Recently, time-series forecasting foundation models trained on large, diverse datasets have demonstrated robust zero-shot and few-shot capabilities. Given the ubiquity of time-series data in IoT, finance, and industrial applications, rigorous benchma...
This study investigates the transfer learning capabilities of Time-Series Foundation Models (TSFMs) under the zero-shot setup, to forecast macroeconomic indicators. New TSFMs are continually emerging, offering significant potential to provide ready-t...
Time series forecasting (TSF) is gaining significance in various applications. In recent years, many pre-trained large language models (LLMs) have been proposed, and some of them have been adapted for use in TSF. When applying LLMs to TSF, existing s...
Market indices, such as the S&P 500, serve as compressed representations of complex constituent-level dynamics. This study proposes a zero-shot forecasting framework capable of predicting index-level trajectories without direct supervision from i...
Time series foundation models (TSFMs) promise accurate zero-shot forecasts, but their value for carbon markets has been examined only in the European Union Emissions Trading System (EU ETS). We evaluate Chronos-2 and TimesFM-2.5 alongside six benchma...
Electricity forecasting is one of the most crucial aspects in maintaining stable, reliable, and autonomous power systems. While recently developed forecasting methods based on large language models can make accurate predictions, these models are stil...
20 September 2023
Typically, renewable-power-generation forecasting using machine learning involves creating separate models for each photovoltaic or wind park, known as single-task learning models. However, transfer learning has gained popularity in recent years, as...
Foundation models for time series forecasting have recently been applied to energy prediction tasks, where they can produce accurate forecasts without task-specific training. This study investigates the impact of two types of covariates, meteorologic...
Satellite observations reveal the offshore distribution of pelagic Sargassum, but predicting its occurrence at individual Caribbean coastlines remains challenging where local observational records are limited. This study tests whether forecasts devel...
Current building energy benchmarking relies on annual Energy Use Intensity (EUI), which cannot detect temporal operational anomalies—such as after-hours equipment operation or irregular scheduling—that represent actionable efficiency oppo...
Forecasting foreign exchange rates over long time periods depends on economic fundamentals. Short-term predictions, by contrast, depend largely on emotions, governmental announcements, the flow of capital, and media commentary. This paper proposes a...
Time-series foundation models have shown strong zero-shot forecasting ability, but their usefulness for industrial fault diagnosis remains unclear. This paper evaluates TimesFM as a frozen forecasting model for few-shot diagnosis of semiconductor pro...
Accurate forecasting is one of the keys to the efficient use of the limited existing energy resources and plays an important role in sustainable development. While most of the current research has focused on energy price forecasting, very few studies...
Accurate photovoltaic (PV) power forecasting is becoming increasingly important for the secure and economical operation of renewable-rich power systems. However, conventional data-driven forecasting methods generally rely on sufficient site-specific...
Detecting anomalies in time series data is crucial across several domains, including healthcare, finance, and automotive. Large Language Models (LLMs) have recently shown promising results by leveraging robust model pretraining. However, fine-tuning...
Surface soil moisture forecasting is important for drought monitoring, irrigation management, and land–atmosphere process analysis but remains challenging because near-surface soil moisture is jointly influenced by antecedent precipitation, atm...
Accurate and efficient cryptocurrency price prediction is vital for investors in the volatile crypto market. This study comprehensively evaluates nine models—including baseline, zero-shot, and deep learning architectures—on 21 major crypt...
Increasingly shaped by heterogeneous on-chain activity rather than a single shared market process, this study investigates 7-day-ahead forecasting using 147 market and on-chain indicators across eight major blockchain ecosystems from October 2023 to...
This paper presents “Quantifying Airline Reputation from Multilingual Online Content: Artificial Intelligence and a Practical Application in a Lightweight Reputation-Intelligence Framework.” Airline reputation is increasingly shaped by mu...
The operational integration of solar energy into modern power grids, particularly for day-ahead markets and battery storage scheduling, requires highly accurate, multi-horizon irradiance forecasts to mitigate cloud-induced intermittency. This study e...
Accurate long-term forecasting of Quality of Transmission (QoT) is critical for the proactive operation and condition-aware management of dynamic elastic optical networks. However, the evolution of QoT is governed by multi-scale dynamics, including s...
