Search Results for author: Yanfei Kang

Found 19 papers, 11 papers with code

Probabilistic Forecast Reconciliation with Kullback-Leibler Divergence Regularization

1 code implementation21 Nov 2023 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.

Time Series

Infinite forecast combinations based on Dirichlet process

no code implementations21 Nov 2023 Yinuo Ren, Feng Li, Yanfei Kang, Jue Wang

Forecast combination integrates information from various sources by consolidating multiple forecast results from the target time series.

Time Series

Forecasting large collections of time series: feature-based methods

1 code implementation25 Sep 2023 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.

Model Selection Time Series

Forecast combinations: an over 50-year review

no code implementations9 May 2022 Xiaoqian Wang, Rob J Hyndman, Feng Li, Yanfei Kang

Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of the mainstream of forecasting research and activities.

Optimal reconciliation with immutable forecasts

1 code implementation20 Apr 2022 Bohan Zhang, Yanfei Kang, Anastasios Panagiotelis, Feng Li

In this paper, we formulate reconciliation methodology that keeps forecasts of a pre-specified subset of variables unchanged or "immutable".

Bayesian forecast combination using time-varying features

no code implementations4 Aug 2021 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 Bayesian Forecasting Model Averaging (FEBAMA).

Time Series Time Series Analysis +1

A hybrid ensemble method with negative correlation learning for regression

1 code implementation6 Apr 2021 Yun Bai, Ganglin Tian, Yanfei Kang, Suling Jia

It is also possible to improve the NCL ensemble with a regularization term in the objective function.


Exploring the representativeness of the M5 competition data

no code implementations4 Mar 2021 Evangelos Theodorou, Shengjie Wang, Yanfei Kang, Evangelos Spiliotis, Spyros Makridakis, Vassilios Assimakopoulos

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 in the field in order to identify best practices and highlight their practical implications.

Marketing Time Series +1

Exploring the social influence of Kaggle virtual community on the M5 competition

no code implementations28 Feb 2021 Xixi Li, Yun Bai, Yanfei Kang

This paper aims to study the social influence of virtual community on user behaviors in the M5 competition.

Forecast with Forecasts: Diversity Matters

no code implementations3 Dec 2020 Yanfei Kang, Wei Cao, Fotios Petropoulos, Feng Li

In this work, we suggest a change of focus from the historical data to the produced forecasts to extract features.

Computational Efficiency Meta-Learning +3

Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation

no code implementations6 Aug 2020 Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu, Yanfei Kang, Christoph Bergmeir

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 real-world applications, outperforming many state-of-the-art univariate forecasting techniques.

Data Augmentation Dynamic Time Warping +3

Distributed ARIMA Models for Ultra-long Time Series

1 code implementation19 Jul 2020 Xiaoqian Wang, Yanfei Kang, Rob J. Hyndman, Feng Li

Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management.

Applications Computation

Déjà vu: forecasting with similarity

2 code implementations31 Aug 2019 Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li, Vassilios Assimakopoulos

Instead of extrapolating, the future paths of the similar reference series are aggregated and serve as the basis for the forecasts of the target series.

Methodology Applications

FFORMPP: Feature-based forecast model performance prediction

1 code implementation30 Aug 2019 Thiyanga S. Talagala, Feng Li, Yanfei Kang

We model the forecast error as a function of time series features calculated from the historical time series with an efficient Bayesian multivariate surface regression approach.


Que será será? The uncertainty estimation of feature-based time series forecasts

2 code implementations8 Aug 2019 Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos, Feng Li

In the training part, we use a collection of time series to train a model to explore how time series features affect the interval forecasting accuracy of different forecasting methods, which makes our proposed framework interpretable in terms of the contribution of each feature to the models' uncertainty prediction.

Methodology Applications Computation

Probabilistic Forecasting with Temporal Convolutional Neural Network

4 code implementations11 Jun 2019 Yitian Chen, Yanfei Kang, Yixiong Chen, Zizhuo Wang

We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting.

Multivariate Time Series Forecasting Probabilistic Time Series Forecasting +2

Forecasting with time series imaging

3 code implementations17 Apr 2019 Xixi Li, Yanfei Kang, Feng Li

Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community.

Model Selection Time Series +1

GRATIS: GeneRAting TIme Series with diverse and controllable characteristics

5 code implementations7 Mar 2019 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.

Benchmarking Clustering +4

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