| Title: | Dynamic Bayesian Network Structure Learning, Parameter Learning and Forecasting |
|---|---|
| Description: | It allows to learn the structure of univariate time series, learning parameters and forecasting. Implements a model of Dynamic Bayesian Networks with temporal windows, with collections of linear regressors for Gaussian nodes, based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>. |
| Authors: | Robson Fernandes [aut, cre, cph] |
| Maintainer: | Robson Fernandes <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.0 |
| Built: | 2024-11-14 05:00:35 UTC |
| Source: | https://github.com/robson-fernandes/dbnlearn |
Dynamic Bayesian Network Structure Learning, Parameter Learning and Forecasting. This package implements a model of Gaussian Dynamic Bayesian Networks with temporal windows, based on collections of linear regressors for Gaussian nodes. The package allows learning the structure of univariate time series, learning parameters and forecasting.
| Package: | dbnlearn-package |
| Type: | Package |
| Version: | 0.1.0 |
| Date: | 2020-07-17 |
| License: | MIT + file LICENSE |
Robson Fernandes
Institute of Mathematical and Computer Sciences
University of Sao Paulo - ICMC-USP
Maintainer: Robson Fernandes [email protected]
Koller D, Friedman N (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
Korb K, Nicholson AE (2010). Bayesian Artificial Intelligence. Chapman & Hall/CRC, 2nd edition.
Pearl J (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann.
Nagarajan R, Scutari M, Lebre S (2013). Bayesian Networks in R with Applications in Systems Biology. Springer.
library(dbnlearn) library(bnviewer) library(ggplot2) #Time Series AirPassengers ts <- AirPassengers #Time Series Preprocessing with time window = 12 X.ts = dbn.preprocessing(ts, window = 12) #Define 70% Train and 30% Test Data Set percent = 0.7 n = nrow(X.ts) trainIndex <- seq_len(length.out = floor(x = percent * n)) X.ts.train <- X.ts[trainIndex,] X.ts.test <- X.ts[-trainIndex,] #Dynamic Bayesian Network Structure Learning ts.learning = dbn.learn(X.ts.train) #Viewer Dynamic Bayesian Network viewer(ts.learning, edges.smooth = TRUE, bayesianNetwork.height = "400px", node.colors = list(background = "#f4bafd", border = "#2b7ce9", highlight = list(background = "#97c2fc", border = "#2b7ce9")), bayesianNetwork.layout = "layout_with_sugiyama") #Dynamic Bayesian Network Fit ts.fit = dbn.fit(ts.learning, X.ts.train) #Predict values prediction = dbn.predict(ts.fit, X.ts.test) #Plot Real vs Predict real = X.ts.test[, "X_t"] prediction = prediction df.validation = data.frame(list(real = real, prediction = prediction)) ggplot(df.validation, aes(seq(1:nrow(df.validation)))) + geom_line(aes(y = real, colour="real")) + geom_line(aes(y = prediction, colour="prediction")) + scale_color_manual(values = c( 'real' = 'deepskyblue', 'prediction' = 'maroon1')) + labs(title = "Dynamic Bayesian Network", subtitle = "AirPassengers Time Series", colour = "Legend", x = "Time Index", y = "Values") + theme_minimal()library(dbnlearn) library(bnviewer) library(ggplot2) #Time Series AirPassengers ts <- AirPassengers #Time Series Preprocessing with time window = 12 X.ts = dbn.preprocessing(ts, window = 12) #Define 70% Train and 30% Test Data Set percent = 0.7 n = nrow(X.ts) trainIndex <- seq_len(length.out = floor(x = percent * n)) X.ts.train <- X.ts[trainIndex,] X.ts.test <- X.ts[-trainIndex,] #Dynamic Bayesian Network Structure Learning ts.learning = dbn.learn(X.ts.train) #Viewer Dynamic Bayesian Network viewer(ts.learning, edges.smooth = TRUE, bayesianNetwork.height = "400px", node.colors = list(background = "#f4bafd", border = "#2b7ce9", highlight = list(background = "#97c2fc", border = "#2b7ce9")), bayesianNetwork.layout = "layout_with_sugiyama") #Dynamic Bayesian Network Fit ts.fit = dbn.fit(ts.learning, X.ts.train) #Predict values prediction = dbn.predict(ts.fit, X.ts.test) #Plot Real vs Predict real = X.ts.test[, "X_t"] prediction = prediction df.validation = data.frame(list(real = real, prediction = prediction)) ggplot(df.validation, aes(seq(1:nrow(df.validation)))) + geom_line(aes(y = real, colour="real")) + geom_line(aes(y = prediction, colour="prediction")) + scale_color_manual(values = c( 'real' = 'deepskyblue', 'prediction' = 'maroon1')) + labs(title = "Dynamic Bayesian Network", subtitle = "AirPassengers Time Series", colour = "Legend", x = "Time Index", y = "Values") + theme_minimal()
Dynamic Bayesian Network Fit
dbn.fit(dbn.learn = NULL, ts = NULL)dbn.fit(dbn.learn = NULL, ts = NULL)
dbn.learn |
Dynamic Bayesian Network Learning. |
ts |
Time Series. |
Dynamic Bayesian Network Fit
Robson Fernandes
library(dbnlearn) library(bnviewer) library(ggplot2) #Time Series AirPassengers ts <- AirPassengers #Time Series Preprocessing with time window = 12 X.ts = dbn.preprocessing(ts, window = 12) #Define 70\% Train and 30\% Test Data Set percent = 0.7 n = nrow(X.ts) trainIndex <- seq_len(length.out = floor(x = percent * n)) X.ts.train <- X.ts[trainIndex,] X.ts.test <- X.ts[-trainIndex,] #Dynamic Bayesian Network Structure Learning ts.learning = dbn.learn(X.ts.train) #Viewer Dynamic Bayesian Network viewer(ts.learning, edges.smooth = TRUE, bayesianNetwork.height = "400px", node.colors = list(background = "#f4bafd", border = "#2b7ce9", highlight = list(background = "#97c2fc", border = "#2b7ce9")), bayesianNetwork.layout = "layout_with_sugiyama") #Dynamic Bayesian Network Fit ts.fit = dbn.fit(ts.learning, X.ts.train) #Predict values prediction = dbn.predict(ts.fit, X.ts.test) #Plot Real vs Predict real = X.ts.test[, "X_t"] prediction = prediction df.validation = data.frame(list(real = real, prediction = prediction)) ggplot(df.validation, aes(seq(1:nrow(df.validation)))) + geom_line(aes(y = real, colour="real")) + geom_line(aes(y = prediction, colour="prediction")) + scale_color_manual(values = c( 'real' = 'deepskyblue', 'prediction' = 'maroon1')) + labs(title = "Dynamic Bayesian Network", subtitle = "AirPassengers Time Series", colour = "Legend", x = "Time Index", y = "Values") + theme_minimal()library(dbnlearn) library(bnviewer) library(ggplot2) #Time Series AirPassengers ts <- AirPassengers #Time Series Preprocessing with time window = 12 X.ts = dbn.preprocessing(ts, window = 12) #Define 70\% Train and 30\% Test Data Set percent = 0.7 n = nrow(X.ts) trainIndex <- seq_len(length.out = floor(x = percent * n)) X.ts.train <- X.ts[trainIndex,] X.ts.test <- X.ts[-trainIndex,] #Dynamic Bayesian Network Structure Learning ts.learning = dbn.learn(X.ts.train) #Viewer Dynamic Bayesian Network viewer(ts.learning, edges.smooth = TRUE, bayesianNetwork.height = "400px", node.colors = list(background = "#f4bafd", border = "#2b7ce9", highlight = list(background = "#97c2fc", border = "#2b7ce9")), bayesianNetwork.layout = "layout_with_sugiyama") #Dynamic Bayesian Network Fit ts.fit = dbn.fit(ts.learning, X.ts.train) #Predict values prediction = dbn.predict(ts.fit, X.ts.test) #Plot Real vs Predict real = X.ts.test[, "X_t"] prediction = prediction df.validation = data.frame(list(real = real, prediction = prediction)) ggplot(df.validation, aes(seq(1:nrow(df.validation)))) + geom_line(aes(y = real, colour="real")) + geom_line(aes(y = prediction, colour="prediction")) + scale_color_manual(values = c( 'real' = 'deepskyblue', 'prediction' = 'maroon1')) + labs(title = "Dynamic Bayesian Network", subtitle = "AirPassengers Time Series", colour = "Legend", x = "Time Index", y = "Values") + theme_minimal()
Dynamic Bayesian Network Structure Learning
dbn.learn(ts = NULL)dbn.learn(ts = NULL)
ts |
Time Series. |
Dynamic Bayesian Network Structure Learning
Robson Fernandes
library(dbnlearn) library(bnviewer) library(ggplot2) #Time Series AirPassengers ts <- AirPassengers #Time Series Preprocessing with time window = 12 X.ts = dbn.preprocessing(ts, window = 12) #Define 70\% Train and 30\% Test Data Set percent = 0.7 n = nrow(X.ts) trainIndex <- seq_len(length.out = floor(x = percent * n)) X.ts.train <- X.ts[trainIndex,] X.ts.test <- X.ts[-trainIndex,] #Dynamic Bayesian Network Structure Learning ts.learning = dbn.learn(X.ts.train) #Viewer Dynamic Bayesian Network viewer(ts.learning, edges.smooth = TRUE, bayesianNetwork.height = "400px", node.colors = list(background = "#f4bafd", border = "#2b7ce9", highlight = list(background = "#97c2fc", border = "#2b7ce9")), bayesianNetwork.layout = "layout_with_sugiyama") #Dynamic Bayesian Network Fit ts.fit = dbn.fit(ts.learning, X.ts.train) #Predict values prediction = dbn.predict(ts.fit, X.ts.test) #Plot Real vs Predict real = X.ts.test[, "X_t"] prediction = prediction df.validation = data.frame(list(real = real, prediction = prediction)) ggplot(df.validation, aes(seq(1:nrow(df.validation)))) + geom_line(aes(y = real, colour="real")) + geom_line(aes(y = prediction, colour="prediction")) + scale_color_manual(values = c( 'real' = 'deepskyblue', 'prediction' = 'maroon1')) + labs(title = "Dynamic Bayesian Network", subtitle = "AirPassengers Time Series", colour = "Legend", x = "Time Index", y = "Values") + theme_minimal()library(dbnlearn) library(bnviewer) library(ggplot2) #Time Series AirPassengers ts <- AirPassengers #Time Series Preprocessing with time window = 12 X.ts = dbn.preprocessing(ts, window = 12) #Define 70\% Train and 30\% Test Data Set percent = 0.7 n = nrow(X.ts) trainIndex <- seq_len(length.out = floor(x = percent * n)) X.ts.train <- X.ts[trainIndex,] X.ts.test <- X.ts[-trainIndex,] #Dynamic Bayesian Network Structure Learning ts.learning = dbn.learn(X.ts.train) #Viewer Dynamic Bayesian Network viewer(ts.learning, edges.smooth = TRUE, bayesianNetwork.height = "400px", node.colors = list(background = "#f4bafd", border = "#2b7ce9", highlight = list(background = "#97c2fc", border = "#2b7ce9")), bayesianNetwork.layout = "layout_with_sugiyama") #Dynamic Bayesian Network Fit ts.fit = dbn.fit(ts.learning, X.ts.train) #Predict values prediction = dbn.predict(ts.fit, X.ts.test) #Plot Real vs Predict real = X.ts.test[, "X_t"] prediction = prediction df.validation = data.frame(list(real = real, prediction = prediction)) ggplot(df.validation, aes(seq(1:nrow(df.validation)))) + geom_line(aes(y = real, colour="real")) + geom_line(aes(y = prediction, colour="prediction")) + scale_color_manual(values = c( 'real' = 'deepskyblue', 'prediction' = 'maroon1')) + labs(title = "Dynamic Bayesian Network", subtitle = "AirPassengers Time Series", colour = "Legend", x = "Time Index", y = "Values") + theme_minimal()
