast values. Computationally expensive, use with care. Default: 100. |
seed |
Random seed. Default: 42. |
dev |
String. Torch implementation of computational platform: "cpu" or "cuda" (gpu). Default: "cpu". |
starting_date |
Date. Initial date to assign temporal values to the series. Default: NULL (progressive numbers). |
dbreak |
String. Minimum time marker for x-axis, in liberal form: i.e., "3 months", "1 week", "20 days". Default: NULL. |
days_off |
String. Weekdays to exclude (i.e., c("saturday", "sunday")). Default: NULL. |
min_set |
Positive integer. Minimun number for validation set in case of automatic resize of past dimension. Default: future. |
holdout |
Positive numeric. Percentage of time series for holdout validation. Default: 0.5. |
batch_size |
Positive integer. Default: 30. |
Value
This function returns a list including:
prediction: a table with quantile predictions, mean and std for each ts
history: plot of loss during the training process for the joint-transformed ts
plot: graph with history and prediction for each ts
learning_error: errors for the joint-ts in the transformed scale (rmse, mae, mdae, mpe, mape, smape, rrse, rae)
feature_errors: errors for each ts in the original scale (rmse, mae, mdae, mpe, mape, smape, rrse, rae)
pred_stats: for each predicted time feature, IQR to range, KL-divergence, risk ratio, upside probability, averaged across time-points and compared at the terminal points.
time_log
Author(s)
Giancarlo Vercellino giancarlo.vercellino@gmail.com
Examples
## Not run:
lambdaTS(bitcoin_gold_oil, c("gold_close", "oil_Close"), 30, deriv = 1)
## End(Not run)
bitcoin_gold_oil data set
Description
A data frame with different time series (prices and volumes) for bitcoin, gold and oil.
Usage
bitcoin_gold_oil
Format
A data frame with 18 columns and 1827 rows.
Source
Yahoo Finance