99 citations · 408 across the 26 of their papers we have counts for
29 papers
Efficient conformal prediction intervals for time series: Online PID-Expert aggregation
Guodong Liu, Yanfei Kang, Ren Miao +1
For a given point forecaster, proportional-integral-derivative (PID) calibration configurations can attain similar overall coverage yet produce different interval widths. We introd…
Multi-objective probabilistic forecast combination for inventory demand
Shengjie Wang, Yanfei Kang, Evangelos Spiliotis +1
Probabilistic forecasts are essential for inventory management, where decisions depend on the full distribution of future demand. While probabilistic forecast combination is widely…
REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning
Weijia Li, Shun Hu, Yanfei Kang
Forecast reconciliation usually starts from a fixed measurement system and asks how forecasts should be projected onto a coherent space. We ask a different question: which addition…
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,…
Combining Probabilistic Forecasts of Intermittent Demand
Shengjie Wang, Yanfei Kang, Fotios Petropoulos
In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, wi…
Predict+Optimize Problem in Renewable Energy Scheduling
Christoph Bergmeir, Frits de Nijs, Evgenii Genov +25
Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are criti…
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…
A hybrid ensemble method with negative correlation learning for regression
Yun Bai, Ganglin Tian, Yanfei Kang +1
Hybrid ensemble, an essential branch of ensembles, has flourished in the regression field, with studies confirming diversity's importance. However, previous ensembles consider dive…
Exploring the representativeness of the M5 competition data
Evangelos Theodorou, Shengjie Wang, Yanfei Kang +3
The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods i…
Exploring the social influence of Kaggle virtual community on the M5 competition
Xixi Li, Yun Bai, Yanfei Kang
One of the most significant differences of M5 over previous forecasting competitions is that it was held on Kaggle, an online platform of data scientists and machine learning pract…
Improving forecasting by subsampling seasonal time series
Xixi Li, Fotios Petropoulos, Yanfei Kang
Time series forecasting plays an increasingly important role in modern business decisions. In today's data-rich environment, people often aim to choose the optimal forecasting mode…
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…
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu +2
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and…
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…
Déjà vu: A data-centric forecasting approach through time series cross-similarity
Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos +3
Accurate forecasts are vital for supporting the decisions of modern companies. Forecasters typically select the most appropriate statistical model for each time series. However, st…
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…