Limits of Solar Flare Forecasting Models and New Deep Learning Approach by G. Francisco, M. Berretti, S. Chierichini, R. Mugatwala, J. Fernandes, T. Barata, D. Del Moro

“…Reliable forecasting models are necessary to mitigate the risks posed by solar flares to human technology. …”
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CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting by Josef Koumar, Karel Hynek, Tomáš Čejka, Pavel Šiška

“…This variability provides a realistic and challenging environment for developing forecasting and anomaly detection models, enabling evaluations that are closer to real-world deployment scenarios. …”
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Taxi Demand Prediction Based on a Combination Forecasting Model in Hotspots by Zhizhen Liu, Hong Chen, Yan Li, Qi Zhang

“…Next, we compared the predictive effect of the random forest model (RFM), ridge regression model (RRM), and combination forecasting model (CFM). …”
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Multivariate Segment Expandable Encoder-Decoder Model for Time Series Forecasting by Yanhong Li, David C. Anastasiu

“…By capturing quantile distributions across segmented subsequences at multiple scales, the model is able to detect complex patterns, enhancing both the accuracy and robustness of forecasts. …”
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Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection by Omar Farhan Al-Hardanee, Hüseyin Demirel

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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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Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm by Xiaolei Chen, Sikun Ge

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Deep learning-based dual monitoring system for power forecasting and fault detection in nuclear power applications by Mingzhe Lyu, Helin Gong, Zhang Chen, Jiangyu Wang, Mingxiao Zhong, Zhiyong Wang, Qing Li, Zefei Pan

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Vibration Signal Forecasting on Rotating Machinery by means of Signal Decomposition and Neurofuzzy Modeling by Daniel Zurita-Millán, Miguel Delgado-Prieto, Juan José Saucedo-Dorantes, Jesus Adolfo Cariño-Corrales, Roque A. Osornio-Rios, Juan Antonio Ortega-Redondo, Rene de J. Romero-Troncoso

“…The method combines the adaptability of neurofuzzy modeling with a signal decomposition strategy to model the patterns of the vibrations signal under different fault scenarios. …”
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An explainable Machine Learning model for Large-Scale Travelling Ionospheric Disturbances forecasting by Ventriglia Vincenzo, Guerra Marco, Cesaroni Claudio, Spogli Luca, Altadill David, Segarra Antoni, Galkin Ivan, Barta Veronika, Verhulst Tobias G.W., de Paula Víctor, Navas-Portella Víctor, Berényi Kitti A., Belehaki Anna

“…The validation procedure consists of a global-level evaluation and interpretation step, firstly, followed by an event-level validation against independent detection methods, which highlights the model’s predictive robustness and suggests its potential for real-time space weather forecasting. …”
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Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles. by Felipe J Colón-González, Leonardo Soares Bastos, Barbara Hofmann, Alison Hopkin, Quillon Harpham, Tom Crocker, Rosanna Amato, Iacopo Ferrario, Francesca Moschini, Samuel James, Sajni Malde, Eleanor Ainscoe, Vu Sinh Nam, Dang Quang Tan, Nguyen Duc Khoa, Mark Harrison, Gina Tsarouchi, Darren Lumbroso, Oliver J Brady, Rachel Lowe

“…The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). …”
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Forecasting Rice Paddy Production in Aceh Using ARIMA and Exponential Smoothing Models by Nurviana Nurviana, Amelia Amelia, Riezky Purnama Sari Riezky Purnama Sari, Ulya Nabilla Ulya Nabilla, Taufan Talib

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Forecasting Eruptions at Steamboat Geyser: Time Scales, Differentiability, and Detectability of Seismic Precursors Through Data‐Driven Methods by Alberto Ardid, Anna Barth, David Dempsey, Michael Manga, Shane J. Cronin

“…We applied isotonic regression, a method that converts raw model outputs into calibrated probabilities, to improve the interpretability of eruption forecasting outputs. …”
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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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THE POTENTIAL OF HYBRID LSTM-GENERATIVE AI ECO-MODEL IN FORECASTING FINANCIAL AND ECONOMIC INDICATORS by Andrii Ivashchenk, Tetiana Ivashchenko

“…Future research directions include enhancing anomaly detection mechanisms, incorporating additional weak predictors, and refining the role of generative AI in hybrid time-series forecasting models.…”
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SentiTSMixer: A Specific Model for Sales Forecasting Using Sentiment Analysis of Customer by Partha Ghosh, Subhashis Das, Subhankar Roy, Ankur Bhattacharjee, Agostino Cortesi, Soumya Sen

“…In this research, we have modified the TSMixer model for sales forecasting by amalgamating customer satisfaction levels regarding a specific product. …”
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On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models by Youness El Mghouchi, Mihaela Tinca Udristioiu

“…This study aims to develop robust predictive and forecasting models for hourly PM concentrations in Craiova, Romania, using advanced hybrid Artificial Intelligence (AI) approaches. …”
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A GRU-Based Model Using GNSS-PWV and Meteorological Data for Forecasting Rainfalls by Longjiang Li, Kefei Zhang, Hong Zhang, Suqin Wu, Dongsheng Zhao, Xiaoming Wang, Andong Hu, Minghao Zhang, Mardina Abdullah

“…These results suggest that the GRU-based model can effectively forecast most rainfall events due to its utilization of more meteorological data in the input data.…”
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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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Forecasting CO2 emissions in BRICS countries using the grey breakpoint prediction models by Huiping Wang, Xinge Guo

“…Finally, the novel grey breakpoint prediction models are used to simulate and forecast the CO2 emissions in BRICS countries. …”
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