Forecasting vibration-derived health indicators is distinct from fault diagnosis and remaining-useful-life estimation, yet controlled comparisons that preserve temporal order, physical scale, and dependence among forecast errors remain limited. This...
Background/Objectives: Early prognostication in idiopathic sudden sensorineural hearing loss (SSNHL) remains difficult, and although the use of large language models (LLMs) in otolaryngology is being increasingly evaluated, data directly comparing th...
Accurate photovoltaic (PV) power forecasting is important for grid dispatch, energy-storage management, and electricity-market trading. This paper presents a PV power forecasting framework that combines multi-model ensemble learning with multi-perspe...
Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models. However, these methods encounter significant challenges...
Large language models (LLMs) and time-series foundation models have advanced traffic forecasting, but almost exclusively on freeway sensors at five-minute, aggregate-flow resolution; their behavior on minute-resolution, movement-level counts at signa...
Indoor CO2 forecasting supports proactive ventilation control that balances air quality with energy efficiency. While Machine Learning (ML) models have shown strong performance in controlled settings such as schools, their generalization across indoo...
The building sector accounts for 34% of global energy demand, making accurate short-term load forecasting essential. Small commercial buildings remain challenging due to high-frequency noise and data sparsity. This study proposes a hybrid machine lea...
Tight gas reservoirs present unique forecasting challenges due to steep decline rates, nonlinear production dynamics, and sensitivity to operational conditions. Conventional decline-curve methods and reservoir simulations are limited either by oversi...
To address the nonlinear amplification of numerical weather prediction (NWP) errors and the difficult trade-off between coverage and sharpness in short-term offshore wind power forecasting, this paper proposes PRWind, a physics-guided framework for s...
Sea surface temperature (SST) plays a pivotal role in air–sea interactions, with implications for climate, weather, and marine ecosystems, particularly in regions like the Caribbean Sea, where upwelling and dynamic oceanographic processes signi...
Accurate short-term building energy consumption forecasting is crucial for energy-system operational efficiency and demand-side management. Existing deep learning models heavily rely on historical data, making them costly for data-scarce buildings. F...
Probabilistic electricity price forecasting (PEPF) is a highly complex task with broad economic and operational impact. Recent advances in time series foundation models (TSFMs) offer promising tools to improve PEPF performance. In contrast, PEPF prov...
The low-altitude airspace of bird flocks is gradually shared by unmanned aerial vehicles (UAVs), posing safety risks that necessitate accurate trajectory forecasting. However, existing vision-based methods often treat trajectory prediction and UAV de...
Accurate prediction of electric vehicle charging demand is imperative for ensuring grid stability and optimizing urban mobility resources. While the emergence of large language models has introduced translation-based forecasting paradigms, existing m...
Diamondback moth (DBM, Plutella xylostella) outbreaks are shaped by delayed meteorological conditions, yet most forecasting models compress weather into a few monthly summaries and provide limited ecological interpretation. We propose MeteoSCOPE, an...
As climate change transforms our environment and human intrusion into natural ecosystems escalates, there is a growing demand for disease spread models to forecast and plan for the next zoonotic disease outbreak. Accurate parametrization of these mod...
As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have acce...
The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) ha...
Zero-shot time series foundation models (TSFMs) forecast unseen series without target-specific training, offering a way to significantly reduce the deployment overhead in enterprise pipelines and industrial sensor streams. However, their performance...
Very short-term wind power forecasting without future numerical weather prediction (NWP) must exploit historical observations while controlling operating-state heterogeneity, erroneous analog retrieval, and unstable expert corrections. This study pro...
The increase in the volume of organic household waste and the transition to a circular economy require the development of reliable forecasting tools capable of ensuring effective planning of waste recycling and bioenergy production systems. Despite s...
Accurate State-of-Health (SOH) prediction is essential for deploying retired electric vehicle batteries into reliable second-life energy storage systems. However, this task is challenged by sparse and noisy operational data from onboard Battery Manag...
Time series and spatio-temporal forecasting are fundamental tasks for complex system modeling and intelligent decision-making, with broad applications in transportation, meteorology, finance, healthcare, and public safety. Compared with simple univar...
Generative models are being used in all domains. While primarily built for processing texts and images, their reach has been further extended towards data-driven forecasting. Whereas there are many statistical, machine learning and deep learning mode...