, Nicolò Rubattu [aut]
, Lorenzo Zambon [aut]
, Giorgio Corani [aut]
| Title: | Probabilistic Reconciliation via Conditioning |
|---|---|
| Description: | Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) <doi:10.1007/978-3-030-67664-3_13>, MCMC reconciliation of count time series (Corani et al., 2024) <doi:10.1016/j.ijforecast.2023.04.003>, Bottom-Up Importance Sampling (Zambon et al., 2024) <doi:10.1007/s11222-023-10343-y>, methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024. The 40th Conference on Uncertainty in Artificial Intelligence, accepted). |
| Authors: | Dario Azzimonti [aut, cre]
, Nicolò Rubattu [aut]
, Lorenzo Zambon [aut]
, Giorgio Corani [aut]
|
| Maintainer: | Dario Azzimonti <[email protected]> |
| License: | LGPL (>= 3) |
| Version: | 0.3.2 |
| Built: | 2024-11-05 06:49:17 UTC |
| Source: | CRAN |
A monthly time series from the carparts dataset, 51 observations, Jan 1998 - Mar 2002.
carparts_examplecarparts_example
Univariate time series of class ts.
Godahewa, R., Bergmeir, C., Webb, G., Hyndman, R.J., & Montero-Manso, P. (2020). Car Parts Dataset (without Missing Values) (Version 2) doi:10.5281/zenodo.4656021
Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D., (2008). Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media.
Godahewa, R., Bergmeir, C., Webb, G., Hyndman, R., & Montero-Manso, P. (2020). Car Parts Dataset (without Missing Values) (Version 2) doi:10.5281/zenodo.4656021
Count time series of extreme market events in five economic sectors. The data refer to the trading days between 2004/12/31 and 2018/12/19 (3508 trading days in total).
extr_mkt_eventsextr_mkt_events
A multivariate time series of class ts.
The counts are computed by considering 29 companies included in the Euro Stoxx 50 index and observing if the value of the CDS spread on a given day exceeds the 90-th percentile of its distribution in the last trading year. The companies are divided in the following sectors: Financial (FIN), Information and Communication Technology (ICT), Manufacturing (MFG), Energy (ENG), and Trade (TRD).
There are 6 time series:
5 bottom time series, corresponding to the daily counts for each sector
1 upper time series, which is the sum of all the bottom (ALL)
Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). Properties of the reconciled distributions for Gaussian and count forecasts. International Journal of Forecasting (in press). doi:10.1016/j.ijforecast.2023.12.004.
Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). Properties of the reconciled distributions for Gaussian and count forecasts. International Journal of Forecasting (in press). doi:10.1016/j.ijforecast.2023.12.004.
Agosto, A. (2022). Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion. doi:10.2139/ssrn.4119895
Base forecasts for the extr_mkt_events dataset, computed using the model by
Agosto, A. (2022).
Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion.
doi:10.2139/ssrn.4119895.
extr_mkt_events_basefcextr_mkt_events_basefc
A list extr_mkt_events_basefc containing
extr_mkt_events_basefc$mudata frame of the base forecast means, for each day
extr_mkt_events_basefc$sizedata frame of the static base forecast size parameters
The predictive distribution for the bottom time series is a multivariate negative binomial with a static vector of dispersion parameters and a time-varying vector of location parameters following a score-driven dynamics. The base forecasts for the upper time series are computed using a univariate version of this model. They are in-sample forecasts: for each training instant, they are computed for time t+1 by conditioning on the counts observed up to time t.
Agosto, A. (2022). Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion. doi:10.2139/ssrn.4119895
Agosto, A. (2022). Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion. doi:10.2139/ssrn.4119895
Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). Properties of the reconciled distributions for Gaussian and count forecasts. International Journal of Forecasting (in press). doi:10.1016/j.ijforecast.2023.12.004.
Creates the aggregation and summing matrices for a temporal hierarchy of time series from a user-selected list of aggregation levels.
get_reconc_matrices(agg_levels, h)get_reconc_matrices(agg_levels, h)
agg_levels |
user-selected list of aggregation levels. |
h |
number of steps ahead for the bottom level forecasts. |
A list containing the named elements:
A the aggregation matrix;
S the summing matrix.
library(bayesRecon) #Create monthly hierarchy agg_levels <- c(1,2,3,4,6,12) h <- 12 rec_mat <- get_reconc_matrices(agg_levels, h) S <- rec_mat$S A <- rec_mat$Alibrary(bayesRecon) #Create monthly hierarchy agg_levels <- c(1,2,3,4,6,12) h <- 12 rec_mat <- get_reconc_matrices(agg_levels, h) S <- rec_mat$S A <- rec_mat$A
A yearly grouped time series dataset, from 1901 to 2003, of infant mortality counts (deaths) in Australia; disaggregated by state (see below), and sex (male and female).
infantMortalityinfantMortality
List of time series of class ts.
States: New South Wales (NSW), Victoria (VIC), Queensland (QLD), South Australia (SA), Western Australia (WA), Northern Territory (NT), Australian Capital Territory (ACT), and Tasmania (TAS).
hts package CRAN
Hyndman, R.J., Ahmed, R.A., Athanasopoulos, G., Shang, H.L. (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579-2589.
A monthly time series, from the M3 forecasting competition ("N1485").
M3_exampleM3_example
List of time series of class ts.
https://forecasters.org/resources/time-series-data/m3-competition/
This dataset contains forecasts for the hierarchy of time series related to the store CA_1 from the M5 competition.
M5_CA1_basefcM5_CA1_basefc
A list containing:
upper: a list of 11 elements each representing an aggregation level. Each element contains: mu, sigma the mean and standard deviation of the Gaussian forecast, actual the actual value, residuals the residuals of the model used to estimate forecasts covariance.
lower: a list of 3049 elements each representing a forecast for each item. Each element contains pmf the probability mass function of the item level forecast, actual the actual value.
