Seasonal time series forecasting by the Walsh-transformation based technique
Sign Up to like & getrecommendations! Published in 2020 at "Central European Journal of Operations Research"
DOI: 10.1007/s10100-019-00614-3
Abstract: It is relatively well known that Walsh–Fourier analysis is capable of approximating functions by decomposing them into simple values: 1 and − 1. This method and its valuable characteristics however, are seldom applied in time series… read more here.
Keywords: time; series forecasting; time series; transformation based ... See more keywords
Sequence in Hybridization of Statistical and Intelligent Models in Time Series Forecasting
Sign Up to like & getrecommendations! Published in 2020 at "Neural Processing Letters"
DOI: 10.1007/s11063-020-10294-9
Abstract: With the importance of forecasting with a high degree of accuracy, the increasing attention has been evolved to combine individual models, especially statistical and intelligent ones. The main aim of such that hybrid models is… read more here.
Keywords: time series; series forecasting; hybrid models; series ... See more keywords
Functional time series forecasting of distributions: A Koopman-Wasserstein approach
Sign Up to like & getrecommendations! Published in 2025 at "Behaviormetrika"
DOI: 10.1007/s41237-025-00278-1
Abstract: We present a novel method for forecasting the temporal evolution of probability distributions observed at discrete time points. Building on the Dynamic Probability Density Decomposition (DPDD), we incorporate distributional dynamics into Wasserstein geometry using a… read more here.
Keywords: time; series forecasting; functional time; time series ... See more keywords
Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory
Sign Up to like & getrecommendations! Published in 2021 at "Energy"
DOI: 10.1016/j.energy.2021.120455
Abstract: Abstract Due to lockdown measures taken by the UK government during the Coronavirus disease 2019 pandemic, the national electricity demand profile presented a notably different performance. The Coronavirus disease 2019 crisis has provided a unique… read more here.
Keywords: electricity demand; series forecasting; electricity; time series ... See more keywords
Bayesian median autoregression for robust time series forecasting
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Forecasting"
DOI: 10.1016/j.ijforecast.2020.11.002
Abstract: Abstract We develop a Bayesian median autoregressive (BayesMAR) model for time series forecasting. The proposed method utilizes time-varying quantile regression at the median, favorably inheriting the robustness of median regression in contrast to the widely… read more here.
Keywords: time; series forecasting; bayesian median; time series ... See more keywords
A non-parametric softmax for improving neural attention in time-series forecasting
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.10.084
Abstract: Abstract Neural attention has become a key component in many deep learning applications, ranging from machine translation to time series forecasting. While many variations of attention have been developed over recent years, all share a… read more here.
Keywords: series forecasting; time series; attention; series ... See more keywords
Financial time series forecasting model based on CEEMDAN and LSTM
Sign Up to like & getrecommendations! Published in 2019 at "Physica A: Statistical Mechanics and its Applications"
DOI: 10.1016/j.physa.2018.11.061
Abstract: Abstract In order to improve the accuracy of the stock market prices forecasting, two hybrid forecasting models are proposed in this paper which combine the two kinds of empirical mode decomposition (EMD) with the long… read more here.
Keywords: time; series forecasting; financial time; time series ... See more keywords
A novel LLM time series forecasting method based on integer-decimal decomposition
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-06581-x
Abstract: The use of traditional deep learning models for time series forecasting has demonstrated strong performance in specific domains, but their applicability remains limited due to their domain-specific nature, which restricts generalization. Inspired by advancements in… read more here.
Keywords: time; integer decimal; series forecasting; language ... See more keywords
Conditional noise generative adversarial networks with Siamese neural network for longer time series forecasting.
Sign Up to like & getrecommendations! Published in 2025 at "Scientific reports"
DOI: 10.1038/s41598-025-30874-w
Abstract: Generative adversarial networks have achieved strong results in computer vision, but their use in time series forecasting remains limited. This paper proposes a conditional noise generative adversarial network with a Siamese neural network as discriminator… read more here.
Keywords: adversarial networks; series forecasting; generative adversarial; network ... See more keywords
Forecasting interrupted time series
Sign Up to like & getrecommendations! Published in 2024 at "Journal of the Operational Research Society"
DOI: 10.1080/01605682.2024.2395315
Abstract: Abstract Forecasting interrupted time series data is a major challenge for forecasting teams, especially in light of events such as the COVID-19 pandemic. This paper investigates several strategies for dealing with interruptions in time series… read more here.
Keywords: time; series forecasting; forecasting interrupted; interrupted time ... See more keywords
Enhancing denoising probability neural network (EDPNet) for mitigating cumulative errors and complex pattern separation in time series forecasting
Sign Up to like & getrecommendations! Published in 2025 at "Measurement Science and Technology"
DOI: 10.1088/1361-6501/adfc8a
Abstract: Time series forecasting is crucial in network traffic management, weather prediction, and traffic scheduling. Recurrent neural networks have significantly progressed time series analysis by effectively utilizing temporal dependencies through autoregressive strategies. However, the inherent nature… read more here.
Keywords: time; series forecasting; enhancing denoising; network ... See more keywords