Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events by Muhammad Azhar Ehsan, Michelle L. L’Heureux, Michael K. Tippett, Andrew W. Robertson, Jeffrey Turmelle

“…The analysis uncovers an asymmetry in predicting the onset of cold and warm ENSO episodes, with warm episode onsets being better forecasted than cold onsets in both DYN and STAT models. …”
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FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING HOLT WINTER'S EXPONENTIAL SMOOTHING METHOD AND EXTREME LEARNING MACHINE METHOD by Rochdi Wasono, Yulia Fitri, M. Al Haris

“…The number of passengers has continued to increase in the last few months at Ahmad Yani International Airport, so a forecast is needed in making decisions to predict the number of passengers in order to maximize existing performance. …”
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Decomposition-reconstruction-optimization framework for hog price forecasting: Integrating STL, PCA, and BWO-optimized BiLSTM. by Xiangjuan Liu, Yunlong Li, Fengtong Wang, Yujie Qin, Zhongyu Lyu

“…This study constructs a multi-stage hybrid forecasting model using hog price time series data and its influencing factors to improve prediction accuracy. …”
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Load Forecasting Using BiLSTM with Quantile Granger Causality: Insights from Geographic–Climatic Coupling Mechanisms by Xianan Huang, Lin Liu, Nuo Xu, Yantao Chen, Xiaofei Wang, Zhenzhi Lin

“…In order to explore the correlation between meteorological factors and power load changes, as well as the role of these factors in load forecasting, a hybrid load forecasting modeling framework based on quantile Granger causality test and bidirectional long short-term memory (QGCT-BiLSTM) is proposed. …”
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A hybrid deep learning framework for short-term load forecasting with improved data cleansing and preprocessing techniques by Muhammad Sajid Iqbal, Muhammad Adnan, Salah Eldeen Gasim Mohamed, Muhammad Tariq

“…Notably, the proposed approach achieves a remarkable MSE of 0.0058 for load forecasting and 0.0033 for generation forecasting. Comparative analysis with state-of-the-art (SOTA) techniques in terms of accuracy and computational cost underscores the superior accuracy of the proposed framework in forecasting both generation and demand. …”
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Urban land surface temperature forecasting: a data-driven approach using regression and neural network models by Nimish Gupta, Bharath Haridas Aithal

“…This research article proposes two forecasting techniques: Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models. …”
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APPLICATION OF EXTREME LEARNING MACHINE METHOD ON STOCK CLOSING PRICE FORECASTING PT ANEKA TAMBANG (PERSERO) TBK by Rita Apriliyanti, Neva Satyahadewi, Wirda Andani

“…Stock prices tend to be volatile which is influenced by the amount of market supply and demand, so forecasting analysis is needed to minimize the risks that may occur. …”
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Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models by Mengkai Chen, Jun Wang, Feilong Zhao, Gaopeng Jiang

“…Abstract To investigate the impact of real estate market sentiment on demand forecasting, this paper constructs a Weibo sentiment index incorporating emotional polarity and verifies its predictive advantage for market demand. …”
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A Short-Term Load Forecasting Method Considering Multiple Factors Based on VAR and CEEMDAN-CNN-BILSTM by Bao Wang, Li Wang, Yanru Ma, Dengshan Hou, Wenwu Sun, Shenghu Li

“…Short-term load is influenced by multiple external factors and shows strong nonlinearity and volatility, which increases the forecasting difficulty. However, most of existing short-term load forecasting methods rely solely on the original load data or take into account a single external factor, which results in significant forecasting errors. …”
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A systematic approach to predicting NFT prices using time series forecasting and macroeconomic factors in digital assets by Sudip Giri, Dongping Du, Mario Beruvides

“…To the authors’ best knowledge, this is the first study to utilize time-series transformers for forecasting NFTs based on macroeconomic factors.…”
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AMDCnet: attention-gate-based multi-scale decomposition and collaboration network for long-term time series forecasting by Shikang Hou, Song Sun, Tao Yin, Zhibin Zhang, Meng Yan

“…IntroductionTime series analysis plays a critical role in various applications, including sensor data monitoring, weather forecasting, economic predictions, and network traffic management. …”
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Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening. by Yaoyang Yi

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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

“…Abstract Geyser eruptions provide a test bed for using geophysical data to forecast eruptions and to understand heat and mass transport in hydrothermal systems. …”
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Forecast of elements of petroleum systems in the joint zone of the Siberian platform and adjacent structures in the southwestern part of the Laptev sea by D. A. Pavelkina, K. A. Guzeyeva, I. S. Avdeeva, I. S. Ustinov

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Granger Causality-Based Forecasting Model for Rainfall at Ratnapura Area, Sri Lanka: A Deep Learning Approach by Shanthi Saubhagya, Chandima Tilakaratne, Pemantha Lakraj, Musa Mammadov

“…Rainfall forecasting, especially extreme rainfall forecasting, is one of crucial tasks in weather forecasting since it has direct impact on accompanying devastating events such as flash floods and fast-moving landslides. …”
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A Day-Ahead PV Power Forecasting Method Based on Irradiance Correction and Weather Mode Reliability Decision by Haonan Dai, Yumo Zhang, Fei Wang

“…However, the existing framework still has the following two problems: (1) weather mode prediction and power forecasting are highly dependent on the accuracy of numerical weather prediction (NWP), but the existing classification forecasting framework ignores the impact from NWP errors; (2) the validity of the classification forecasting framework comes from the accurate prediction of weather modes, but the existing framework lacks the analysis and decision-making mechanism of the reliability of weather mode prediction results, which will lead to a significant decline in the overall accuracy when weather modes are wrongly predicted. …”
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Short-Term Electricity Price Forecasting Using the Empirical Mode Decomposed Hilbert-LSTM and Wavelet-LSTM Models by Kunal Shejul, R. Harikrishnan, Amit Kukker

“…The availability of the electricity price forecast is essential for the electricity market participants to make informed decisions. …”
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FORECASTING MONTHLY RAINFALL IN PANGKEP REGENCY USING STATISTICAL DOWNSCALING MODEL WITH ROBUST PRINCIPAL COMPONENT REGRESSION TECHNIQUE by Sitti Sahriman, Anisa Anisa

“…Additionally, the 2023 rainfall forecast results showed that both methods yielded relatively similar accuracy. …”
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Charging Strategies for Electric Vehicles Using a Machine Learning Load Forecasting Approach for Residential Buildings in Canada by Ahmad Mohsenimanesh, Evgueniy Entchev

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Improving rainfall forecasting using deep learning data fusing model approach for observed and climate change data by Farhan Amir Fardush Sham, Ahmed El-Shafie, Wan Zurina Binti Wan Jaafar, S. Adarsh, Mohsen Sherif, Ali Najah Ahmed

“…Traditional forecasting methods, such as linear regression, autoregressive models, and time-series analysis, are limited in their ability to capture the intricate and dynamic nature of rainfall patterns. …”
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