Methods for detection of crises in the economy in the early stages by V. A. Ivaniuk, N. M. Abdikeev

“…The object of the study was the crisis situation of the individual assets, as well as methods of detecting the crisis at an early stage.…”
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Marine soundscape forecasting: A deep learning-based approach by Shashidhar Siddagangaiah

“…Despite the rapid development of anomaly detection algorithms and deep-learning models for forecasting, their application to marine soundscapes remains unexplored. …”
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An integrated framework for multi-commodity agricultural price forecasting and anomaly detection using attention-boosted models by Eko Sediyono, Kristoko Dwi Hartomo, Christian Arthur, Intiyas Utami, Ronny Prabowo, Raymond Chiong

“…The proposed models outperformed baseline methods by achieving lower forecasting and anomaly detection errors. …”
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Analyzing Taiwanese Traffic Patterns on Consecutive Holidays Through Forecast Reconciliation and Prediction-Based Anomaly Detection Techniques by Mahsa Ashouri, Frederick Kin Hing Phoa, Marzia Angela Cremona

“…We propose a prediction-based detection method for identifying highway traffic anomalies using reconciled ordinary least squares (OLS) forecasts and bootstrap prediction intervals. …”
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Automated Detection of coronaL MAss Ejecta origiNs for Space Weather AppliCations (ALMANAC) by Thomas Williams, Huw Morgan

“…This paper presents a method that detects and estimates the central coordinates of CME eruptions in Extreme Ultraviolet data, with the dual aim of providing an early alert, and giving an initial estimate of the CME direction of propagation to a CME geometrical model. …”
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System analysis and information processing to solve the problem of detecting breakdowns of computer information storage by N. M. Kodatsky, E. A. Revyakina, A. R. Gazizov

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Ensemble-based forecasting of wildfire potentials using relative index in Gangwon Province, South Korea by Sang Yeob Kim, Changhyun Jun, Wooyoung Na

“…This study proposes post-processing procedures for wildfire forecasting by applying statistical index-merging methods to enhance the utility of conventional wildfire indices in forested regions of South Korea. …”
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Evaluating LSTM Performance on Multivariate Time Series with One-Class SVM Outlier Detection by Ragita Anillya Putri, Sugiyarto Surono, Aris Thobirin

“…Weekly sales forecasting plays a crucial role in retail business planning and inventory management.This study evaluates the prediction performance of a Long Short-Term Memory (LSTM) model for weekly sales forecasting after data preprocessing using standardization and outlier detection with One-Class Support Vector Machine (OCSVM) method. …”
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Forecasting the Detection of Lyman-alpha Forest Weak Lensing from the Dark Energy Spectroscopic Instrument and Other Future Surveys by Patrick Shaw, Rupert A. C. Croft, R. Benton Metcalf

“…We show that spectral surveys with low density and high volume are promising candidates for forest weak lensing in addition to the high resolution data that have been considered in previous work. We present forecasts for future spectral surveys and show that with larger datasets a detection with signal to noise $>10$ will be possible.…”
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Integrated Time Series Analysis, Clustering, and Forecasting for Energy Efficiency Optimization and Tariff Management by Anderson Jhones Passos Nascimento, Menaouar Berrehil El Kattel, Jose Antonio Fernandes de Macedo, Fernando Luiz Marcelo Antunes

“…Additionally, the study incorporates consumption forecasting methods to estimate the behavior of new consumer units or those with limited historical data, optimizing the definition of contracted demand and promoting energy efficiency. …”
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Optimising energy distribution and detecting vulnerabilities in networks using artificial intelligence by D. Koshkin, O. Sadovoy, A. Rudenko, V. Sokolik

“…Load forecasting methods, including neural networks, decision trees, and reinforcement learning, contributed to reducing energy consumption and preventing overloads. …”
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Advances in AI-based rainfall forecasting: a comprehensive review of past, present, and future directions with intelligent data fusion and climate change models by Farhan Amir Fardush Sham, Ahmed El-Shafie, Wan Zurina Wan Jaafar, Adarsh S, Mohsen Sherif, Ali Najah Ahmed

“…Accurate rainfall forecasting is crucial for managing water resources, supporting agriculture, and preparing for natural disasters, especially as climate variability becomes more pronounced. …”
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Simulation Study to Identify Factors Affecting the Performance of LSTM and XGBoost for Anomaly Detection on Labeled Time Series Data by Muhammad Rizky Nurhambali, Yenni Angraini, Anwar Fitrianto

“…Both use a forecasting approach for anomaly detection. However, the limitations of both methods on anomalies, such as data length, labeling method, and number of anomalies have not been explored. …”
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Adaptation of k-means to automated forecasting of poorly structured time series of economic dynamics by Dunskaia Lada, Popova Elena

“…Furthermore, machine learning and deep data analysis methods allow not only to improve the forecasting accuracy but also to identify hidden patterns that may be overlooked by traditional statistical approaches. …”
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Defly Compass Trend Analysis Methodology: Quantifying Trend Detection to Improve Foresight in Strategic Decision Making by Mabel López Bordao, Antonia Ferrer Sapena, Carlos A. Reyes Pérez, Enrique A. Sánchez Pérez

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The Statistical Detecting of Sulfur Hexafluoride and Nitrous Oxide Effect on Seasonal Rainfalls in Iran During Recent Decade by Yosef Ghavidel Rahimi, Maysam Tolabi Nejad, Manochehr Farajzadeh

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A real-time PCR method for rapid detection of Gymnodinium sanguineum by HE Shan-ying, YU Zhi-gang

“…For detecting the harmful algal bloom (HAB) species sensitively and rapidly, Gymnodinium sanguineum was taken as the object, and the rapid detection method- RFQ-PCR (real-time fluorescent quantitative polymerase chain reaction) technique was applied to the HAB species detection. …”
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Research Status and Prospect of Torsional Vibration Detection Methods for Rotating Machinery by GUO Yan-ling, QIU Feng, LI Zhi-peng

“…We introduce each method of these methods, espound in detail the woking principle, reaserch significance, practical application, advantages and disadvantages of each methods, and finally forecast the development trend of these methods. …”
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Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks by Hebin Guo, Kexin Li, Yutong Rou

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Non-Intrusive Monitoring and Detection of Mobility Loss in Older Adults Using Binary Sensors by Ioan Susnea, Emilia Pecheanu, Adina Cocu, Adrian Istrate, Catalin Anghel, Paul Iacobescu

“…Clinical mobility assessment methods, though precise, are resource-intensive and economically impractical, and most of the existing solutions for automatic detection of mobility anomalies are either obtrusive or improper for long time monitoring. …”
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