AIMC Topic: Forecasting

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TCN-QV: an attention-based deep learning method for long sequence time-series forecasting of gold prices.

PloS one
Accurate prediction of gold prices is crucial for investment decision-making and national risk management. The time series data of gold prices exhibits random fluctuations, non-linear characteristics, and high volatility, making prediction extremely ...

SSSLN:Multivariate Time Series Forecasting via Collaborative Dynamic Graph Learning.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series (MTS) forecasting has achieved notable progress through graph modeling. However, existing approaches often face two key challenges. First, traditional dynamic graph learning (DGL) methods typically maintain dynamic graphs dir...

Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction.

Frontiers in public health
INTRODUCTION: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors. Machine learning offers powerful tools...

Multi-Granularity Autoformer for long-term deterministic and probabilistic power load forecasting.

Neural networks : the official journal of the International Neural Network Society
Long-term power load forecasting is critical for power system planning but is constrained by intricate temporal patterns. Transformer-based models emphasize modeling long- and short-term dependencies yet encounter limitations from complexity and para...

Deep learning modelling to forecast emergency department visits using calendar, meteorological, internet search data and stock market price.

Computer methods and programs in biomedicine
BACKGROUND: Accurate prediction of hospital emergency department (ED) patient visits and acuity levels have potential to improve resource allocation including manpower planning and hospital bed allocation. Internet search data have been used in medic...

Integrated codec decomposed Transformer for long-term series forecasting.

Neural networks : the official journal of the International Neural Network Society
Recently, Transformer-based and multilayer perceptron (MLP) based architectures have formed a competitive landscape in the field of time series forecasting. There is evidence that series decomposition can further enhance the model's ability to percei...

Integrating AI for infectious disease prediction: A hybrid ANN-XGBoost model for leishmaniasis in Pakistan.

Acta tropica
Addressing leishmaniasis infection remains a substantial challenge in KP-Pakistan due to the increased infection prevalence. Understanding its spreading tool offerings is a major challenge. We essentially design effective approaches to pinpoint its e...

FADE: Forecasting for anomaly detection on ECG.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multip...

Confidence interval forecasting model of small watershed flood based on compound recurrent neural networks and Bayesian.

PloS one
Flood forecasting exhibits rapid fluctuations, water level forecasting shows great uncertainty and inaccuracy in small watersheds, and the reliability and accuracy performance of traditional probability forecasting is often unbalanced. This study com...

Chronic Total Occlusion Percutaneous Coronary Intervention: Present and Future.

Circulation. Cardiovascular interventions
Chronic total occlusion percutaneous coronary intervention has evolved into a subspecialty of interventional cardiology. Using a variety of antegrade and retrograde techniques, experienced operators currently achieve success rates of 85% to 90%, with...