Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization by Xue-Bo Jin, Hong-Xing Wang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su, Jian-Lei Kong

“…However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. …”
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The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects by Li Minghui, Zhou Decheng, Zhang Shiqi

“…However, traditional TFP methods only focus on predicting time series in traffic data, and it is difficult for these methods to capture the interdependent relationship between the spatial distribution of traffic across a network and the temporal evolution of traffic conditions at each location. sequences. …”
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Quadratic Forms in Random Matrices with Applications in Spectrum Sensing by Daniel Gaetano Riviello, Giusi Alfano, Roberto Garello

“…Quadratic forms with random kernel matrices are ubiquitous in applications of multivariate statistics, ranging from signal processing to time series analysis, biomedical systems design, wireless communications performance analysis, and other fields. …”
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Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Naha... by Abazar Solgi, Vahid Nourani, Amir Pourhaghi

“…For this purpose, the original time series using wavelet theory decomposed to multiple subtime series. …”
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Green Economy Transition in Vietnam: Assessing Unemployment and Life Expectancy Dynamics by Jude Anne P. Salvador, Elisha Angel Anne G. Santiago, Ronaldo R. Cabauatan, Marie Antoinette L. Rosete

“…Covering the period from 1991 to 2023, the study utilizes a descriptive-quantitative approach with time-series data. It employs the Ordinary Least Squares (OLS) model in EViews to examine the connections between these variables and socioeconomic results. …”
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Data Transformation Technique to Improve the Outlier Detection Power of Grubbs’ Test for Data Expected to Follow Linear Relation by K. K. L. B. Adikaram, M. A. Hussein, M. Effenberger, T. Becker

“…However, ranking of data eliminates the actual sequence of a data series, which is an important factor for determining outliers in some cases (e.g., time series). Thus in such a data set, Grubbs test will not identify outliers correctly. …”
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Nonstationary Generalised Autoregressive Conditional Heteroskedasticity Modelling for Fitting Higher Order Moments of Financial Series within Moving Time Windows by Luke De Clerk, Sergey Savel’ev

“…When we assume a Gaussian conditional distribution, we fail to capture any empirical data when fitting the first three even moments of financial time series. We show instead that a mixture of normal distributions is needed to better capture the higher order moments of the data. …”
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Spatial change analysis, Analysis of Spatial patterns changes in rainy season over Iran by Maryam Sanaei, Gholamreza Barati, Alireza Shakiba

“…In addition, the results of Alexandersonchr('39')s statistics to identify mutations in the long-term series of the rainy season showed that the time series of 13 stations out of 108 stations studied experienced a sudden jump that these mutations are more in the southern stations in the country and in later years. …”
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Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning by Jianye Zhou, Xinyu Yang, Lin Zhang, Siyu Shao, Gangying Bian

“…The proposed method adopts 512 time series to conduct experiments on two main mechanical datasets of bearings and gears in the variable-speed gearbox, which verifies the effectiveness and versatility of the method. …”
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IMPACT OF CAPITAL FLIGHT AND EXCHANGE RATES ON DOMESTIC INVESTMENT IN NIGERIA by Muftau Adeniyi Ijaiya, Kolawole Alabi Babaita

“…In line with the objectives, autoregressive distributed lags (ARDL) model and the Pairwise Granger Causality Test were used to analyse the time series data collected from 1981 to 2016 from the Central Bank of Nigeria Statistics Bulletin. …”
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ARMA Prediction of SBAS Ephemeris and Clock Corrections for Low Earth Orbiting Satellites by Jeongrae Kim, Mingyu Kim

“…The SBAS correction is only available within its service area, and the prediction of the SBAS corrections during the outage period can extend the coverage area. Two time series forecasting models, autoregressive moving average (ARMA) and autoregressive (AR), are proposed to predict the corrections outside the service area. …”
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Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction by Bo Wang, Liming Zhang, Hengrui Ma, Hongxia Wang, Shaohua Wan

“…Then, based on the long short-term memory depth neural network time series prediction, parallel long short-term memory multitask learning model is established to achieve horizontal interaction among multienergy systems and based on user-driven behavioral data to achieve vertical interaction between source and load. …”
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GOVERNMENT EXPENDITURE AND CONSUMPTION IN NIGERIA (1990-2022): AN ARDL APPROACH by EMMANUEL O. AWE, NWIE ABWO SIMON, MOHAMMED NASIRU, UMAR JOHNSON AKOGWU

“…On this note the study employs a combination of quantitative methods, including time series data analysis and econometric modeling via an Autoregressive Distributed Lag Model(ARDL) approach to investigate the impact of government expenditure on consumption in Nigeria. …”
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GOVERNMENT HEALTH EXPENDITURE AND EDUCATIONAL OUTCOME IN NIGERIA: INTERROGATING THE NEXUS by Aishat Princess Umar, Mathew Rotimi Ekundayo, Naphtali W. John

“…To achieve this objective the study collected time series data on the relevant variables namely: government health expenditure, school enrolment ratio which was used as the proxy for educational outcome, infant mortality which chosen because it received the direct effect of a change in government health expenditure and the Gross Domestic Product of Nigeria. …”
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Wasserstein Non-Negative Matrix Factorization for Multi-Layered Graphs and its Application to Mobility Data by Hirotaka Kaji, Kazushi Ikeda

“…Another example of a multi-layered graph is the time series of mobility when periodicity is considered. …”
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Solar Modulation of AMS-02 Daily Proton and Helium Fluxes with Modified Force-field Approximation Models by Cheng-Rui Zhu, Mei-Juan Wang

“…By fitting to the daily proton and helium fluxes, we get the time series of solar modulation potential. We find good agreement of data and model predictions for both proton and helium with the same parameters in two modified force field approximation models. …”
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Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model by Jieqi Lei, Xuyuan Wang, Yiming Zhang, Lian Zhu, Lin Zhang

“…One of the important issues in epidemic prevention and control is the assessment of the prevention and control effectiveness. Changes in the time series of daily new confirmed cases can reflect the impact of policies in certain regions. …”
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Modeling the Curb Parking Price in Urban Center District of China Using TSM-RAM Approach by Yan Wan, Jibiao Zhou, Wenqiang He, Changxi Ma

“…The cities were divided into three categories: rich cities (RCs), poor cities (PCs), and tourist cities (TCs). Both the time series method (TSM) and regression analysis method (RAM) were developed to simultaneously examine the factors associated with the CPP among parking users. …”
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Specialized Bacteroidetes dominate the Arctic Ocean during marine spring blooms by Álvaro Redondo-Río, Christopher J. Mundy, Javier Tamames, Carlos Pedrós-Alió

“…A metagenomic time series from Arctic seawater was obtained from Dease Strait, to analyse the changes in bacterioplankton caused by the summer phytoplankton bloom. …”
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Transitions in intensive care: Investigating critical slowing down post extubation. by Lucinda Khalil, Sandip V George, Katherine L Brown, Samiran Ray, Simon Arridge

“…We tested for significant increases (p <.05) between extubation and re-intubation, in the variance and autocorrelation, over the time series data of heart rate, respiratory rate and mean blood pressure. …”
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