Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
Deep learning models for time series forecasting: a review
W Li, KLE Law - IEEE Access, 2024 - ieeexplore.ieee.org
Time series forecasting involves justifying assertions scientifically regarding potential states
or predicting future trends of an event based on historical data recorded at various time …
or predicting future trends of an event based on historical data recorded at various time …
Multivariate temporal convolutional network: A deep neural networks approach for multivariate time series forecasting
R Wan, S Mei, J Wang, M Liu, F Yang - Electronics, 2019 - mdpi.com
Multivariable time series prediction has been widely studied in power energy, aerology,
meteorology, finance, transportation, etc. Traditional modeling methods have complex …
meteorology, finance, transportation, etc. Traditional modeling methods have complex …
Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods
Z Yang, L Ce, L Lian - Applied Energy, 2017 - Elsevier
Electricity prices have rather complex features such as high volatility, high frequency,
nonlinearity, mean reversion and non-stationarity that make forecasting very difficult …
nonlinearity, mean reversion and non-stationarity that make forecasting very difficult …
Perspectives of photovoltaic energy market development in the european union
P Bórawski, L Holden, A Bełdycka-Bórawska - Energy, 2023 - Elsevier
Photovoltaic (PV) energy has recently been gaining much attention worldwide. It is the least
expensive energy source which can be used to replace part of the energy from fossil fuels …
expensive energy source which can be used to replace part of the energy from fossil fuels …
Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks
Y Wei, MC Chen - Transportation Research Part C: Emerging …, 2012 - Elsevier
Short-term passenger flow forecasting is a vital component of transportation systems. The
forecasting results can be applied to support transportation system management such as …
forecasting results can be applied to support transportation system management such as …
Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks
Reliable and accurate short-term subway passenger flow prediction is important for
passengers, transit operators, and public agencies. Traditional studies focus on regular …
passengers, transit operators, and public agencies. Traditional studies focus on regular …
PSO-based analysis of Echo State Network parameters for time series forecasting
Abstract Echo State Networks, ESNs, are standardly composed of additive units undergoing
sigmoid function activation. They consist of a randomly recurrent neuronal infra-structure …
sigmoid function activation. They consist of a randomly recurrent neuronal infra-structure …
Development of wind energy market in the European Union
P Bórawski, A Bełdycka-Bórawska, KJ Jankowski… - Renewable Energy, 2020 - Elsevier
Renewable energy sources (RES) can play a significant role in economic growth. This
article examines the development of the wind energy market in the EU. The applicable …
article examines the development of the wind energy market in the EU. The applicable …
An investigation of complex fuzzy sets for large-scale learning
Complex fuzzy sets are an extension of type-1 fuzzy sets with complex-valued membership
functions. Over the last 20 years, time-series forecasting has emerged as the most important …
functions. Over the last 20 years, time-series forecasting has emerged as the most important …