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Self-supervised learning for accelerometer-based human activity recognition: A survey
Self-supervised learning (SSL) has emerged as a promising alternative to purely supervised
learning, since it can learn from labeled and unlabeled data using a pre-train-then-fine-tune …
learning, since it can learn from labeled and unlabeled data using a pre-train-then-fine-tune …
[HTML][HTML] Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model
Renewable energy production and the balance between production and demand have
become increasingly crucial in modern power systems, necessitating accurate forecasting …
become increasingly crucial in modern power systems, necessitating accurate forecasting …
Efficient time series processing for transformers and state-space models through token merging
Transformer architectures have shown promising results in time series processing. However,
despite recent advances in subquadratic attention mechanisms or state-space models …
despite recent advances in subquadratic attention mechanisms or state-space models …
Enhancing Hierarchical Sales Forecasting with Promotional Data: A Comparative Study Using ARIMA and Deep Neural Networks.
Retailers depend on accurate sales forecasts to effectively plan operations and manage
supply chains. These forecasts are needed across various levels of aggregation, making …
supply chains. These forecasts are needed across various levels of aggregation, making …
Causal Mechanism-Enabled Zero-Label Learning for Power Generation Forecasting of Newly-Built PV Sites
Power forecasting of newly built photovoltaic (PV) sites faces huge challenges owing to the
lack of sufficient training samples. To this end, this paper proposes an unsupervised zero …
lack of sufficient training samples. To this end, this paper proposes an unsupervised zero …
Foundation Models for CPS-IoT: Opportunities and Challenges
Methods from machine learning (ML) have transformed the implementation of Perception-
Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of …
Cognition-Communication-Action loops in Cyber-Physical Systems (CPS) and the Internet of …
[HTML][HTML] Exploration of Foundational Models for Blood Glucose Forecasting in Type-1 Diabetes Pediatric Patients
Aims: The accurate prediction of blood glucose (BG) levels is critical for managing Type-1
Diabetes (T1D) in pediatric patients, where variability due to factors like physical activity and …
Diabetes (T1D) in pediatric patients, where variability due to factors like physical activity and …
Are Time Series Foundation Models Ready to Revolutionize Predictive Building Analytics?
Recent advancements in large language models have spurred significant developments in
Time Series Foundation Models (TSFMs). These models claim great promise in performing …
Time Series Foundation Models (TSFMs). These models claim great promise in performing …
Introducing ProsperNN—a Python package for forecasting with neural networks
We present the package prosper_nn, that provides four neural network architectures
dedicated to time series forecasting, implemented in PyTorch. In addition, prosper_nn …
dedicated to time series forecasting, implemented in PyTorch. In addition, prosper_nn …
Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation
The advent of large language models (LLMs) has dramatically advanced the state-of-the-art
in numerous natural language generation tasks. For LLMs to be applied reliably, it is …
in numerous natural language generation tasks. For LLMs to be applied reliably, it is …