A: the aggregation matrix for A.
S: the S matrix for the hierarchy.
Q_u: scaling factors for computing MASE on the upper forecasts.
Q_b: scaling factors for computing MASE on the bottom forecasts.
The store CA_1 contains 3049 item level time series and 11 aggregate time series:
Store level aggregation (CA_1)
Category level aggregations (HOBBIES, HOUSEHOLD, FOODS)
Department level aggregations (HOBBIES_1, HOBBIES_2, HOUSEHOLD_1, HOUSEHOLD_2, FOODS_1, FOODS_2, FOODS_3)
Forecasts are generated with the function forecast and the model adam from the package smooth.
The models for the bottom time series are selected with multiplicative Gamma error term (MNN);
The models for the upper time series (AXZ) is selected with Gaussian additive error term, seasonality selected based on information criterion.
The raw data was downloaded with the package m5.
Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilis. (2020). The M5 Accuracy competition: Results, findings and conclusions. International Journal of Forecasting 38(4) 1346-1364. doi:10.1016/j.ijforecast.2021.10.009
Joachimiak K (2022). m5: 'M5 Forecasting' Challenges Data. R package version 0.1.1, https://CRAN.R-project.org/package=m5.
Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilis. (2020). The M5 Accuracy competition: Results, findings and conclusions. International Journal of Forecasting 38(4) 1346-1364. doi:10.1016/j.ijforecast.2021.10.009
Svetunkov I (2023). smooth: Forecasting Using State Space Models. R package version 4.0.0, https://CRAN.R-project.org/package=smooth.
Returns the mean from the PMF specified by pmf.
PMF.get_mean(pmf)PMF.get_mean(pmf)
pmf |
the PMF object. |
A numerical value for mean of the distribution.
PMF.get_var(), PMF.get_quantile(), PMF.sample(), PMF.summary()
library(bayesRecon) # Let's build the pmf of a Binomial distribution with parameters n and p n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) # The true mean corresponds to n*p true_mean <- n*p mean_from_PMF <- PMF.get_mean(pmf=pmf_binomial) cat("True mean:", true_mean, "\nMean from PMF:", mean_from_PMF)library(bayesRecon) # Let's build the pmf of a Binomial distribution with parameters n and p n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) # The true mean corresponds to n*p true_mean <- n*p mean_from_PMF <- PMF.get_mean(pmf=pmf_binomial) cat("True mean:", true_mean, "\nMean from PMF:", mean_from_PMF)
Returns the p quantile from the PMF specified by pmf.
PMF.get_quantile(pmf, p)PMF.get_quantile(pmf, p)
pmf |
the PMF object. |
p |
the probability of the required quantile. |
A numeric value for the quantile.
PMF.get_mean(), PMF.get_var(), PMF.sample(), PMF.summary()
library(bayesRecon) # Let's build the pmf of a Binomial distribution with parameters n and p n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) # The true median is ceiling(n*p) quant_50 <- PMF.get_quantile(pmf=pmf_binomial,p=0.5) cat("True median:", ceiling(n*p), "\nMedian from PMF:", quant_50)library(bayesRecon) # Let's build the pmf of a Binomial distribution with parameters n and p n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) # The true median is ceiling(n*p) quant_50 <- PMF.get_quantile(pmf=pmf_binomial,p=0.5) cat("True median:", ceiling(n*p), "\nMedian from PMF:", quant_50)
Returns the variance from the PMF specified by pmf.
PMF.get_var(pmf)PMF.get_var(pmf)
pmf |
the PMF object. |
A numerical value for variance.
PMF.get_mean(), PMF.get_quantile(), PMF.sample(), PMF.summary()
library(bayesRecon) # Let's build the pmf of a Binomial distribution with parameters n and p n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) # The true variance corresponds to n*p*(1-p) true_var <- n*p*(1-p) var_from_PMF <- PMF.get_var(pmf=pmf_binomial) cat("True variance:", true_var, "\nVariance from PMF:", var_from_PMF)library(bayesRecon) # Let's build the pmf of a Binomial distribution with parameters n and p n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) # The true variance corresponds to n*p*(1-p) true_var <- n*p*(1-p) var_from_PMF <- PMF.get_var(pmf=pmf_binomial) cat("True variance:", true_var, "\nVariance from PMF:", var_from_PMF)
Samples (with replacement) from the probability distribution specified by pmf.
PMF.sample(pmf, N_samples)PMF.sample(pmf, N_samples)
pmf |
the PMF object. |
N_samples |
number of samples. |
Samples drawn from the distribution specified by pmf.
PMF.get_mean(), PMF.get_var(), PMF.get_quantile(), PMF.summary()
library(bayesRecon) # Let's build the pmf of a Binomial distribution with parameters n and p n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,n)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) # Draw samples from the PMF object set.seed(1) samples <- PMF.sample(pmf=pmf_binomial,N_samples = 1e4) # Plot the histogram computed with the samples and the true value of the PMF hist(samples,breaks=seq(0,n),freq=FALSE) points(seq(0,n)-0.5,pmf_binomial,pch=16)library(bayesRecon) # Let's build the pmf of a Binomial distribution with parameters n and p n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,n)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) # Draw samples from the PMF object set.seed(1) samples <- PMF.sample(pmf=pmf_binomial,N_samples = 1e4) # Plot the histogram computed with the samples and the true value of the PMF hist(samples,breaks=seq(0,n),freq=FALSE) points(seq(0,n)-0.5,pmf_binomial,pch=16)
Returns the summary (min, max, IQR, median, mean) of the PMF specified by pmf.
