| Title: | Variational Seq2Seq Model with Lambda Transformer for Time Series Analysis |
|---|---|
| Description: | Time series analysis based on lambda transformer and variational seq2seq, built on 'Torch'. |
| Authors: | Giancarlo Vercellino |
| Maintainer: | Giancarlo Vercellino <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1 |
| Built: | 2025-02-22 04:56:12 UTC |
| Source: | https://github.com/cran/lambdaTS |
A data frame with different time series (prices and volumes) for bitcoin, gold and oil.
bitcoin_gold_oilbitcoin_gold_oil
A data frame with 18 columns and 1827 rows.
Yahoo Finance
Time series analysis based on Lambda Transformer and Variational Seq2Seq, built on 'Torch'.
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 )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 )
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. |
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
Giancarlo Vercellino [email protected]
## Not run: lambdaTS(bitcoin_gold_oil, c("gold_close", "oil_Close"), 30, deriv = 1) ## End(Not run)## Not run: lambdaTS(bitcoin_gold_oil, c("gold_close", "oil_Close"), 30, deriv = 1) ## End(Not run)