| Title: | Multiform Seq2Seq Model for Time-Feature Analysis |
|---|---|
| Description: | Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable. |
| Authors: | Giancarlo Vercellino |
| Maintainer: | Giancarlo Vercellino <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.4 |
| Built: | 2024-10-25 06:03:44 UTC |
| Source: | https://github.com/cran/proteus |
A data frame with the close prices for Amazon, Google and Facebook.
amzn_aapl_fbamzn_aapl_fb
A data frame with 4 columns and 1798 rows.
Yahoo Finance
Proteus is a Sequence-to-Sequence Variational Model designed for time-feature analysis, leveraging a wide range of distributions for improved accuracy. Unlike traditional methods that rely solely on the normal distribution, Proteus uses various latent models to better capture and predict complex processes. To achieve this, Proteus employs a neural network architecture that estimates the shape, location, and scale parameters of the chosen distribution. This approach transforms past sequence data into future sequence parameters, improving the model's prediction capabilities. Proteus also assesses the accuracy of its predictions by estimating the error of measurement and calculating the confidence interval. By utilizing a range of distributions and advanced modeling techniques, Proteus provides a more accurate and comprehensive approach to time-feature analysis.
proteus( data, target, future, past, ci = 0.8, smoother = FALSE, t_embed = 30, activ = "linear", nodes = 32, distr = "normal", optim = "adam", loss_metric = "crps", epochs = 30, lr = 0.01, patience = 10, latent_sample = 100, verbose = TRUE, stride = 1, dates = NULL, rolling_blocks = FALSE, n_blocks = 4, block_minset = 30, error_scale = "naive", error_benchmark = "naive", batch_size = 30, omit = FALSE, min_default = 1, future_plan = "future::multisession", seed = 42 )proteus( data, target, future, past, ci = 0.8, smoother = FALSE, t_embed = 30, activ = "linear", nodes = 32, distr = "normal", optim = "adam", loss_metric = "crps", epochs = 30, lr = 0.01, patience = 10, latent_sample = 100, verbose = TRUE, stride = 1, dates = NULL, rolling_blocks = FALSE, n_blocks = 4, block_minset = 30, error_scale = "naive", error_benchmark = "naive", batch_size = 30, omit = FALSE, min_default = 1, future_plan = "future::multisession", seed = 42 )
data |
A data frame with time features on columns and possibly a date column (not mandatory) |
target |
Vector of strings. Names of the time features to be jointly analyzed |
future |
Positive integer. The future dimension with number of time-steps to be predicted |
past |
Positive integer. Length of past sequences |
ci |
Positive numeric. Confidence interval. Default: 0.8 |
smoother |
Logical. Perform optimal smoothing using standard loess for each time feature. Default: FALSE |
t_embed |
Positive integer. Number of embedding for the temporal dimension. Minimum value is equal to 2. Default: 30. |
activ |
String. Activation function to be used by the forward network. Implemented functions are: "linear", "mish", "swish", "leaky_relu", "celu", "elu", "gelu", "selu", "bent", "softmax", "softmin", "softsign", "softplus", "sigmoid", "tanh". Default: "linear". |
nodes |
Positive integer. Nodes for the forward neural net. Default: 32. |
distr |
String. Distribution to be used by variational model. Implemented distributions are: "normal", "genbeta", "gev", "gpd", "genray", "cauchy", "exp", "logis", "chisq", "gumbel", "laplace", "lognorm", "skewed". Default: "normal". |
optim |
String. Optimization method. Implemented methods are: "adadelta", "adagrad", "rmsprop", "rprop", "sgd", "asgd", "adam". |
loss_metric |
String. Loss function for the variational model. Three options: "elbo", "crps", "score". Default: "crps". |
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. |
latent_sample |
Positive integer. Number of samples to draw from the latent variables. Default: 100. |
verbose |
Logical. Default: TRUE |
stride |
Positive integer. Number of shifting positions for sequence generation. Default: 1. |
dates |
String. Label of feature where dates are located. Default: NULL (progressive numbering). |
rolling_blocks |
Logical. Option for incremental or rolling window. Default: FALSE. |
n_blocks |
Positive integer. Number of distinct blocks for back-testing. Default: 4. |
block_minset |
Positive integer. Minimum number of sequence to create a block. Default: 3. |
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". |
batch_size |
Positive integer. Default: 30. |
omit |
Logical. Flag to TRUE to remove missing values, otherwise all gaps, both in dates and values, will be filled with kalman filter. Default: FALSE. |
min_default |
Positive numeric. Minimum differentiation iteration. Default: 1. |
future_plan |
how to resolve the future parallelization. Options are: "future::sequential", "future::multisession", "future::multicore". For more information, take a look at future specific documentation. Default: "future::multisession". |
seed |
Random seed. Default: 42. |
This function returns a list including:
model_descr: brief model description (number of tensors and parameters)
prediction: a table with quantile predictions, mean, std, mode, skewness and kurtosis for each time feature (and other metrics, such as iqr_to_range, above_to_below_range, upside_prob, divergence).
pred_sampler: empirical function for sampling each prediction point for each time feature
plot: graph with history and prediction for each time feature
feature_errors: train and test error for each time feature (me, mae, mse, rmsse, mpe, mape, rmae, rrmse, rame, mase, smse, sce)
history: average cross-validation loss across blocks
time_log: computation time.