PMF.summary(pmf, Ltoll = .TOLL, Rtoll = .RTOLL)PMF.summary(pmf, Ltoll = .TOLL, Rtoll = .RTOLL)
pmf |
the PMF object. |
Ltoll |
used for computing the min of the PMF: the min is the smallest value with probability greater than Ltoll (default: 1e-15) |
Rtoll |
used for computing the max of the PMF: the max is the largest value with probability greater than Rtoll (default: 1e-9) |
A summary data.frame
PMF.get_mean(), PMF.get_var(), PMF.get_quantile(), PMF.sample()
library(bayesRecon) # Let's build the pmf of a Binomial distribution with parameters n and p n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) # Print the summary of this distribution PMF.summary(pmf=pmf_binomial)library(bayesRecon) # Let's build the pmf of a Binomial distribution with parameters n and p n <- 10 p <- 0.6 pmf_binomial <- apply(matrix(seq(0,10)),MARGIN=1,FUN=function(x) dbinom(x,size=n,prob=p)) # Print the summary of this distribution PMF.summary(pmf=pmf_binomial)
Uses the Bottom-Up Importance Sampling algorithm to draw samples from the reconciled forecast distribution, obtained via conditioning.
reconc_BUIS( A, base_forecasts, in_type, distr, num_samples = 20000, suppress_warnings = FALSE, seed = NULL )reconc_BUIS( A, base_forecasts, in_type, distr, num_samples = 20000, suppress_warnings = FALSE, seed = NULL )
A |
aggregation matrix (n_upper x n_bottom). |
base_forecasts |
A list containing the base_forecasts, see details. |
in_type |
A string or a list of length n_upper + n_bottom. If it is a list the i-th element is a string with two possible values:
If it |
distr |
A string or a list of length n_upper + n_bottom describing the type of base forecasts. If it is a list the i-th element is a string with two possible values:
If |
num_samples |
Number of samples drawn from the reconciled distribution.
This is ignored if |
suppress_warnings |
Logical. If |
seed |
Seed for reproducibility. |
The parameter base_forecast is a list containing n = n_upper + n_bottom elements.
The first n_upper elements of the list are the upper base forecasts, in the order given by the rows of A.
The elements from n_upper+1 until the end of the list are the bottom base forecasts, in the order given by the columns of A.
The i-th element depends on the values of in_type[[i]] and distr[[i]].
If in_type[[i]]='samples', then base_forecast[[i]] is a vector containing samples from the base forecast distribution.
If in_type[[i]]='params', then base_forecast[[i]] is a list containing the estimated:
mean and sd for the Gaussian base forecast if distr[[i]]='gaussian', see Normal;
lambda for the Poisson base forecast if distr[[i]]='poisson', see Poisson;
size and prob (or mu) for the negative binomial base forecast if distr[[i]]='nbinom', see NegBinomial.
See the description of the parameters in_type and distr for more details.
Warnings are triggered from the Importance Sampling step if:
weights are all zeros, then the upper is ignored during reconciliation;
the effective sample size is < 200;
the effective sample size is < 1% of the sample size (num_samples if in_type is 'params' or the size of the base forecast if if in_type is 'samples').
Note that warnings are an indication that the base forecasts might have issues. Please check the base forecasts in case of warnings.
A list containing the reconciled forecasts. The list has the following named elements:
bottom_reconciled_samples: a matrix (n_bottom x num_samples) containing the reconciled samples for the bottom time series;
upper_reconciled_samples: a matrix (n_upper x num_samples) containing the reconciled samples for the upper time series;
reconciled_samples: a matrix (n x num_samples) containing the reconciled samples for all time series.
Zambon, L., Azzimonti, D. & Corani, G. (2024). Efficient probabilistic reconciliation of forecasts for real-valued and count time series. Statistics and Computing 34 (1), 21. doi:10.1007/s11222-023-10343-y.
library(bayesRecon) # Create a minimal hierarchy with 2 bottom and 1 upper variable rec_mat <- get_reconc_matrices(agg_levels=c(1,2), h=2) A <- rec_mat$A S <- rec_mat$S #1) Gaussian base forecasts #Set the parameters of the Gaussian base forecast distributions mu1 <- 2 mu2 <- 4 muY <- 9 mus <- c(muY,mu1,mu2) sigma1 <- 2 sigma2 <- 2 sigmaY <- 3 sigmas <- c(sigmaY,sigma1,sigma2) base_forecasts = list() for (i in 1:length(mus)) { base_forecasts[[i]] = list(mean = mus[[i]], sd = sigmas[[i]]) } #Sample from the reconciled forecast distribution using the BUIS algorithm buis <- reconc_BUIS(A, base_forecasts, in_type="params", distr="gaussian", num_samples=100000, seed=42) samples_buis <- buis$reconciled_samples #In the Gaussian case, the reconciled distribution is still Gaussian and can be #computed in closed form Sigma <- diag(sigmas^2) #transform into covariance matrix analytic_rec <- reconc_gaussian(A, base_forecasts.mu = mus, base_forecasts.Sigma = Sigma) #Compare the reconciled means obtained analytically and via BUIS print(c(S %*% analytic_rec$bottom_reconciled_mean)) print(rowMeans(samples_buis)) #2) Poisson base forecasts #Set the parameters of the Poisson base forecast distributions lambda1 <- 2 lambda2 <- 4 lambdaY <- 9 lambdas <- c(lambdaY,lambda1,lambda2) base_forecasts <- list() for (i in 1:length(lambdas)) { base_forecasts[[i]] = list(lambda = lambdas[i]) } #Sample from the reconciled forecast distribution using the BUIS algorithm buis <- reconc_BUIS(A, base_forecasts, in_type="params", distr="poisson", num_samples=100000, seed=42) samples_buis <- buis$reconciled_samples #Print the reconciled means print(rowMeans(samples_buis))library(bayesRecon) # Create a minimal hierarchy with 2 bottom and 1 upper variable rec_mat <- get_reconc_matrices(agg_levels=c(1,2), h=2) A <- rec_mat$A S <- rec_mat$S #1) Gaussian base forecasts #Set the parameters of the Gaussian base forecast distributions mu1 <- 2 mu2 <- 4 muY <- 9 mus <- c(muY,mu1,mu2) sigma1 <- 