Dynamic Bayesian Network Predict
dbn.predict(dbn.fit = NULL, ts = NULL)dbn.predict(dbn.fit = NULL, ts = NULL)
dbn.fit |
Dynamic Bayesian Network Fit |
ts |
Time Series. |
Dynamic Bayesian Network Predict
Robson Fernandes
library(dbnlearn) library(bnviewer) library(ggplot2) #Time Series AirPassengers ts <- AirPassengers #Time Series Preprocessing with time window = 12 X.ts = dbn.preprocessing(ts, window = 12) #Define 70\% Train and 30\% Test Data Set percent = 0.7 n = nrow(X.ts) trainIndex <- seq_len(length.out = floor(x = percent * n)) X.ts.train <- X.ts[trainIndex,] X.ts.test <- X.ts[-trainIndex,] #Dynamic Bayesian Network Structure Learning ts.learning = dbn.learn(X.ts.train) #Viewer Dynamic Bayesian Network viewer(ts.learning, edges.smooth = TRUE, bayesianNetwork.height = "400px", node.colors = list(background = "#f4bafd", border = "#2b7ce9", highlight = list(background = "#97c2fc", border = "#2b7ce9")), bayesianNetwork.layout = "layout_with_sugiyama") #Dynamic Bayesian Network Fit ts.fit = dbn.fit(ts.learning, X.ts.train) #Predict values prediction = dbn.predict(ts.fit, X.ts.test) #Plot Real vs Predict real = X.ts.test[, "X_t"] prediction = prediction df.validation = data.frame(list(real = real, prediction = prediction)) ggplot(df.validation, aes(seq(1:nrow(df.validation)))) + geom_line(aes(y = real, colour="real")) + geom_line(aes(y = prediction, colour="prediction")) + scale_color_manual(values = c( 'real' = 'deepskyblue', 'prediction' = 'maroon1')) + labs(title = "Dynamic Bayesian Network", subtitle = "AirPassengers Time Series", colour = "Legend", x = "Time Index", y = "Values") + theme_minimal()library(dbnlearn) library(bnviewer) library(ggplot2) #Time Series AirPassengers ts <- AirPassengers #Time Series Preprocessing with time window = 12 X.ts = dbn.preprocessing(ts, window = 12) #Define 70\% Train and 30\% Test Data Set percent = 0.7 n = nrow(X.ts) trainIndex <- seq_len(length.out = floor(x = percent * n)) X.ts.train <- X.ts[trainIndex,] X.ts.test <- X.ts[-trainIndex,] #Dynamic Bayesian Network Structure Learning ts.learning = dbn.learn(X.ts.train) #Viewer Dynamic Bayesian Network viewer(ts.learning, edges.smooth = TRUE, bayesianNetwork.height = "400px", node.colors = list(background = "#f4bafd", border = "#2b7ce9", highlight = list(background = "#97c2fc", border = "#2b7ce9")), bayesianNetwork.layout = "layout_with_sugiyama") #Dynamic Bayesian Network Fit ts.fit = dbn.fit(ts.learning, X.ts.train) #Predict values prediction = dbn.predict(ts.fit, X.ts.test) #Plot Real vs Predict real = X.ts.test[, "X_t"] prediction = prediction df.validation = data.frame(list(real = real, prediction = prediction)) ggplot(df.validation, aes(seq(1:nrow(df.validation)))) + geom_line(aes(y = real, colour="real")) + geom_line(aes(y = prediction, colour="prediction")) + scale_color_manual(values = c( 'real' = 'deepskyblue', 'prediction' = 'maroon1')) + labs(title = "Dynamic Bayesian Network", subtitle = "AirPassengers Time Series", colour = "Legend", x = "Time Index", y = "Values") + theme_minimal()
Time Series Preprocessing with time window.
