Network self attention for forecasting time series.

Article Mathematics, Interdisciplinary Applications

Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis

Huizi Cui et al.

Summary: This paper introduces a method based on belief entropy to measure the complexity of physiological signals in biological systems. The method has better accuracy and applicability compared to existing entropy algorithms.

CHAOS SOLITONS & FRACTALS (2022)

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Article Mathematics, Interdisciplinary Applications

INFORMATION FRACTAL DIMENSION OF MASS FUNCTION

Chenhui Qiang et al.

Summary: Fractal dimension plays a crucial role in nonlinear science, and calculating the fractal dimension of mass function remains an important and unsolved problem. This study proposes an information fractal dimension for mass function and demonstrates its effectiveness through numerical examples. It also discovers a similarity between the dimension corresponding to the maximum Deng entropy of mass function and the fractal dimension of the Sierpinski triangle.

FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY (2022)

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Article Physics, Multidisciplinary

A novel method for forecasting time series based on directed visibility graph and improved random walk

Yuntong Hu et al.

Summary: This paper proposes a reconstructing-forecasting method based on directed visibility graph and random walk process to improve the accuracy of time series predictions.

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS (2022)

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Article Computer Science, Information Systems

A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting

Shuai Zhang et al.

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)

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Article Automation & Control Systems

Probability transformation of mass function: A weighted network method based on the ordered visibility graph

Luyuan Chen et al.

Summary: The proposed OVGWP is a weighted network method based on the ordered visibility graph, using belief entropy to calculate information volume and generate a corresponding network for transforming basic probability assignment to probability distribution. Experimental results show that OVGWP produces effective and reasonable transformation performance while satisfying the consistency of upper and lower boundaries.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)

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Article Computer Science, Information Systems

Conflicting evidence combination from the perspective of networks

Leihui Xiong et al.

Summary: This paper proposes a new evidence combination method based on the interaction among nodes in complex networks, which combines evidence efficiently using weighted average and Dempster's rule of combination, showing better performance through a numerical example.

INFORMATION SCIENCES (2021)

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Article Computer Science, Information Systems

FQTSFM: A fuzzy-quantum time series forecasting model

Pritpal Singh

Summary: The study proposed an improved quantum optimization algorithm to address the issues of universe of discourse selection and fuzzy degree determination in fuzzy time series models. By integrating this algorithm with the FTS modeling approach, a hybrid model called FQTSFM was developed, which shows fast convergence and accurate forecasting results.

INFORMATION SCIENCES (2021)

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Article Computer Science, Artificial Intelligence

A distance for belief functions of orderable set

Cuiping Cheng et al.

Summary: This paper introduces a method for measuring conflicts between ordered sets, which effectively quantifies the similarity between sets and provides a more robust and accurate characterization of the agreement of ordered sets.

PATTERN RECOGNITION LETTERS (2021)

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Article Neurosciences

Deep learning model of fMRI connectivity predicts PTSD symptom trajectories in recent trauma survivors

Shelly Sheynin et al.

Summary: This study introduces a novel neural network model that utilizes fMRI data to predict PTSD symptoms in individuals exposed to trauma. The results show that this method outperforms previous techniques in predicting PTSD symptoms and can also predict PTSD symptom clusters and persistence. This is the first deep learning approach applied to fMRI data for predicting clinical outcomes related to PTSD.

NEUROIMAGE (2021)

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Article Environmental Sciences

Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images

Francois Waldner et al.

Summary: DECODE is a method that automatically extracts accurate field boundary data from satellite imagery using deep learning, with high accuracy and transferability. The method demonstrated superior performance and scalability in the Australian grains zone.

REMOTE SENSING (2021)

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Article Computer Science, Information Systems

Forecasting Copper Electrorefining Cathode Rejection by Means of Recurrent Neural Networks With Attention Mechanism

Pedro Pablo Correa et al.

Summary: Electrolytic refining is the final step in copper smelting production, converting molten copper into high-quality cathodes through electrolysis. Accurately predicting cathodic rejection is crucial for economic production forecasting. Data-driven methods like deep learning provide a more precise estimation of cathodic rejection.

IEEE ACCESS (2021)

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Article Automation & Control Systems