2 sigma2 <- 2 sigmaY <- 3 sigmas <- c(sigmaY,sigma1,sigma2) base_forecasts = list() for (i in 1:length(mus)) { base_forecasts[[i]] = list(mean = mus[[i]], sd = sigmas[[i]]) } #Sample from the reconciled forecast distribution using the BUIS algorithm buis <- reconc_BUIS(A, base_forecasts, in_type="params", distr="gaussian", num_samples=100000, seed=42) samples_buis <- buis$reconciled_samples #In the Gaussian case, the reconciled distribution is still Gaussian and can be #computed in closed form Sigma <- diag(sigmas^2) #transform into covariance matrix analytic_rec <- reconc_gaussian(A, base_forecasts.mu = mus, base_forecasts.Sigma = Sigma) #Compare the reconciled means obtained analytically and via BUIS print(c(S %*% analytic_rec$bottom_reconciled_mean)) print(rowMeans(samples_buis)) #2) Poisson base forecasts #Set the parameters of the Poisson base forecast distributions lambda1 <- 2 lambda2 <- 4 lambdaY <- 9 lambdas <- c(lambdaY,lambda1,lambda2) base_forecasts <- list() for (i in 1:length(lambdas)) { base_forecasts[[i]] = list(lambda = lambdas[i]) } #Sample from the reconciled forecast distribution using the BUIS algorithm buis <- reconc_BUIS(A, base_forecasts, in_type="params", distr="poisson", num_samples=100000, seed=42) samples_buis <- buis$reconciled_samples #Print the reconciled means print(rowMeans(samples_buis))
Closed form computation of the reconciled forecasts in case of Gaussian base forecasts.
reconc_gaussian(A, base_forecasts.mu, base_forecasts.Sigma)reconc_gaussian(A, base_forecasts.mu, base_forecasts.Sigma)
A |
aggregation matrix (n_upper x n_bottom). |
base_forecasts.mu |
a vector containing the means of the base forecasts. |
base_forecasts.Sigma |
a matrix containing the covariance matrix of the base forecasts. |
In the vector of the means of the base forecasts the order must be: first the upper, then the bottom; the order within the uppers is given by the rows of A, the order within the bottoms by the columns of A. The order of the rows of the covariance matrix of the base forecasts is the same.
The function returns only the reconciled parameters of the bottom variables. The reconciled upper parameters and the reconciled samples for the entire hierarchy can be obtained from the reconciled bottom parameters. See the example section.
A list containing the bottom reconciled forecasts. The list has the following named elements:
bottom_reconciled_mean: reconciled mean for the bottom forecasts;
bottom_reconciled_covariance: reconciled covariance for the bottom forecasts.
Corani, G., Azzimonti, D., Augusto, J.P.S.C., Zaffalon, M. (2021). Probabilistic Reconciliation of Hierarchical Forecast via Bayes' Rule. ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459. doi:10.1007/978-3-030-67664-3_13.
Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). Properties of the reconciled distributions for Gaussian and count forecasts. International Journal of Forecasting (in press). doi:10.1016/j.ijforecast.2023.12.004.
library(bayesRecon) # Create a minimal hierarchy with 2 bottom and 1 upper variable A <- get_reconc_matrices(agg_levels=c(1,2), h=2)$A #Set the parameters of the Gaussian base forecast distributions mu1 <- 2 mu2 <- 4 muY <- 9 mus <- c(muY,mu1,mu2) sigma1 <- 2 sigma2 <- 2 sigmaY <- 3 sigmas <- c(sigmaY,sigma1,sigma2) Sigma <- diag(sigmas^2) # need to transform into covariance matrix analytic_rec <- reconc_gaussian(A, base_forecasts.mu = mus, base_forecasts.Sigma = Sigma) bottom_mu_reconc <- analytic_rec$bottom_reconciled_mean bottom_Sigma_reconc <- analytic_rec$bottom_reconciled_covariance # Obtain reconciled mu and Sigma for the upper variable upper_mu_reconc <- A %*% bottom_mu_reconc upper_Sigma_reconc <- A %*% bottom_Sigma_reconc %*% t(A) # Obtain reconciled mu and Sigma for the entire hierarchy S <- rbind(A, diag(2)) # first, get summing matrix S Y_mu_reconc <- S %*% bottom_mu_reconc Y_Sigma_reconc <- S %*% bottom_Sigma_reconc %*% t(S) # note that this is a singular matrix # Obtain reconciled samples for the entire hierarchy: # i.e., sample from the reconciled bottoms and multiply by S chol_decomp = chol(bottom_Sigma_reconc) # Compute the Cholesky Decomposition Z = matrix(stats::rnorm(n = 2000), nrow = 2) # Sample from standard normal B = t(chol_decomp) %*% Z + matrix(rep(bottom_mu_reconc, 1000), nrow=2) # Apply the transformation U = S %*% B Y_reconc = rbind(U, B)library(bayesRecon) # Create a minimal hierarchy with 2 bottom and 1 upper variable A <- get_reconc_matrices(agg_levels=c(1,2), h=2)$A #Set the parameters of the Gaussian base forecast distributions mu1 <- 2 mu2 <- 4 muY <- 9 mus <- c(muY,mu1,mu2) sigma1 <- 2 sigma2 <- 2 sigmaY <- 3 sigmas <- c(sigmaY,sigma1,sigma2) Sigma <- diag(sigmas^2) # need to transform into covariance matrix analytic_rec <- reconc_gaussian(A, base_forecasts.mu = mus, base_forecasts.Sigma = Sigma) bottom_mu_reconc <- analytic_rec$bottom_reconciled_mean bottom_Sigma_reconc <- analytic_rec$bottom_reconciled_covariance # Obtain reconciled mu and Sigma for the upper variable upper_mu_reconc <- A %*% bottom_mu_reconc upper_Sigma_reconc <- A %*% bottom_Sigma_reconc %*% t(A) # Obtain reconciled mu and Sigma for the entire hierarchy S <- rbind(A, diag(2)) # first, get summing matrix S Y_mu_reconc <- S %*% bottom_mu_reconc Y_Sigma_reconc <- S %*% bottom_Sigma_reconc %*% t(S) # note that this is a singular matrix # Obtain reconciled samples for the entire hierarchy: # i.e., sample from the reconciled bottoms and multiply by S chol_decomp = chol(bottom_Sigma_reconc) # Compute the Cholesky Decomposition Z = matrix(stats::rnorm(n = 2000), nrow = 2) # Sample from standard normal B = t(chol_decomp) %*% Z + matrix(rep(bottom_mu_reconc, 1000), nrow=2) # Apply the transformation U = S %*% B Y_reconc = rbind(U, B)
Uses Markov Chain Monte Carlo algorithm to draw samples from the reconciled forecast distribution, which is obtained via conditioning.
