Search Results for author:
Found 15 papers, 10 papers with code
Time Series Analysis for Education: Methods, Applications, and Future Directions
The contributions of this paper include a detailed taxonomy of educational data, a synthesis of time series techniques with specific educational applications, and a forward-looking perspective on emerging trends and future research opportunities in educational analysis.
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
Conditioned models, on the other hand, utilize extra information to enhance performance and are similarly divided for both predictive and generative tasks.
Logic Agent: Enhancing Validity with Logic Rule Invocation
Chain-of-Thought (CoT) prompting has emerged as a pivotal technique for augmenting the inferential capabilities of language models during reasoning tasks.
Large Language Models for Education: A Survey and Outlook
The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education.
Bringing Generative AI to Adaptive Learning in Education
The recent surge in generative AI technologies, such as large language models and diffusion models, has boosted the development of AI applications in various domains, including science, finance, and education.
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
Benchmarks and Custom Package for Energy Forecasting
However, there are many differences between energy forecasting and traditional time series forecasting.
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection.
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
To fill this gap, we review current state-of-the-art SSL methods for time series data in this article.
DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty.
LogiCoT: Logical Chain-of-Thought Instruction-Tuning
LogiCoT serves as an instruction set for teaching models of logical reasoning and elicits general reasoning skills.
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes
This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system.
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data.
NetRCA: An Effective Network Fault Cause Localization Algorithm
Localizing the root cause of network faults is crucial to network operation and maintenance.
Transformers in Time Series: A Survey
From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis.