dbn.preprocessing(ts = NULL, window = 12)dbn.preprocessing(ts = NULL, window = 12)
ts |
Time Series. |
window |
Number of steps in the time window. |
Time Series Preprocessing
Robson Fernandes
library(dbnlearn) library(bnviewer) library(ggplot2) #Time Series AirPassengers ts <- AirPassengers #Time Series Preprocessing with time window = 12 X.ts = dbn.preprocessing(ts, window = 12) #Define 70\% Train and 30\% Test Data Set percent = 0.7 n = nrow(X.ts) trainIndex <- seq_len(length.out = floor(x = percent * n)) X.ts.train <- X.ts[trainIndex,] X.ts.test <- X.ts[-trainIndex,] #Dynamic Bayesian Network Structure Learning ts.learning = dbn.learn(X.ts.train) #Viewer Dynamic Bayesian Network viewer(ts.learning, edges.smooth = TRUE, bayesianNetwork.height = "400px", node.colors = list(background = "#f4bafd", border = "#2b7ce9", highlight = list(background = "#97c2fc", border = "#2b7ce9")), bayesianNetwork.layout = "layout_with_sugiyama") #Dynamic Bayesian Network Fit ts.fit = dbn.fit(ts.learning, X.ts.train) #Predict values prediction = dbn.predict(ts.fit, X.ts.test) #Plot Real vs Predict real = X.ts.test[, "X_t"] prediction = prediction df.validation = data.frame(list(real = real, prediction = prediction)) ggplot(df.validation, aes(seq(1:nrow(df.validation)))) + geom_line(aes(y = real, colour="real")) + geom_line(aes(y = prediction, colour="prediction")) + scale_color_manual(values = c( 'real' = 'deepskyblue', 'prediction' = 'maroon1')) + labs(title = "Dynamic Bayesian Network", subtitle = "AirPassengers Time Series", colour = "Legend", x = "Time Index", y = "Values") + theme_minimal()library(dbnlearn) library(bnviewer) library(ggplot2) #Time Series AirPassengers ts <- AirPassengers #Time Series Preprocessing with time window = 12 X.ts = dbn.preprocessing(ts, window = 12) #Define 70\% Train and 30\% Test Data Set percent = 0.7 n = nrow(X.ts) trainIndex <- seq_len(length.out = floor(x = percent * n)) X.ts.train <- X.ts[trainIndex,] X.ts.test <- X.ts[-trainIndex,] #Dynamic Bayesian Network Structure Learning ts.learning = dbn.learn(X.ts.train) #Viewer Dynamic Bayesian Network viewer(ts.learning, edges.smooth = TRUE, bayesianNetwork.height = "400px", node.colors = list(background = "#f4bafd", border = "#2b7ce9", highlight = list(background = "#97c2fc", border = "#2b7ce9")), bayesianNetwork.layout = "layout_with_sugiyama") #Dynamic Bayesian Network Fit ts.fit = dbn.fit(ts.learning, X.ts.train) #Predict values prediction = dbn.predict(ts.fit, X.ts.test) #Plot Real vs Predict real = X.ts.test[, "X_t"] prediction = prediction df.validation = data.frame(list(real = real, prediction = prediction)) ggplot(df.validation, aes(seq(1:nrow(df.validation)))) + geom_line(aes(y = real, colour="real")) + geom_line(aes(y = prediction, colour="prediction")) + scale_color_manual(values = c( 'real' = 'deepskyblue', 'prediction' = 'maroon1')) + labs(title = "Dynamic Bayesian Network", subtitle = "AirPassengers Time Series", colour = "Legend", x = "Time Index", y = "Values") + theme_minimal()