This is a bare-bones implementation of the Metropolis-Hastings algorithm, we suggest the usage of tools to check the convergence. The function only works with Poisson or Negative Binomial base forecasts.
The function reconc_BUIS() is generally faster on most hierarchies.
reconc_MCMC( A, base_forecasts, distr, num_samples = 10000, tuning_int = 100, init_scale = 1, burn_in = 1000, seed = NULL )reconc_MCMC( A, base_forecasts, distr, num_samples = 10000, tuning_int = 100, init_scale = 1, burn_in = 1000, seed = NULL )
A |
aggregation matrix (n_upper x n_bottom). |
base_forecasts |
list of the parameters of the base forecast distributions, see details. |
distr |
a string describing the type of predictive distribution. |
num_samples |
number of samples to draw using MCMC. |
tuning_int |
number of iterations between scale updates of the proposal. |
init_scale |
initial scale of the proposal. |
burn_in |
number of initial samples to be discarded. |
seed |
seed for reproducibility. |
The parameter base_forecast is a list containing n = n_upper + n_bottom elements.
Each element is a list containing the estimated:
mean and sd for the Gaussian base forecast, see Normal, if distr='gaussian';
lambda for the Poisson base forecast, see Poisson, if distr='poisson';
size and prob (or mu) for the negative binomial base forecast, see NegBinomial, if distr='nbinom'.
The first n_upper elements of the list are the upper base forecasts, in the order given by the rows of A. The elements from n_upper+1 until the end of the list are the bottom base forecasts, in the order given by the columns of A.
A list containing the reconciled forecasts. The list has the following named elements:
bottom_reconciled_samples: a matrix (n_bottom x num_samples) containing reconciled samples for the bottom time series;
upper_reconciled_samples: a matrix (n_upper x num_samples) containing reconciled samples for the upper time series;
reconciled_samples: a matrix (n x num_samples) containing the reconciled samples for all time series.
Corani, G., Azzimonti, D., Rubattu, N. (2024). Probabilistic reconciliation of count time series. International Journal of Forecasting 40 (2), 457-469. doi:10.1016/j.ijforecast.2023.04.003.
library(bayesRecon) # Create a minimal hierarchy with 2 bottom and 1 upper variable rec_mat <- get_reconc_matrices(agg_levels=c(1,2), h=2) A <- rec_mat$A #Set the parameters of the Poisson base forecast distributions lambda1 <- 2 lambda2 <- 4 lambdaY <- 9 lambdas <- c(lambdaY,lambda1,lambda2) base_forecasts = list() for (i in 1:length(lambdas)) { base_forecasts[[i]] = list(lambda = lambdas[i]) } #Sample from the reconciled forecast distribution using MCMC mcmc = reconc_MCMC(A, base_forecasts, distr = "poisson", num_samples = 30000, seed = 42) samples_mcmc <- mcmc$reconciled_samples #Compare the reconciled means with those obtained via BUIS buis = reconc_BUIS(A, base_forecasts, in_type="params", distr="poisson", num_samples=100000, seed=42) samples_buis <- buis$reconciled_samples print(rowMeans(samples_mcmc)) print(rowMeans(samples_buis))library(bayesRecon) # Create a minimal hierarchy with 2 bottom and 1 upper variable rec_mat <- get_reconc_matrices(agg_levels=c(1,2), h=2) A <- rec_mat$A #Set the parameters of the Poisson base forecast distributions lambda1 <- 2 lambda2 <- 4 lambdaY <- 9 lambdas <- c(lambdaY,lambda1,lambda2) base_forecasts = list() for (i in 1:length(lambdas)) { base_forecasts[[i]] = list(lambda = lambdas[i]) } #Sample from the reconciled forecast distribution using MCMC mcmc = reconc_MCMC(A, base_forecasts, distr = "poisson", num_samples = 30000, seed = 42) samples_mcmc <- mcmc$reconciled_samples #Compare the reconciled means with those obtained via BUIS buis = reconc_BUIS(A, base_forecasts, in_type="params", distr="poisson", num_samples=100000, seed=42) samples_buis <- buis$reconciled_samples print(rowMeans(samples_mcmc)) print(rowMeans(samples_buis))
Uses importance sampling to draw samples from the reconciled forecast distribution, obtained via conditioning, in the case of a mixed hierarchy.
reconc_MixCond( A, fc_bottom, fc_upper, bottom_in_type = "pmf", distr = NULL, num_samples = 20000, return_type = "pmf", suppress_warnings = FALSE, seed = NULL )reconc_MixCond( A, fc_bottom, fc_upper, bottom_in_type = "pmf", distr = NULL, num_samples = 20000, return_type = "pmf", suppress_warnings = FALSE, seed = NULL )
A |
Aggregation matrix (n_upper x n_bottom). |
fc_bottom |
A list containing the bottom base forecasts, see details. |
fc_upper |
A list containing the upper base forecasts, see details. |
bottom_in_type |
A string with three possible values:
|
distr |
A string describing the type of bottom base forecasts ('poisson' or 'nbinom'). This is only used if |
num_samples |
Number of samples drawn from the reconciled distribution.
