99 citations · 388 across the 21 of their papers we have counts for
21 papers
Recovery-Informed Forecasting Strategy Enhancement
Feng Li, Taozhu Ruan
We propose a three-stage framework named as Recovery-Informed Strategy Enhancement (RISE) to forecast the recovery of Chinese outbound tourism following the coronavirus disease 201…
Grid Point Approximation for Distributed Nonparametric Smoothing and Prediction
Yuan Gao, Rui Pan, Feng Li +2
Kernel smoothing is a widely used nonparametric method in modern statistical analysis. The problem of efficiently conducting kernel smoothing for a massive dataset on a distributed…
Optimal starting point for time series forecasting
Yiming Zhong, Yinuo Ren, Guangyao Cao +2
Recent advances on time series forecasting mainly focus on improving the forecasting models themselves. However, when the time series data suffer from potential structural breaks o…
Infinite forecast combinations based on Dirichlet process
Yinuo Ren, Feng Li, Yanfei Kang +1
Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series. Instead of the need to select a single opti…
Probabilistic Forecast Reconciliation with Kullback-Leibler Divergence Regularization
Guanyu Zhang, Feng Li, Yanfei Kang
As the popularity of hierarchical point forecast reconciliation methods increases, there is a growing interest in probabilistic forecast reconciliation. Many studies have utilized…
Forecasting large collections of time series: feature-based methods
Li Li, Feng Li, Yanfei Kang
In economics and many other forecasting domains, the real world problems are too complex for a single model that assumes a specific data generation process. The forecasting perform…
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,…
Another look at forecast trimming for combinations: robustness, accuracy and diversity
Xiaoqian Wang, Yanfei Kang, Feng Li
Forecast combination is widely recognized as a preferred strategy over forecast selection due to its ability to mitigate the uncertainty associated with identifying a single "best"…
Forecast combinations: an over 50-year review
Xiaoqian Wang, Rob J Hyndman, Feng Li +1
Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of the mainstream of forecasting research and activities. Combi…
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…
Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications
Li Li, Yanfei Kang, Fotios Petropoulos +1
Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on develo…
Bayesian forecast combination using time-varying features
Li Li, Yanfei Kang, Feng Li
In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time series features, which is called Feature-based Bayesi…
Hierarchical forecasting with a top-down alignment of independent level forecasts
Matthias Anderer, Feng Li
Hierarchical forecasting with intermittent time series is a challenge in both research and empirical studies. Extensive research focuses on improving the accuracy of each hierarchy…
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…
Forecast with Forecasts: Diversity Matters
Yanfei Kang, Wei Cao, Fotios Petropoulos +1
Forecast combinations have been widely applied in the last few decades to improve forecasting. Estimating optimal weights that can outperform simple averages is not always an easy…
Distributed ARIMA Models for Ultra-long Time Series
Xiaoqian Wang, Yanfei Kang, Rob J Hyndman +1
Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This…
FFORMPP: Feature-based forecast model performance prediction
Thiyanga S. Talagala, Feng Li, Yanfei Kang
This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We model the forecast error as a function of time series features calcu…
Forecasting with time series imaging
Xixi Li, Yanfei Kang, Feng Li
Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for foreca…
GRATIS: GeneRAting TIme Series with diverse and controllable characteristics
Yanfei Kang, Rob J Hyndman, Feng Li
The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evalu…
Improving forecasting performance using covariate-dependent copula models
Feng Li, Yanfei Kang
Copulas provide an attractive approach for constructing multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance…
Efficient Bayesian Multivariate Surface Regression
Feng Li, Mattias Villani
Methods for choosing a fixed set of knot locations in additive spline models are fairly well established in the statistical literature. While most of these methods are in principle…