Giancarlo Vercellino [email protected]
https://rpubs.com/giancarlo_vercellino/proteus
proteus_random_search is a function for fine-tuning using random search on the hyper-parameter space of proteus (predefined or custom).
proteus_random_search( n_samp, data, target, future, past = NULL, ci = 0.8, smoother = FALSE, t_embed = NULL, activ = NULL, nodes = NULL, distr = NULL, optim = NULL, loss_metric = "crps", epochs = 30, lr = NULL, patience = 10, latent_sample = 100, verbose = TRUE, stride = NULL, dates = NULL, rolling_blocks = FALSE, n_blocks = 4, block_minset = 10, error_scale = "naive", error_benchmark = "naive", batch_size = 30, min_default = 1, seed = 42, future_plan = "future::multisession", omit = FALSE, keep = FALSE )proteus_random_search( n_samp, data, target, future, past = NULL, ci = 0.8, smoother = FALSE, t_embed = NULL, activ = NULL, nodes = NULL, distr = NULL, optim = NULL, loss_metric = "crps", epochs = 30, lr = NULL, patience = 10, latent_sample = 100, verbose = TRUE, stride = NULL, dates = NULL, rolling_blocks = FALSE, n_blocks = 4, block_minset = 10, error_scale = "naive", error_benchmark = "naive", batch_size = 30, min_default = 1, seed = 42, future_plan = "future::multisession", omit = FALSE, keep = FALSE )
n_samp |
Positive integer. Number of models to be randomly generated sampling the hyper-parameter space. |
data |
A data frame with time features on columns and possibly a date column (not mandatory). |
target |
Vector of strings. Names of the time features to be jointly analyzed. |
future |
Positive integer. The future dimension with number of time-steps to be predicted. |
past |
Positive integer. Length of past sequences. Default: NULL (search range future:2*future). |
ci |
Positive numeric. Confidence interval. Default: 0.8. |
smoother |
Logical. Perform optimal smoothing using standard loess for each time feature. Default: FALSE. |
t_embed |
Positive integer. Number of embedding for the temporal dimension. Minimum value is equal to 2. Default: NULL (search range 2:30). |
activ |
String. Activation function to be used by the forward network. Implemented functions are: "linear", "mish", "swish", "leaky_relu", "celu", "elu", "gelu", "selu", "bent", "softmax", "softmin", "softsign", "softplus", "sigmoid", "tanh". Default: NULL (full-option search). |
nodes |
Positive integer. Nodes for the forward neural net. Default: NULL (search range 2:1024). |
distr |
String. Distribution to be used by variational model. Implemented distributions are: "normal", "genbeta", "gev", "gpd", "genray", "cauchy", "exp", "logis", "chisq", "gumbel", "laplace", "lognorm", "skewed". Default: NULL (full-option search). |
optim |
String. Optimization method. Implemented methods are: "adadelta", "adagrad", "rmsprop", "rprop", "sgd", "asgd", "adam". Default: NULL (full-option search). |
loss_metric |
String. Loss function for the variational model. Three options: "elbo", "crps", "score". Default: "crps". |
epochs |
Positive integer. Default: 30. |
lr |
Positive numeric. Learning rate. Default: NULL (search range 0.001:0.1). |
patience |
Positive integer. Waiting time (in epochs) before evaluating the overfit performance. Default: epochs. |
latent_sample |
Positive integer. Number of samples to draw from the latent variables. Default: 100. |
verbose |
Logical. Default: TRUE |
stride |
Positive integer. Number of shifting positions for sequence generation. Default: NULL (search range 1:3). |
dates |
String. Label of feature where dates are located. Default: NULL (progressive numbering). |
rolling_blocks |
Logical. Option for incremental or rolling window. Default: FALSE. |
n_blocks |
Positive integer. Number of distinct blocks for back-testing. Default: 4. |
block_minset |
Positive integer. Minimum number of sequence to create a block. Default: 3. |
error_scale |
String. Scale for the scaled error metrics (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 (for continuous variables). Two options: "naive" (sequential extension of last value) or "average" (mean value of true sequence). Default: "naive". |
batch_size |
Positive integer. Default: 30. |
min_default |
Positive numeric. Minimum differentiation iteration. Default: 1. |
seed |
Random seed. Default: 42. |
future_plan |
how to resolve the future parallelization. Options are: "future::sequential", "future::multisession", "future::multicore". For more information, take a look at future specific documentation. Default: "future::multisession". |
omit |
Logical. Flag to TRUE to remove missing values, otherwise all gaps, both in dates and values, will be filled with kalman filter. Default: FALSE. |
keep |
Logical. Flag to TRUE to keep all the explored models. Default: FALSE. |
This function returns a list including:
random_search: summary of the sampled hyper-parameters and average error metrics.
best: best model according to overall ranking on all average error metrics (for negative metrics, absolute value is considered).
all_models: list with all generated models (if keep flagged to TRUE).
time_log: computation time.
Giancarlo Vercellino [email protected]
https://rpubs.com/giancarlo_vercellino/proteus