This is ignored if |
return_type |
The return type of the reconciled distributions. A string with three possible values:
|
suppress_warnings |
Logical. If |
seed |
Seed for reproducibility. |
The base bottom forecasts fc_bottom must be a list of length n_bottom, where each element is either
a PMF object (see details below), if bottom_in_type='pmf';
a vector of samples, if bottom_in_type='samples';
a list of parameters, if bottom_in_type='params':
lambda for the Poisson base forecast if distr='poisson', see Poisson;
size and prob (or mu) for the negative binomial base forecast if distr='nbinom',
see NegBinomial.
The base upper forecasts fc_upper must be a list containing the parameters of
the multivariate Gaussian distribution of the upper forecasts.
The list must contain only the named elements mu (vector of length n_upper)
and Sigma (n_upper x n_upper matrix).
The order of the upper and bottom base forecasts must match the order of (respectively) the rows and the columns of A.
A PMF object is a numerical vector containing the probability mass function of a discrete distribution. Each element corresponds to the probability of the integers from 0 to the last value of the support. See also PMF.get_mean, PMF.get_var, PMF.sample, PMF.get_quantile, PMF.summary for functions that handle PMF objects.
Warnings are triggered from the Importance Sampling step if:
weights are all zeros, then the upper forecast is ignored during reconciliation;
the effective sample size is < 200;
the effective sample size is < 1% of the sample size.
Note that warnings are an indication that the base forecasts might have issues. Please check the base forecasts in case of warnings.
A list containing the reconciled forecasts. The list has the following named elements:
bottom_reconciled: a list containing the pmf, the samples (matrix n_bottom x num_samples) or both,
depending on the value of return_type;
upper_reconciled: a list containing the pmf, the samples (matrix n_upper x num_samples) or both,
depending on the value of return_type.
Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). Probabilistic reconciliation of mixed-type hierarchical time series. The 40th Conference on Uncertainty in Artificial Intelligence, accepted.
reconc_TDcond(), reconc_BUIS()
library(bayesRecon) # Consider a simple hierarchy with two bottom and one upper A <- matrix(c(1,1),nrow=1) # The bottom forecasts are Poisson with lambda=15 lambda <- 15 n_tot <- 60 fc_bottom <- list() fc_bottom[[1]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) fc_bottom[[2]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) # The upper forecast is a Normal with mean 40 and std 5 fc_upper<- list(mu=40, Sigma=matrix(5^2)) # We can reconcile with reconc_MixCond res.mixCond <- reconc_MixCond(A, fc_bottom, fc_upper) # Note that the bottom distributions are slightly shifted to the right PMF.summary(res.mixCond$bottom_reconciled$pmf[[1]]) PMF.summary(fc_bottom[[1]]) PMF.summary(res.mixCond$bottom_reconciled$pmf[[2]]) PMF.summary(fc_bottom[[2]]) # The upper distribution is slightly shifted to the left PMF.summary(res.mixCond$upper_reconciled$pmf[[1]]) PMF.get_var(res.mixCond$upper_reconciled$pmf[[1]])library(bayesRecon) # Consider a simple hierarchy with two bottom and one upper A <- matrix(c(1,1),nrow=1) # The bottom forecasts are Poisson with lambda=15 lambda <- 15 n_tot <- 60 fc_bottom <- list() fc_bottom[[1]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) fc_bottom[[2]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) # The upper forecast is a Normal with mean 40 and std 5 fc_upper<- list(mu=40, Sigma=matrix(5^2)) # We can reconcile with reconc_MixCond res.mixCond <- reconc_MixCond(A, fc_bottom, fc_upper) # Note that the bottom distributions are slightly shifted to the right PMF.summary(res.mixCond$bottom_reconciled$pmf[[1]]) PMF.summary(fc_bottom[[1]]) PMF.summary(res.mixCond$bottom_reconciled$pmf[[2]]) PMF.summary(fc_bottom[[2]]) # The upper distribution is slightly shifted to the left PMF.summary(res.mixCond$upper_reconciled$pmf[[1]]) PMF.get_var(res.mixCond$upper_reconciled$pmf[[1]])
Uses the top-down conditioning algorithm to draw samples from the reconciled forecast distribution. Reconciliation is performed in two steps: first, the upper base forecasts are reconciled via conditioning, using only the hierarchical constraints between the upper variables; then, the bottom distributions are updated via a probabilistic top-down procedure.
reconc_TDcond( A, fc_bottom, fc_upper, bottom_in_type = "pmf", distr = NULL, num_samples = 20000, return_type = "pmf", suppress_warnings = FALSE, seed = NULL )reconc_TDcond( A, fc_bottom, fc_upper, bottom_in_type = "pmf", distr = NULL, num_samples = 20000, return_type = "pmf", suppress_warnings = FALSE, seed = NULL )
A |
aggregation matrix (n_upper x n_bottom). |
fc_bottom |
A list containing the bottom base forecasts, see details. |
fc_upper |
A list containing the upper base forecasts, see details. |
bottom_in_type |
A string with three possible values:
|
distr |
A string describing the type of bottom base forecasts ('poisson' or 'nbinom'). This is only used if |
num_samples |
Number of samples drawn from the reconciled distribution.
