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A novel extreme adaptive GRU for multivariate time series forecasting
Nature
Multivariate time series forecasting is a critical problem in many real-world scenarios. Recent advances in deep learning have significantly...
7 months ago
Application of hybrid fuzzy interval-based machine learning models on financial time series — A case study of Taiwan biotech index during the epidemic period
Frontiers
This study employs 4 machine learning models, namely BPN, LSTM, RF, and ELM, to establish predictive models for the Taiwan biotech index during the COVID-19...
2 months ago
New survey on deep learning solutions for cellular traffic prediction
Tech Xplore
The bustling streets of a modern city are filled with countless individuals using their smartphones for streaming videos, sending messages...
5 months ago
Quickfire round with… Robert DeWitt
Best Execution
We take a deep dive into the increasingly important role of AI in algo execution with the head of quantitative strategies and data group for EMEA equities...
1 month ago
Predicting quantum emitter fluctuations with time-series forecasting models
Nature
In this work, we assess the random nature of the quantum fluctuations, and we present time series forecasting deep learning models to analyse and predict QE...
5 months ago
(PDF) Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability
ResearchGate
This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023.
4 months ago
Temporal Graph Learning in 2024. Continue the journey for evolving… | by Shenyang(Andy) Huang
Towards Data Science
Many complex networks evolve over time including transaction networks, traffic networks, social networks and more. Temporal Graph Learning...
7 months ago
TripleConvTransformer: A deep learning vessel trajectory prediction method fusing discretized meteorological data
Frontiers
The shipping industry is increasingly threatened by global climate change. Reliable trajectory prediction can be used to perceive potential risks and ensure...
2 months ago
GREEN.DAT.AI: Enabling energy-efficient AI services
Innovation News Network
GREEN.DAT.AI is accelerating the green energy transition through the development of energy-efficient AI services and data spaces.
9 months ago
Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach
The Lancet
Here we present a novel multi-stage deep learning model to forecast the number of COVID-19 cases and deaths for each US state at a weekly level for a forecast...
18 months ago