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Time Series Forecasting for Energy Consumption
Energies, 2022 Introduction In the last few years, there has been considerable progress in time series forecasting algorithms, which are becoming more and more accurate, and their applications are numerous and varied [...]
M. C. Pegalajar, L. G. B. Ruiz
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A Framework for Imbalanced Time-Series Forecasting [PDF]
, 2022 Time-series forecasting plays an important role in many domains. Boosted by the advances in Deep Learning algorithms, it has for instance been used to predict wind power for eolic energy production, stock market fluctuations, or motor overheating. In some of these tasks, we are interested in predicting accurately some particular moments which often are
Luis P. Silvestrin +2 more
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FUZZY MODEL FOR TIME SERIES FORECASTING
Scientific Journal of Astana IT University, 2023 In 2007, in Kazakhstan, there was a transition of TDM (Time Division Multiplexing) circuit-switched technologies to IP (Internet Protocol) packet technology, which created a modern infrastructure for the ICT (information communication technologies ...
Zhanar Ibraeva +2 more
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Forecasting of GPU Prices Using Transformer Method
Jurnal Sisfokom, 2023 GPU or VGA (graphic processing unit) is a vital component of computers and laptops, used for tasks such as rendering videos, creating game environments, and compiling large amounts of code.
Risyad Faisal Hadi +2 more
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Woa-wtconv-kanformer for long term time series forecasting [PDF]
PLoS ONEMeng Ling Ming +3 more
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Financial time series forecasting methods [PDF]
ITM Web of ConferencesThe paper presents the development of time series forecasting algorithms based on the Integrated Autoregressive Moving Average Model (ARIMA) and the Fourier Expansion model.
Zinenko Anna, Stupina Alena
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A Time Series Forecasting Method
ITM Web of Conferences, 2017 This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally.
Wang Zhao-Yu +3 more
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Nyström Regularization for Time Series Forecasting
CoRR, 2021 This paper focuses on learning rate analysis of Nyström regularization with sequential sub-sampling for $τ$-mixing time series. Using a recently developed Banach-valued Bernstein inequality for $τ$-mixing sequences and an integral operator approach based on second-order decomposition, we succeed in deriving almost optimal learning rates of Nyström ...
Zirui Sun +3 more
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Neural network ensemble operators for time series forecasting [PDF]
, 2014 The combination of forecasts resulting from an ensemble of neural networks has been shown to outperform the use of a single ``best'' network model.
Kourentzes, Nikos +2 more
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Forecasting Time Series with Boot.EXPOS Procedure
Revstat Statistical Journal, 2009 To forecast future values of a time series is one of the main goals in times series analysis. Many forecasting methods have been developed and its performance evaluated.
Clara Cordeiro , M. Manuela Neves
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