This is ignored if |
return_type |
The return type of the reconciled distributions. A string with three possible values:
|
suppress_warnings |
Logical. If |
seed |
Seed for reproducibility. |
The base bottom forecasts fc_bottom must be a list of length n_bottom, where each element is either
a PMF object (see details below), if bottom_in_type='pmf';
a vector of samples, if bottom_in_type='samples';
a list of parameters, if bottom_in_type='params':
lambda for the Poisson base forecast if distr='poisson', see Poisson;
size and prob (or mu) for the negative binomial base forecast if distr='nbinom',
see NegBinomial.
The base upper forecasts fc_upper must be a list containing the parameters of
the multivariate Gaussian distribution of the upper forecasts.
The list must contain only the named elements mu (vector of length n_upper)
and Sigma (n_upper x n_upper matrix).
The order of the upper and bottom base forecasts must match the order of (respectively) the rows and the columns of A.
A PMF object is a numerical vector containing the probability mass function of a discrete distribution. Each element corresponds to the probability of the integers from 0 to the last value of the support. See also PMF.get_mean, PMF.get_var, PMF.sample, PMF.get_quantile, PMF.summary for functions that handle PMF objects.
If some of the reconciled upper samples lie outside the support of the bottom-up distribution, those samples are discarded and a warning is triggered. The warning reports the percentage of samples kept.
A list containing the reconciled forecasts. The list has the following named elements:
bottom_reconciled: a list containing the pmf, the samples (matrix n_bottom x num_samples) or both,
depending on the value of return_type;
upper_reconciled: a list containing the pmf, the samples (matrix n_upper x num_samples) or both,
depending on the value of return_type.
Zambon, L., Azzimonti, D., Rubattu, N., Corani, G. (2024). Probabilistic reconciliation of mixed-type hierarchical time series. The 40th Conference on Uncertainty in Artificial Intelligence, accepted.
reconc_MixCond(), reconc_BUIS()
library(bayesRecon) # Consider a simple hierarchy with two bottom and one upper A <- matrix(c(1,1),nrow=1) # The bottom forecasts are Poisson with lambda=15 lambda <- 15 n_tot <- 60 fc_bottom <- list() fc_bottom[[1]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) fc_bottom[[2]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) # The upper forecast is a Normal with mean 40 and std 5 fc_upper<- list(mu=40, Sigma=matrix(c(5^2))) # We can reconcile with reconc_TDcond res.TDcond <- reconc_TDcond(A, fc_bottom, fc_upper) # Note that the bottom distributions are shifted to the right PMF.summary(res.TDcond$bottom_reconciled$pmf[[1]]) PMF.summary(fc_bottom[[1]]) PMF.summary(res.TDcond$bottom_reconciled$pmf[[2]]) PMF.summary(fc_bottom[[2]]) # The upper distribution remains similar PMF.summary(res.TDcond$upper_reconciled$pmf[[1]]) PMF.get_var(res.TDcond$upper_reconciled$pmf[[1]]) ## Example 2: reconciliation with unbalanced hierarchy # We consider the example in Fig. 9 of Zambon et al. (2024). # The hierarchy has 5 bottoms and 3 uppers A <- matrix(c(1,1,1,1,1, 1,1,0,0,0, 0,0,1,1,0),nrow=3,byrow = TRUE) # Note that the 5th bottom only appears in the highest level, this is an unbalanced hierarchy. n_upper = nrow(A) n_bottom = ncol(A) # The bottom forecasts are Poisson with lambda=15 lambda <- 15 n_tot <- 60 fc_bottom <- list() for(i in seq(n_bottom)){ fc_bottom[[i]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) } # The upper forecasts are a multivariate Gaussian mu = c(75, 30, 30) Sigma = matrix(c(5^2,5,5, 5, 10, 0, 5, 0,10), nrow=3, byrow = TRUE) fc_upper<- list(mu=mu, Sigma=Sigma) ## Not run: # If we reconcile with reconc_TDcond it won't work res.TDcond <- reconc_TDcond(A, fc_bottom, fc_upper) ## End(Not run) # We can balance the hierarchy with by duplicating the node b5 # In practice this means: # i) consider the time series observations for b5 as the upper u4, # ii) fit the multivariate ts model for u1, u2, u3, u4. # In this example we simply assume that the forecast for u1-u4 is # Gaussian with the mean and variance of u4 given by the parameters in b5. mean_b5 <- lambda var_b5 <- lambda mu = c(75, 30, 30,mean_b5) Sigma = matrix(c(5^2,5,5,5, 5, 10, 0, 0, 5, 0, 10, 0, 5, 0, 0, var_b5), nrow=4, byrow = TRUE) fc_upper<- list(mu=mu, Sigma=Sigma) # We also need to update the aggregation matrix A <- matrix(c(1,1,1,1,1, 1,1,0,0,0, 0,0,1,1,0, 0,0,0,0,1),nrow=4,byrow = TRUE) # We can now reconcile with TDcond res.TDcond <- reconc_TDcond(A, fc_bottom, fc_upper) # Note that the reconciled distribution of b5 and u4 are identical, # keep this in mind when using the results of your reconciliation! max(abs(res.TDcond$bottom_reconciled$pmf[[5]]- res.TDcond$upper_reconciled$pmf[[4]]))library(bayesRecon) # Consider a simple hierarchy with two bottom and one upper A <- matrix(c(1,1),nrow=1) # The bottom forecasts are Poisson with lambda=15 lambda <- 15 n_tot <- 60 fc_bottom <- list() fc_bottom[[1]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) fc_bottom[[2]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) # The upper forecast is a Normal