Publications (13)
Discrete forecast reconciliation
Bohan Zhang, Anastasios Panagiotelis, Yanfei Kang
This paper presents a formal framework and proposes algorithms to extend forecast reconciliation to discrete-valued data to extend forecast reconciliation to discrete-valued data,…
Forecast Linear Augmented Projection (FLAP): A free lunch to reduce forecast error variance
Yangzhuoran Fin Yang, George Athanasopoulos, Rob J. Hyndman +1
A novel forecast linear augmented projection (FLAP) method is introduced, which reduces the forecast error variance of any unbiased multivariate forecast without introducing bias.…
Estimating granular house price distributions in the Australian market using Gaussian mixtures
Willem P Sijp, Anastasios Panagiotelis
A new methodology is proposed to approximate the time-dependent house price distribution at a fine regional scale using Gaussian mixtures. The means, variances and weights of the m…
Forecast reconciliation with non-linear constraints
Daniele Girolimetto, Anastasios Panagiotelis, Tommaso Di Fonzo +1
Methods for forecasting time series adhering to linear constraints have seen notable development in recent years, especially with the advent of forecast reconciliation. This paper…
Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?
Bohan Zhang, Anastasios Panagiotelis, Han Li
Forecast reconciliation has attracted significant research interest in recent years, with most studies taking the hierarchy of time series as given. We extend existing work that us…
Vector Copula Variational Inference and Dependent Block Posterior Approximations
Yu Fu, Michael Stanley Smith, Anastasios Panagiotelis
The key to VI is the selection of a tractable density to approximate the Bayesian posterior. For large and complex models a common choice is to assume independence between multivar…
Updating Variational Bayes: Fast sequential posterior inference
Nathaniel Tomasetti, Catherine S. Forbes, Anastasios Panagiotelis
Variational Bayesian (VB) methods produce posterior inference in a time frame considerably smaller than traditional Markov Chain Monte Carlo approaches. Although the VB posterior i…
Bayesian Forecasting in Economics and Finance: A Modern Review
Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn +6
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem…
Forecasting: theory and practice
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos +77
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organ…
Model combinations through revised base-rates
Fotios Petropoulos, Evangelos Spiliotis, Anastasios Panagiotelis
Standard selection criteria for forecasting models focus on information that is calculated for each series independently, disregarding the general tendencies and performances of th…
Computationally Efficient Learning of Statistical Manifolds
Fan Cheng, Anastasios Panagiotelis, Rob J Hyndman
Analyzing high-dimensional data with manifold learning algorithms often requires searching for the nearest neighbors of all observations. This presents a computational bottleneck i…
Reconciliation of probabilistic forecasts with an application to wind power
Jooyoung Jeon, Anastasios Panagiotelis, Fotios Petropoulos
New methods are proposed for adjusting probabilistic forecasts to ensure coherence with the aggregation constraints inherent in temporal hierarchies. The different approaches neste…
Optimal reconciliation with immutable forecasts
Bohan Zhang, Yanfei Kang, Anastasios Panagiotelis +1
The practical importance of coherent forecasts in hierarchical forecasting has inspired many studies on forecast reconciliation. Under this approach, so-called base forecasts are p…