Article
Computer Science, Artificial Intelligence
Fabio Giampaolo, Federico Gatta, Edoardo Prezioso, Salvatore Cuomo, Mengchu Zhou, Giancarlo Fortino, Francesco Piccialli
Summary: This study proposes a novel ensemble approach for generating predictions in a multivariate framework. It reduces data dimensionality through an encoding technique, extracts useful information via single predictive procedures, and combines the processed data to produce the final forecast. Extensive experiments demonstrate the higher accuracy and robustness of the proposed ensemble compared to conventional methods and state-of-the-art strategies.
INFORMATION FUSION
(2023)
Article
Chemistry, Multidisciplinary
Emmanuel Gbenga Dada, David Opeoluwa Oyewola, Stephen Bassi Joseph, Onyeka Emebo, Olugbenga Oluseun Oluwagbemi
Summary: The monkeypox outbreak has become a global public health emergency, and effective measures to treat and control the disease are still poorly understood. This research aims to predict the transmission rate of monkeypox using historical data and employs stacking ensemble learning and machine learning techniques. Experimental results demonstrate that the proposed stacking ensemble learning outperforms other machine learning approaches in terms of predictive performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Vilde Jensen, Filippo Maria Bianchi, Stian Normann Anfinsen
Summary: This article presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR), which constructs distribution-free and approximately marginally valid prediction intervals (PIs) suitable for nonstationary and heteroscedastic time series data. By utilizing bootstrap ensemble estimator and generic machine learning algorithms, EnCQR outperforms models based only on quantile regression (QR) or conformal prediction (CP), delivering sharper, more informative, and valid PIs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Amal Saadallah, Matthias Jakobs, Katharina Morik
Summary: The complexity and evolution of time series data make forecasting one of the most challenging tasks in machine learning. Combining a diverse set of forecasters in heterogeneous ensembles and using gradient-based saliency maps for online ensemble pruning can lead to excellent results in prediction tasks. Empirical studies show that this method outperforms state-of-the-art approaches and baselines.
Article
Computer Science, Artificial Intelligence
Jianping Li, Jun Hao, QianQian Feng, Xiaolei Sun, Mingxi Liu
Summary: This paper proposes a heterogeneous ensemble forecasting model with multi-objective programming for nonlinear time series, and validates it using the Baltic Dry Index's time series data. Experimental results demonstrate the model's superior robustness in conducting out-of-sample predictions under different lead times.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Fatemeh Nazarieh, Mohammad Naderi Dehkordi
Summary: This research develops a novel decomposition ensemble-based network named VMD-DENetwork for time series forecasting over different horizons. A robust decomposition technique called variational mode decomposition (VMD) is applied to decompose the input sequence into several intrinsic modes. The proposed DENetwork combines multiple learners to model the nonlinear and complex relationships, and a firefly optimization algorithm is adopted to enhance the efficiency of VMD-DENetwork. The experimental results confirm the outstanding prediction performance of the proposed model.
Article
Computer Science, Information Systems
Shuai Zhang, Yong Chen, Wenyu Zhang, Ruijun Feng
Summary: In this study, a novel ensemble deep learning model is proposed for accurate and stable time series forecasting by generating various basic predictors and enhancing them through a new dynamic error correction method. The model combines basic predictors using a stacking-based ensemble method with kernel ridge regression as the meta-predictor, and an enhanced genetic algorithm is used for ensemble pruning to increase forecasting accuracy and stability. Experimental results showed the superiority of the proposed model in dealing with time series forecasting tasks.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Rakshitha Godahewa, Kasun Bandara, Geoffrey Webb, Slawek Smyl, Christoph Bergmeir
Summary: Ensembling techniques are used to improve the performance of Global Forecasting Models (GFM) and univariate models in heterogeneous datasets. A new clustered ensembles methodology is proposed to train multiple GFMs on different clusters of series, achieving higher accuracy than baseline models.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Sheng-Xiang Lv, Lu Peng, Huanling Hu, Lin Wang
Summary: This study develops a selective machine learning ensemble model (SMLE) that utilizes a novel soft selection algorithm to improve prediction accuracy. Experimental results show that the proposed model outperforms individual forecasts, advanced techniques, and ensemble strategies in terms of accuracy and reliability.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Evaldas Vaiciukynas, Paulius Danenas, Vilius Kontrimas, Rimantas Butleris
Summary: The study suggests that ensemble forecasting of time series using meta-learning to adaptively predict the diversity and size of the ensemble yields better results. By ranking different forecasting methods and selecting the best ones for ensemble formation, the proposed approach outperforms existing benchmarks with weighted pooling achieving the best overall performance.
Article
Mathematics, Applied
Waddah Saeed
Summary: This paper introduces the model used in the M4 forecasting competition, which combines several statistical methods and outperforms the benchmarks in terms of forecasting accuracy. The proposed model is also compared with other forecasting methods and shown to produce accurate results.
COMPUTATIONAL & APPLIED MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Borui Cai, Shuiqiao Yang, Longxiang Gao, Yong Xiang
Summary: This paper introduces a novel hybrid variational autoencoder (HyVAE) for forecasting time series by jointly learning the local patterns and temporal dynamics. Experimental results demonstrate that the proposed HyVAE achieves better results compared to other methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Tuanfei Zhu, Cheng Luo, Zhihong Zhang, Jing Li, Siqi Ren, Yifu Zeng
Summary: This paper introduces a structure-preserving Oversampling method for high-dimensional imbalanced time series classification, OHIT, and integrates it into boosting framework to form a new ensemble algorithm OHITBoost. Extensive experiments on several publicly available time-series datasets demonstrate their effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Review
Chemistry, Analytical
Nemesio Fava Sopelsa Neto, Stefano Frizzo Stefenon, Luiz Henrique Meyer, Raul Garcia Ovejero, Valderi Reis Quietinho Leithardt
Summary: This paper proposes a hybrid model for monitoring the electrical power grid by improving existing models using wavelet transform. The results show that using wavelet transform can significantly improve model performance, especially the wavelet ANFIS model.
Article
Mathematics
Yuyi Zhang, Ruimin Ma, Jing Liu, Xiuxiu Liu, Ovanes Petrosian, Kirill Krinkin
Summary: This study focuses on energy time series forecasting competitions, such as power generation and building energy consumption forecasts, using reliable sensor records and accurate exogenous variables. By introducing the Explainable AI method (SHAP), models' performance is explained to strengthen trust and transparency. Results show that the integrated model performs more stable and efficient, with LightGBM showing significant advantages. Through SHAP interpretation, lagging characteristics of building area and target variables are identified as important features.
