GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism by Burak Gülmez

“…The findings provide valuable insights for practitioners in financial markets and contribute to the advancement of hybrid intelligent systems for time series prediction. The model's superior performance is attributed to its unique integration of evolutionary optimization, attention-based feature selection, and fuzzy logic's ability to handle uncertainty in financial data.…”
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Reappraising double pendulum dynamics across multiple computational platforms by Sandy Herho, Faiz Fajary, Katarina Herho, Iwan Anwar, Rusmawan Suwarman, Dasapta Irawan

“…These were solved numerically using the efficient Runge-Kutta-Fehlberg method, implemented in Python, R, GNU Octave, and Julia, while runtimes and memory usage were extensively benchmarked across these environments. Time series analyses, including the calculation of Shannon entropy and the Kolmogorov - Smirnov test, quantified the system's unpredictability and sensitivity to infinitesimal perturbations of the initial conditions. …”
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Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models by Laurentiu Fara, Alexandru Diaconu, Dan Craciunescu, Silvian Fara

“…This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. …”
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Reinforcement learning for deep portfolio optimization by Ruyu Yan, Jiafei Jin, Kun Han

“…Additionaly, it was crucial to simultaneously consider the time series and complex asset correlations of financial market information. …”
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Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index by Xiaole Wan, Zhen Zhang, Chi Zhang, Qingchun Meng

“…So, the stock data of the CSI 300 were chosen and divided into two time series, prepared for analysis via network theory. …”
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New Polarization Basis for Polarimetric Phased Array Weather Radar: Theory and Polarimetric Variables Measurement by Jian Dong, Qingfu Liu, Xuesong Wang

“…In addition, the estimates of these parameters based on the time series data acquired with the new polarization basis are also investigated. …”
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Bivariate EMD-Based Data Adaptive Approach to the Analysis of Climate Variability by Md. Khademul Islam Molla, Poly Rani Ghosh, Keikichi Hirose

“…This paper presents a data adaptive approach for the analysis of climate variability using bivariate empirical mode decomposition (BEMD). The time series of climate factors: daily evaporation, maximum and minimum temperatures are taken into consideration in variability analysis. …”
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Short‐Term Prediction of the Dst Index and Estimation of Efficient Uncertainty Using a Hybrid Deep Learning Network by Ruyao Wang, Jianhui Wang, Tuo Liang, Huixiong Zhang

“…The proposed model achieves excellent scalability by extracting representative embeddings from the Dst index time series through an encoder‐decoder framework and integrating these with external solar wind parameters via a prediction network. …”
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Temporal and spatial co-occurrence of pacific oyster mortality and increased planktonic Vibrio abundance by Elliot Scanes, Nachshon Siboni, Jaimie Potts, Shivanesh Rao, Maurizio Labbate, Justin R. Seymour

“…In Port Stephens, Australia, we characterized the microbial community and quantified the abundance of total Vibrio, Vibrio harveyi, and Vibrio parahaemolyticus in a (i) 27-month seawater planktonic microbial time-series; (ii) samples of Pacific oysters (Crassostrea gigas) during a mortality event and (iii) seawater samples following the mortality event. …”
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Assessment of Meteorological Drought in Korea under Climate Change by Jaewon Kwak, Soojun Kim, Jaewon Jung, Vijay P. Singh, Dong Ryul Lee, Hung Soo Kim

“…To analyze the effect of climate change, spatial distribution of drought in the future is analyzed using the SPI time series calculated from Representative Concentration Pathways (RCPs) scenarios and HADGEM3-RA regional climate model. …”
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Bifurcation Analysis and Synchronous Patterns between Field Coupled Neurons with Time Delay by Li Zhang, Xinlei An, Jiangang Zhang, Qianqian Shi

“…Then, the synchronization patterns of two HR neurons with different stimulation are analyzed by error diagrams and time series diagrams. It is confirmed that the synchronous pattern has certain regularity and is related not only to the neurons with large stimulation current but also to the time delay and coupling gain. …”
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Evaluation of the Fluctuation Mechanism of Behavioral Financial Market Based on Edge Computing by Xiaoliang Yuan

“…Analysis of the volatility characteristics of financial markets must give priority to the analysis of financial chronological order. Financial time series are characterized by differences in financial markets, which are indeterminate orders, and the analysis of their fluctuations becomes crucial for stimulating the microstructure of financial behavior markets. …”
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Climate Predictions: The Chaos and Complexity in Climate Models by D. T. Mihailović, G. Mimić, I. Arsenić

“…Finally, we have explored possible differences in complexities of two global and two regional climate models using their air temperature and precipitation output time series. The complexities were obtained with the algorithm for calculating the Kolmogorov complexity.…”
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Meta Learning Strategies for Comparative and Efficient Adaptation to Financial Datasets by Kubra Noor, Ubaida Fatima

“…This research proposes a Meta learning framework for financial time series forecasting, designed to rapidly adapt to novel market conditions with minimal retraining. …”
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Quadratic magnetic gradients from seven- and nine-spacecraft constellations by C. Shen, G. Zeng, R. Kieokaew, Y. Zhou

“…This study focuses on deriving both linear and quadratic spatial gradients of the magnetic field using data from the nine-spacecraft (9S/C) HelioSwarm or seven-spacecraft (7S/C) Plasma Observatory constellations. Time series magnetic measurements, combined with transformations between reference frames, were employed to determine the apparent velocity of the magnetic structure and the quadratic magnetic gradient components along the direction of motion. …”
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Agricultural Credit Policy and Livestock Development in Nigeria by Sunday Akpan, Veronica Nkanta, Edet Udoh

“…This research aimed to provide empirical information on the relationship between the livestock production index and the credit policy environment in Nigeria. Time series data were used, and an autoregressive distributed lag (ARDL) bound test approach was adopted to establish the presence of co-integration among series. …”
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Empirical mode decomposition analysis of climate changes with special reference to rainfall data by Md. Khademul Islam Molla, M. Sayedur Rahman, Akimasa Sumi, Pabitra Banik

“…We have used empirical mode decomposition (EMD) method, which is especially well fitted for analyzing time-series data representing nonstationary and nonlinear processes. …”
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The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification by Jiyong Li, Shunming Li, Xiaohong Chen, Lili Wang

“…The whole paper aims to classify the feature vectors extracted from the time series and magnitude of spectral analysis and to discriminate the state of the rolling element bearings by virtue of multiclass LSSVM. …”
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A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting by Guangyuan Xing, Shaolong Sun, Jue Guo

“…First, we utilize EEMD to decompose original time series of PM2.5 concentrations into a specific amount of independent intrinsic mode functions (IMFs) and residual term. …”
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Forecast of Chaotic Series in a Horizon Superior to the Inverse of the Maximum Lyapunov Exponent by Miguel Alfaro, Guillermo Fuertes, Manuel Vargas, Juan Sepúlveda, Matias Veloso-Poblete

“…In this article, two models of the forecast of time series obtained from the chaotic dynamic systems are presented: the Lorenz system, the manufacture system, and the volume of the Great Salt Lake of Utah. …”
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