Results 21 to 30 of about 2,067,646 (378)
Learning skillful medium-range global weather forecasting
Science, 2023 Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy but does not directly use historical ...
Remi Lam +17 more
semanticscholar +1 more source
Accurate medium-range global weather forecasting with 3D neural networks
Nature, 2023 Three-dimensional deep neural networks can be trained to forecast global weather patterns, including extreme weather, with accuracy greater than or equal to that of the best numerical weather prediction models.
Kaifeng Bi +5 more
semanticscholar +1 more source
Long-term Forecasting with TiDE: Time-series Dense Encoder [PDF]
Trans. Mach. Learn. Res., 2023 Recent work has shown that simple linear models can outperform several Transformer based approaches in long term time-series forecasting. Motivated by this, we propose a Multi-layer Perceptron (MLP) based encoder-decoder model, Time-series Dense Encoder (
Abhimanyu Das +5 more
semanticscholar +1 more source
FuXi: a cascade machine learning forecasting system for 15-day global weather forecast [PDF]
npj Climate and Atmospheric Science, 2023 Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has led to state-of-the-art ML models that have superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)’s high ...
Lei Chen +6 more
semanticscholar +1 more source
Random Scenario Forecasts Versus Stochastic Forecasts [PDF]
International Statistical Review, 2004 Summary Probabilistic population forecasts are useful because they describe uncertainty in a quantitatively useful way. One approach (that we call LT) uses historical data to estimate stochastic models (e.g., a time series model) of vital rates, and then makes forecasts.
Tuljapurkar, Shripad +2 more
openaire +4 more sources
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
AAAI Conference on Artificial Intelligence, 2019 Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of transportation. However, it is very challenging since the traffic flows usually show high nonlinearities and complex patterns.
S. Guo +4 more
semanticscholar +1 more source
Forecasting Profitability [PDF]
SSRN Electronic Journal, 2013 We use newly-available Indian panel data to estimate how the returns to planting-stage investments vary by rainfall realizations. We show that the forecasts significantly affect farmer investment decisions and that these responses account for a substantial fraction of the inter-annual variability in planting-stage investments, that the skill of the ...
Mark Rosenzweig, Christopher R. Udry
openaire +3 more sources
Argoverse: 3D Tracking and Forecasting With Rich Maps [PDF]
Computer Vision and Pattern Recognition, 2019 We present Argoverse, a dataset designed to support autonomous vehicle perception tasks including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a fleet of autonomous vehicles in Pittsburgh and Miami as well as 3D ...
Ming-Fang Chang +10 more
semanticscholar +1 more source
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting [PDF]
International Conference on Information and Knowledge Management, 2022 Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods due to their state-of-the-art performance ...
Zezhi Shao +4 more
semanticscholar +1 more source
Time-series forecasting with deep learning: a survey [PDF]
Philosophical Transactions of the Royal Society A, 2020 Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-horizon time ...
Bryan Lim, Stefan Zohren
semanticscholar +1 more source