with mean 40 and std 5 fc_upper<- list(mu=40, Sigma=matrix(c(5^2))) # We can reconcile with reconc_TDcond res.TDcond <- reconc_TDcond(A, fc_bottom, fc_upper) # Note that the bottom distributions are shifted to the right PMF.summary(res.TDcond$bottom_reconciled$pmf[[1]]) PMF.summary(fc_bottom[[1]]) PMF.summary(res.TDcond$bottom_reconciled$pmf[[2]]) PMF.summary(fc_bottom[[2]]) # The upper distribution remains similar PMF.summary(res.TDcond$upper_reconciled$pmf[[1]]) PMF.get_var(res.TDcond$upper_reconciled$pmf[[1]]) ## Example 2: reconciliation with unbalanced hierarchy # We consider the example in Fig. 9 of Zambon et al. (2024). # The hierarchy has 5 bottoms and 3 uppers A <- matrix(c(1,1,1,1,1, 1,1,0,0,0, 0,0,1,1,0),nrow=3,byrow = TRUE) # Note that the 5th bottom only appears in the highest level, this is an unbalanced hierarchy. n_upper = nrow(A) n_bottom = ncol(A) # The bottom forecasts are Poisson with lambda=15 lambda <- 15 n_tot <- 60 fc_bottom <- list() for(i in seq(n_bottom)){ fc_bottom[[i]] <- apply(matrix(seq(0,n_tot)),MARGIN=1,FUN=function(x) dpois(x,lambda=lambda)) } # The upper forecasts are a multivariate Gaussian mu = c(75, 30, 30) Sigma = matrix(c(5^2,5,5, 5, 10, 0, 5, 0,10), nrow=3, byrow = TRUE) fc_upper<- list(mu=mu, Sigma=Sigma) ## Not run: # If we reconcile with reconc_TDcond it won't work res.TDcond <- reconc_TDcond(A, fc_bottom, fc_upper) ## End(Not run) # We can balance the hierarchy with by duplicating the node b5 # In practice this means: # i) consider the time series observations for b5 as the upper u4, # ii) fit the multivariate ts model for u1, u2, u3, u4. # In this example we simply assume that the forecast for u1-u4 is # Gaussian with the mean and variance of u4 given by the parameters in b5. mean_b5 <- lambda var_b5 <- lambda mu = c(75, 30, 30,mean_b5) Sigma = matrix(c(5^2,5,5,5, 5, 10, 0, 0, 5, 0, 10, 0, 5, 0, 0, var_b5), nrow=4, byrow = TRUE) fc_upper<- list(mu=mu, Sigma=Sigma) # We also need to update the aggregation matrix A <- matrix(c(1,1,1,1,1, 1,1,0,0,0, 0,0,1,1,0, 0,0,0,0,1),nrow=4,byrow = TRUE) # We can now reconcile with TDcond res.TDcond <- reconc_TDcond(A, fc_bottom, fc_upper) # Note that the reconciled distribution of b5 and u4 are identical, # keep this in mind when using the results of your reconciliation! max(abs(res.TDcond$bottom_reconciled$pmf[[5]]- res.TDcond$upper_reconciled$pmf[[4]]))
Computes the Schäfer Strimmer shrinkage estimator for a covariance matrix from a matrix of samples.
schaferStrimmer_cov(x)schaferStrimmer_cov(x)
x |
matrix of samples with dimensions nxp (n samples, p dimensions). |
This function computes the shrinkage to a diagonal covariance with unequal variances.
Note that here we use the estimators and and we internally
use the correlation matrix in place of the covariance to compute the optimal shrinkage factor.
A list containing the shrinkage estimator and the optimal lambda. The list has the following named elements:
shrink_cov: the shrinked covariance matrix (p x p);
lambda_star: the optimal lambda for the shrinkage;
Schäfer, Juliane, and Korbinian Strimmer. (2005). A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics. Statistical Applications in Genetics and Molecular Biology 4: Article32. doi:10.2202/1544-6115.1175.
# Generate some multivariate normal samples # Parameters nSamples <- 200 pTrue <- 2 # True moments trueSigma <- matrix(c(3,2,2,2), nrow=2) chol_trueSigma <- chol(trueSigma) trueMean <- c(0,0) # Generate samples set.seed(42) x <- replicate(nSamples, trueMean) + t(chol_trueSigma)%*%matrix(stats::rnorm(pTrue*nSamples), nrow = pTrue, ncol = nSamples) x <- t(x) res_shrinkage <- schaferStrimmer_cov(x) res_shrinkage$lambda_star # should be 0.01287923# Generate some multivariate normal samples # Parameters nSamples <- 200 pTrue <- 2 # True moments trueSigma <- matrix(c(3,2,2,2), nrow=2) chol_trueSigma <- chol(trueSigma) trueMean <- c(0,0) # Generate samples set.seed(42) x <- replicate(nSamples, trueMean) + t(chol_trueSigma)%*%matrix(stats::rnorm(pTrue*nSamples), nrow = pTrue, ncol = nSamples) x <- t(x) res_shrinkage <- schaferStrimmer_cov(x) res_shrinkage$lambda_star # should be 0.01287923
Creates a list of aggregated time series from a time series of class ts.
temporal_aggregation(y, agg_levels = NULL)temporal_aggregation(y, agg_levels = NULL)
y |
univariate time series of class ts. |
agg_levels |
user-selected list of aggregation levels. |
If agg_levels=NULL then agg_levels is automatically generated by taking all the factors of the time series frequency.
A list of ts objects each containing the aggregates time series in the order defined by agg_levels.
# Create a monthly count time series with 100 observations y <- ts(data=stats::rpois(100,lambda = 2),frequency = 12) # Create the aggregate time series according to agg_levels y_agg <- temporal_aggregation(y,agg_levels = c(2,3,4,6,12)) # Show annual aggregate time series print(y_agg$`f=1`)# Create a monthly count time series with 100 observations y <- ts(data=stats::rpois(100,lambda = 2),frequency = 12) # Create the aggregate time series according to agg_levels y_agg <- temporal_aggregation(y,agg_levels = c(2,3,4,6,12)) # Show annual aggregate time series print(y_agg$`f=1`)