Results 41 to 50 of about 21,026,228 (310)
Predictive analytics beyond time series: Predicting series of events extracted from time series data
Wind Energy, 2022 Realizing carbon neutral energy generation creates the challenge of accurately predicting time‐series generation data for long‐term capacity planning and for short‐term operational decisions.
Sambeet Mishra +3 more
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On predictability of time series [PDF]
Physica A: Statistical Mechanics and its Applications, 2019 The method to estimate the predictability of human mobility was proposed in [C. Song \emph{et al.}, Science {\bf 327}, 1018 (2010)], which is extensively followed in exploring the predictability of disparate time series. However, the ambiguous description in the original paper leads to some misunderstandings, including the inconsistent logarithm bases ...
Xu, Paiheng +3 more
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Time series momentum: Is it there?
Journal of Financial Economics, 2018 Time series momentum (TSM) refers to the predictability of the past 12-month return on the next one-month return and is the focus of several recent influential studies. This paper shows that asset-by-asset time series regressions reveal little evidence of TSM, both in- and out-of-sample. While the t-statistic in a pooled regression appears large, it is
HUANG, Dashan +3 more
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Neural Network Ensembles for Time Series Prediction [PDF]
, 2007 Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predictive engine shifts from the historical auto-regression to modelling complex
Ruta, Dymitr +3 more
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A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data
Mathematics, 2023 In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by LR fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as ...
Gholamreza Hesamian +2 more
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CoRR, 2019 Multivariate time series is a very active topic in the research community and many machine learning tasks are being used in order to extract information from this type of data. However, in real-world problems data has missing values, which may difficult the application of machine learning techniques to extract information. In this paper we focus on the
Samuel Arcadinho, Paulo Mateus
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The 8th International Conference on Time Series and Forecasting, 2022 Este artículo forma parte de las actas del "The 8th International Conference on Time Series and Forecasting."
Florian Combes +2 more
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Fractional integration and cointegration in US financial time series data [PDF]
, 2011 This paper examines several US monthly financial time series data using fractional integration and cointegration techniques. The univariate analysis based on fractional integration aims to determine whether the series are I(1) (in which case markets ...
Gil-Alana, Luis A. +5 more
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Time Alignment Measurement for Time Series
Pattern Recognition, 2018 Abstract When a comparison between time series is required, measurement functions provide meaningful scores to characterize similarity between sequences. Quite often, time series appear warped in time, i.e, although they may exhibit amplitude and shape similarity, they appear dephased in time.
Duarte Folgado +5 more
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Time Series Compression Survey
ACM Computing Surveys, 2023 Smart objects are increasingly widespread and their ecosystem, also known as the Internet of Things (IoT), is relevant in many application scenarios. The huge amount of temporally annotated data produced by these smart devices demands efficient techniques for the transfer and storage of time series data.
Chiarot, Giacomo, Silvestri, Claudio
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