Energy Use and Demand Prediction Using Time-Series Deep Learning Forecasting Techniques: Application for a University Campus by Bivin Pradeep, Parag Kulkarni, Farman Ullah, Abderrahmane Lakas

“…The window size six performs better for most time-series algorithms and hyperparameter combinations.…”
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Analysis of the high order ADL(<i>p, q</i>) models used to describe connections between time series by T. R. Kalugin, A. K. Kim, D. A. Petrusevich

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Inferring 3D displacement time series through InSAR measurements and potential field theory in volcanic areas by Andrea Barone, Maurizio Fedi, Antonio Pepe, Pietro Mastro, Pietro Tizzani, Raffaele Castaldo

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Association Between Diurnal Temperature Range and Risk of Cardiomyopathy-Induced Hospitalisation in Henan, China: A Time-Series Study by Zhang Z, Wang Y, Zhou Y, Pei J, Zhao G, Dong P, Yang K, Liu H, Xie N, Li X, Zang X, Lin F, Chen Z

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Developments on a 22&thinsp;GHz microwave radiometer and reprocessing of 13-year time series for water vapour studies by A. Bell, A. Bell, E. Sauvageat, E. Sauvageat, E. Sauvageat, G. Stober, G. Stober, K. Hocke, K. Hocke, A. Murk, A. Murk

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Time series analysis of radiant heat using 75 hours VIIRS satellite day and night band nightfire data by Jyoti U. Devkota

“…The time series data is decomposed to study the effect of various components. …”
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Enhanced Adaptive Neural-Fuzzy Inference System for Dynamic Time Series Prediction Using Self-Feedback and Hybrid Training by Andrew Topper, Honglei Yao

“…Neural networks and fuzzy systems have emerged as leading methods for forecasting chaotic time series. This study introduces an improved adaptive neural-fuzzy inference system (ANFIS) specifically tailored for forecasting chaotic time series. …”
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Short-term effects of combined environmental factors on respiratory disease mortality in Qingdao city: A time-series investigation. by Xin Zhang, Zijian Xi, Min Yang, Xiuqin Zhang, Ruikai Wu, Shuang Li, Lu Pan, Yuan Fang, Peng Lv, Yan Ma, Haiping Duan, Bingling Wang, Kunzheng Lv

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How to Identify If Your Time Series Inputs Are Adequate for AI Applications: Assessing Minimum Data Requirements in Environmental Analyses by Eduart Murcia Botache, Sandra Guzman

“…It provides an overview of the factors that should be considered when employing ML applications with time series (TS) data as input. Written by Eduart Murcia and Sandra M. …”
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Impact of zero-markup consumable policy and national procurement of coronary stents on hospitalization expenses: an interrupted time series analysis by Shao-long Wu, Shao-long Wu, Kun Wang, Xin Yang, Ning Liu, Ning Liu, Wen-xin Zou, Xiao-wei Li, Dan-hua Huang, Tian-hao Yu

“…The analysis of interrupted time series revealed that the elimination of the consumable markup policy had a limited effect on the level and trend of total hospitalization expenses. …”
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Investigation of the Spatial Clustering Properties of Seismic Time Series: A Comparative Study from Shallow to Intermediate-Depth Earthquakes by Ke Ma, Long Guo, Wangheng Liu

“…With this modified DFA, seismic time series (m≥4.5) pertaining to most seismically active regions of the world from the year 1972 up to the year 2016 are comparatively analyzed. …”
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Daily time series of 12 human thermal stress indices in Greece, aggregated at commune level (1998–2022)Zenodo by Georgios Charvalis, Michalis Koureas, Chloe Brimicombe, Chara Bogogiannidou, Fani Kalala, Varbara Mouchtouri, Christos Hadjichristodoulou

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A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series by Thi-Thu-Hong Phan, André Bigand, Émilie Poisson Caillault

“…Therefore, this paper aims to introduce a new approach for filling successive missing values in low/uncorrelated multivariate time series which allows managing a high level of uncertainty. …”
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An Attention Encoder-Decoder Dual Graph Convolutional Network with Time Series Correlation for Multi-Step Traffic Flow Prediction by Shanchun Zhao, Xu Li

“…Due to the complex and nonlinear nature of traffic flow time series, traditional methods of prediction tasks tend to ignore the heterogeneity and long series dependencies of spatio-temporal data. …”
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Indirect impacts of the COVID-19 pandemic at two tertiary neonatal units in Zimbabwe and Malawi: an interrupted time series analysis by Michelle Heys, Caroline Crehan, Emma Wilson, Felicity Fitzgerald, Mario Cortina Borja, Msandeni Chiume, Samuel R Neal, Hannah Gannon, Gwen Chimhini, Simbarashe Chimhuya, Tim Hull-Bailey, Tarisai Chiyaka, Deliwe Nkhoma

“…Objectives To examine indirect impacts of the COVID-19 pandemic on neonatal care in low-income and middle-income countries.Design Interrupted time series analysis.Setting Two tertiary neonatal units in Harare, Zimbabwe and Lilongwe, Malawi.Participants We included a total of 6800 neonates who were admitted to either neonatal unit from 1 June 2019 to 25 September 2020 (Zimbabwe: 3450; Malawi: 3350). …”
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Did the COVID-19 quarantine policies applied in Cochabamba, Bolivia mitigated cases successfully? an interrupted time series analysis by Rodrigo K. Arce Cardozo, Osvaldo Fonseca-Rodríguez, Yercin Mamani Ortiz, Miguel San Sebastian, Frida Jonsson

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Hybrid GNSS time-series prediction method based on ensemble empirical mode decomposition with long short-term memory by Yu Zhou, Xiaoxing He, Shengdao Wang, Shunqiang Hu, Xiwen Sun, Jiahui Huang

Subjects: “…GNSS height time series…”
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Geometric Brownian Motion-Based Time Series Modeling Methodology for Statistical Autocorrelated Process Control: Logarithmic Return Model by Siaw Li Lee, Chin Ying Liew, Chee Khium Chen, Li Li Voon

“…Fitting a time series model to the process data before applying a control chart to the residuals is essential to fulfill the basic assumptions of statistical process control (SPC). …”
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Coastal water quality assessment based on the remotely sensed water quality index using time series of satellite images by Taraneh Mirgheidari, Behzad Rayegani, Javad Bodagh-Jamali

“…Finally, an effective index for assessing the quality of coastal waters was provided by time series of satellite images and the waters of Assaluyeh port were zoned. …”
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DFCNformer: A Transformer Framework for Non-Stationary Time-Series Forecasting Based on De-Stationary Fourier and Coefficient Network by Yuxin Jin, Yuhan Mao, Genlang Chen

Subjects: “…time-series prediction…”
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