The Effect of Household versus Enterprise Credit on Economic Growth in Turkiye by Betül İsmiç, Sedef Şen

“…An annual time series data from 1986 to 2021 is applied for Turkiye. …”
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Revisiting the Question: The Cause of the Solar Cycle Variation of Total Solar Irradiance by N. B. Xiang

“…The coherence of two time series at these timescales should be due to a particular phase relation between sunspots and TSI. …”
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Edge-of-Chaos and Chaotic Dynamics in Resistor-Inductor-Diode-Based Reservoir Computing by A. H. Abbas, Hend Abdel-Ghani, Ivan S. Maksymov

“…We also evaluate the performance of RLD-RC tuned to operate in a chaotic regime under forced initial conditions, revealing the ability of the so-designed computer to accurately forecast complex time series and highlighting the potential of RLD circuits to serve as a backbone of efficient and versatile hardware RC systems.…”
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Trends in Extreme Precipitation Indices in Iran: 1951–2007 by Robert C. Balling, Mohammad Sadegh Keikhosravi Kiany, Shouraseni Sen Roy, Javad Khoshhal

“…We investigate trends in extreme precipitation in Iran for 1951–2007 using the recently released APHRODITE daily rainfall time series. We find that seven different indices of extreme precipitation all show an upward trend through the study period. …”
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Enhancing urban air quality prediction using time-based-spatial forecasting framework by Shrikar Jayaraman, Nathezhtha T, Abirami S, Sakthivel G

“…The integration of CNNs and time series model allowed for an clearer and deeper understanding of geographical and pollutant concentration factors that contribute to air quality variations.…”
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An underground view of surface hydrology: what can piezometers tell us about river floods and droughts? by Pelletier, Antoine, Andréassian, Vazken

“…We propose a simple correlation analysis between yearly extrema of groundwater level and streamflow time series, performed on a large set of 107 catchments and 355 piezometers, located throughout mainland France. …”
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Incorporation of Structural Health Monitoring Coupled Data in Dynamic Extreme Stress Prediction of Steel Bridges Using Dynamic Coupled Linear Models by Xueping Fan, Zhipeng Shang, Guanghong Yang, Xiaoxiong Zhao, Yuefei Liu

“…Firstly, the modeling processes about dynamic coupled linear models (DCLM) are provided based on a supposed coupled time series; furthermore, the dynamic probabilistic recursion processes about DCLM are given with Bayes method; secondly, the monitoring dynamic coupled extreme stress data is taken as a time series, historical monitoring coupled extreme stress data-based DCLM and the corresponding Bayesian probabilistic recursion processes are given for predicting bridge extreme stresses; furthermore, the monitoring mechanism is provided for monitoring the prediction precision of DCLM; finally, the monitoring coupled extreme stress data of a steel bridge is used to illustrate the proposed approach which can provide the foundations for bridge reliability prediction and assessment.…”
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Module Anchored Network Inference: A Sequential Module-Based Approach to Novel Gene Network Construction from Genomic Expression Data on Human Disease Mechanism by Annamalai Muthiah, Susanna R. Keller, Jae K. Lee

“…Using MANI, we inferred a 7-gene adipogenesis network based on time-series gene expression data during adipocyte differentiation. …”
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The dynamics of lowland river sections of Danube and Tisza in the Carpathian basin by Imre M. Jánosi, Imre M. Jánosi, István Zsuffa, István Zsuffa, Tibor Bíró, Boglárka O. Lakatos, Boglárka O. Lakatos, András Szöllősi-Nagy, Zsolt Hetesi, Zsolt Hetesi

“…The paper demonstrates that cubic spline fits and down-sampling (where necessary) produce reliable, evenly sampled time series that smoothly reconstruct water level and river discharge data. …”
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Assessing survey design changes of long-term fishery-independent groundfish trawl surveys in the Gulf of Mexico by Adam G. Pollack, Eric R. Hoffmayer, Theodore S. Switzer, David S. Hanisko, Jill M. Hendon, John Mareska, Fernando Martinez-Andrade, Jeff Rester, Zachary C. Zuckerman, Gilmore Pellegrin

“…However, maintaining an unchanged time series can pose several potential issues as management needs change resulting in the need to alter either the survey design or its spatial extent. …”
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Probabilistic Forecasting of Crude Oil Prices Using Conditional Generative Adversarial Network Model with Lévy Process by Mohammed Alruqimi, Luca Di Persio

“…Recent research highlights Generative Adversarial Networks (GANs) as a promising alternative approach for capturing intricate patterns in time series data, leveraging the adversarial learning framework. …”
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Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service by Liang Ye, Xintao Xia, Zhen Chang

“…In this paper, the time series of a vibration signal is used to characterize the state information for SPRB, and four runtime data points can be predicted in the future, which depends on four chaotic forecasting models to preprocess the time series. …”
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45 years of directional wave recorded data at the Acqua Alta oceanographic tower by Luigi Cavaleri, Angela Pomaro, Luciana Bertotti, Andrea Bonometto, Luca De Nat, Alvise Papa, Valerio Volpe

“…Abstract The dataset comprises a 45-year-long directional wave time series recorded at the Acqua Alta Oceanographic research Tower (AAOT) since 1979. …”
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A Novel Method to Forecast Nitrate Concentration Levels in Irrigation Areas for Sustainable Agriculture by Halil Karahan, Müge Erkan Can

“…In Scenario I, random values from the dataset were predicted, while in Scenario II, predictions were made as a time series, and model results were compared with measured values for both scenarios. …”
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Analysis and Discussion of Atmospheric Precursor of European Heat Summers by Christine Träger-Chatterjee, Richard W. Müller, Jörg Bendix

“…However, using this index as predictor would lead to one false alarm and one missed event in the time series analysed (1958–2011). Hints are found that the disturbance of the “dipole-summer” connection is due to El Niño/Southern Oscillation (ENSO). …”
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Mathematical modelling and forecasting of the Lithuanian export by Virmantas Kvedaras, Rimantas Rudzkis

“…This paper examines the Lithuanian export trends by different countries using a modem non-stationary time series and econometric theory. To avoid spurious regression, time series are modelled as an integrated process. …”
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Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market by Fang Wang, Sai Tang, Menggang Li

“…Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. …”
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Bayesian Approach for Sequential Probabilistic Back Analysis of Uncertain Geomechanical Parameters and Reliability Updating of Tunneling-Induced Ground Settlements by Cong Li, Shui-Hua Jiang, Jinhui Li, Jinsong Huang

“…This paper proposes a new sequential probabilistic back analysis approach for probabilistically determining the uncertain geomechanical parameters of shield tunnels by using time-series monitoring data. The approach is proposed based on the recently developed Bayesian updating with subset simulation. …”
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Multistep Wind Speed Prediction Based on the Fractional-Order Longicorn Swarm Algorithm and the Two-Stage Modal Decomposition Strategy by Shuren Pu, Yuanchen Gao, Yifeng Sun, Chang Liu, Siyu He, Fengjiao Wu, Delan Zhu, Bin Wang

“…Least squares support vector machine (LSSVM) and variational mode decomposition are common research methods for wind speed time series prediction. Addressing the challenge of selecting relevant parameters of LSSVM and VMD, a fractional-order Beetle swarm optimization algorithm is proposed to optimize the relevant parameters. …”
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A Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks by Muhammed Adil Yatkin, Mihkel Kõrgesaar, Ümit Işlak

“…These sequences represent time stamps or samplings of a continuous process collectively forming a time series dataset utilized for training recurrent neural networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for pattern prediction. …”
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