AIMC Topic: Forecasting

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Deep learning via LSTM models for COVID-19 infection forecasting in India.

PloS one
The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lac...

LSTM in Algorithmic Investment Strategies on BTC and S&P500 Index.

Sensors (Basel, Switzerland)
We use LSTM networks to forecast the value of the BTC and S&P500 index, using data from 2013 to the end of 2020, with the following frequencies: daily, 1 h, and 15 min data. We introduce our innovative loss function, which improves the usefulness of ...

A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy.

Computational intelligence and neuroscience
Gross domestic product (GDP) is an important indicator for determining a country's or region's economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehen...

A Deep Learning Framework About Traffic Flow Forecasting for Urban Traffic Emission Monitoring System.

Frontiers in public health
As urban traffic pollution continues to increase, there is an urgent need to build traffic emission monitoring and forecasting system for the urban traffic construction. The traffic emission monitoring and forecasting system's core is the prediction ...

[Technical innovations and future perspectives].

Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen
BACKGROUND: Digital systems have increasingly become integrated into the modern operating room in the last few decades. This has brought about a massive change, especially in minimally invasive surgery.

Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study.

International journal of environmental research and public health
Continuity of care (COC) has been shown to possess numerous health benefits for chronic diseases. Specifically, the establishment of its level can facilitate clinical decision-making and enhanced allocation of healthcare resources. However, the use o...

Spatial-Temporal Convolutional Transformer Network for Multivariate Time Series Forecasting.

Sensors (Basel, Switzerland)
Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods ...

Machine learning prediction model of acute kidney injury after percutaneous coronary intervention.

Scientific reports
Acute kidney injury (AKI) after percutaneous coronary intervention (PCI) is associated with a significant risk of morbidity and mortality. The traditional risk model provided by the National Cardiovascular Data Registry (NCDR) is useful for predictin...

Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.

PloS one
OBJECTIVES: This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China.

Novel Model Based on Artificial Neural Networks to Predict Short-Term Temperature Evolution in Museum Environment.

Sensors (Basel, Switzerland)
The environmental microclimatic characteristics are often subject to fluctuations of considerable importance, which can cause irreparable damage to art works. We explored the applicability of Artificial Intelligence (AI) techniques to the Cultural He...