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Forecasting

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Uncertainty and sensitivity analysis of deep learning models for diurnal temperature range (DTR) forecasting over five Indian cities.

Environmental monitoring and assessment
In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used t...

State Causality and Adaptive Covariance Decomposition Based Time Series Forecasting.

Sensors (Basel, Switzerland)
Time series forecasting is a very vital research topic. The scale of time series in numerous industries has risen considerably in recent years as a result of the advancement of information technology. However, the existing algorithms pay little atten...

Predicting the monthly consumption and production of natural gas in the USA by using a new hybrid forecasting model based on two-layer decomposition.

Environmental science and pollution research international
As an efficient, economical, and clean energy, natural gas plays an important role in the development of the new energy revolution. Accurate prediction of natural gas consumption and production can adjust energy deployment in advance, which can ensur...

Urban Flooding Prediction Method Based on the Combination of LSTM Neural Network and Numerical Model.

International journal of environmental research and public health
At present, urban flood risk analysis and forecasting and early warning mainly use numerical models for simulation and analysis, which are more accurate and can reflect urban flood risk well. However, the calculation speed of numerical models is slow...

Hybrid attention-based temporal convolutional bidirectional LSTM approach for wind speed interval prediction.

Environmental science and pollution research international
Precise wind speed prediction is crucial for the management of the wind power generation systems. However, the stochastic nature of the wind speed makes optimal interval prediction very complicated. In this paper, a hybrid approach consisting of impr...

Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade.

EBioMedicine
BACKGROUND: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic patho...

A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand.

Sensors (Basel, Switzerland)
In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local s...

Advances in artificial intelligence to predict cancer immunotherapy efficacy.

Frontiers in immunology
Tumor immunotherapy, particularly the use of immune checkpoint inhibitors, has yielded impressive clinical benefits. Therefore, it is critical to accurately screen individuals for immunotherapy sensitivity and forecast its efficacy. With the applicat...

[Future perspectives in surgery from a German point of view].

Chirurgie (Heidelberg, Germany)
In this article two aspects of the topic of future perspectives in surgery from a German point of view are discussed: firstly, topics of healthcare policy, such as upcoming alterations of the healthcare system, including required minimum quantities w...