Results 241 to 250 of about 3,518,669 (281)

SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters

International Conference on Machine Learning
This paper introduces SparseTSF, a novel, extremely lightweight model for Long-term Time Series Forecasting (LTSF), designed to address the challenges of modeling complex temporal dependencies over extended horizons with minimal computational resources ...
Shengsheng Lin, Weiwei Lin, Wentai Wu, Haojun Chen, Junjie Yang   +4 more
semanticscholar   +1 more source

Models for long-term energy forecasting

2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491), 2004
Based on historical data related to an actual power system, the paper develops and evaluates the accuracy of a range of mathematical models for long-term forecasting of energy demand in the system. Starting from the basic relationship in an econometric model based on regression analysis, the development and evaluation are extended to include advanced ...
C.W. Fu, T.T. Nguyen
openaire   +1 more source

Long‐term probabilistic forecasting of earthquakes

Journal of Geophysical Research: Solid Earth, 1994
We estimate long‐term worldwide earthquake probabilities by extrapolating catalogs of seismic moment solutions. We base the forecast on correlations of seismic moment tensor solutions. The forecast is expressed as a map showing predicted rate densities for earthquake occurrence and for focal mechanism orientation.
Y. Y. Kagan, D. D. Jackson
openaire   +1 more source

Future prices in long-term forecasting

Futures, 1969
Long-range planning systems have, in the past, been based mainly on “volume” changes, implying a constant price structure. But the impact of changes in relative prices on the structures of output are at least as important as the impact of changes in technology.
E. Fontela, G. McNeill
openaire   +1 more source

Unlocking the Power of LSTM for Long Term Time Series Forecasting

AAAI Conference on Artificial Intelligence
Traditional recurrent neural network architectures, such as long short-term memory neural networks (LSTM), have historically held a prominent role in time series forecasting (TSF) tasks. While the recently introduced sLSTM for Natural Language Processing
Yaxuan Kong, Zepu Wang, Yuqi Nie, Tian Zhou, Stefan Zohren, Yuxuan Liang, Peng Sun, Qingsong Wen   +7 more
semanticscholar   +1 more source

Long-Term Forecasting of World Economy

World Economy and International Relations, 2014
The paper considers contemporary approaches of long-term forecasting of world and national economics. Foreign practice is of especial interest of authors. Researches and results of PricewaterhouseCoopers and Goldman Sachs are exposed in details. Authors make the following conclusions: a production function model is used often to generate forecasts. The
Y. Lukashin, L. Rakhlina
openaire   +1 more source

Long-Term Energy and Peak Power Demand Forecasting Based on Sequential-XGBoost

IEEE Transactions on Power Systems
Long-term energy and peak power forecast are essential tasks for the effective planning of power systems. Utilities often conduct long-term energy consumption and peak power demand forecasting separately through different forecasting frameworks ...
Tingze Zhang, Xinan Zhang, Osaka Rubasinghe, Yulin Liu, Y. Chow, H. Iu, Tyrone Fernando   +6 more
semanticscholar   +1 more source

Broken symmetry and long‐term forecasting

Journal of Geophysical Research: Atmospheres, 2007
This paper takes a novel approach to a known basic difficulty with computer simulations of nonlinear dynamical systems relevant to climate modeling. Specifically, we show by minimal examples how small systematic modeling errors might survive averaging over an ensemble of initial conditions.
Christopher Essex, Silvana Ilie, Robert M. Corless   +2 more
openaire   +1 more source

TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting

International Conference on Machine Learning
Non-stationarity poses significant challenges for multivariate time series forecasting due to the inherent short-term fluctuations and long-term trends that can lead to spurious regressions or obscure essential long-term relationships.
Peiyuan Liu, Beiliang Wu, Yifan Hu, Naiqi Li, Tao Dai, Jigang Bao, Shu-Tao Xia   +6 more
semanticscholar   +1 more source

Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS

Water resources management, 2021
Hossien Riahi-Madvar, Majid Dehghani, R. Memarzadeh, Bahram Gharabaghi   +3 more
semanticscholar   +1 more source