FTimeXer: Frequency-aware Time-series Transformer with Exogenous variables for Robust Carbon Footprint Forecasting
arXiv:2604.02347v1 Announce Type: new Abstract: Accurate and up-to-date forecasting of the power grid's carbon footprint is crucial for effective product carbon footprint (PCF) accounting and informed decarbonization decisions. However, the carbon intensity of the grid exhibits high non-stationarity, and existing methods often struggle to effectively leverage periodic and oscillatory patterns. Furthermore, these methods tend to perform poorly when confronted with irregular exogenous inputs, such as missing data or misalignment. To tackle these challenges, we propose FTimeXer, a frequency-aware time-series Transformer designed with a robust training scheme that accommodates exogenous factors. FTimeXer features an Fast Fourier Transform (FFT)-driven frequency branch combined with gated time-
View PDF HTML (experimental)
Abstract:Accurate and up-to-date forecasting of the power grid's carbon footprint is crucial for effective product carbon footprint (PCF) accounting and informed decarbonization decisions. However, the carbon intensity of the grid exhibits high non-stationarity, and existing methods often struggle to effectively leverage periodic and oscillatory patterns. Furthermore, these methods tend to perform poorly when confronted with irregular exogenous inputs, such as missing data or misalignment. To tackle these challenges, we propose FTimeXer, a frequency-aware time-series Transformer designed with a robust training scheme that accommodates exogenous factors. FTimeXer features an Fast Fourier Transform (FFT)-driven frequency branch combined with gated time-frequency fusion, allowing it to capture multi-scale periodicity effectively. It also employs stochastic exogenous masking in conjunction with consistency regularization, which helps reduce spurious correlations and enhance stability. Experiments conducted on three real-world datasets show consistent improvements over strong baselines. As a result, these enhancements lead to more reliable forecasts of grid carbon factors, which are essential for effective PCF accounting and informed decision-making regarding decarbonization.
Comments: Accepted by The 5th International Conference on Electronics Technology and Artificial Intelligence (ETAI 2026)
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2604.02347 [cs.LG]
(or arXiv:2604.02347v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2604.02347
arXiv-issued DOI via DataCite
Submission history
From: Hui Ma [view email] [v1] Mon, 16 Feb 2026 04:19:56 UTC (2,397 KB)
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
transformertrainingannounce
An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications
arXiv:2509.19185v3 Announce Type: replace Abstract: Foundation model (FM)-based AI agents are rapidly gaining adoption across diverse domains, but their inherent non-determinism and non-reproducibility pose testing and quality assurance challenges. While recent benchmarks provide task-level evaluations, there is limited understanding of how developers verify the internal correctness of these agents during development. To address this gap, we conduct the first large-scale empirical study of testing practices in the AI agent ecosystem, analyzing 39 open-source agent frameworks and 439 agentic applications. We identify ten distinct testing patterns and find that novel, agent-specific methods like DeepEval are seldom used (around 1%), while traditional patterns like negative and membership tes
A Multi-Language Perspective on the Robustness of LLM Code Generation
arXiv:2504.19108v5 Announce Type: replace Abstract: Large language models have gained significant traction and popularity in recent times, extending their usage to code-generation tasks. While this field has garnered considerable attention, the exploration of testing and evaluating the robustness of code generation models remains an ongoing endeavor. Previous studies have primarily focused on code generation models specifically for the Python language, overlooking other widely used programming languages. In this work, we conduct a comprehensive comparative analysis to assess the robustness performance of several prominent code generation models and investigate whether robustness can be improved by repairing perturbed docstrings using an LLM. Furthermore, we investigate how their performanc
Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models
arXiv:2411.10656v2 Announce Type: replace Abstract: Context: Large Language Models (LLMs) like GPT-5 and LLaMA-405b exhibit advanced code generation abilities, but their deployment demands substantial computation resources and energy. Quantization can reduce memory footprint and hardware requirements, yet may degrade code quality. Objective: This study investigates code generation performance of smaller LLMs, examines the effect of quantization, and identifies common code quality issues as a proof of concepts (PoC). Method: Four open-source LLMs are evaluated on Python benchmarks using code similarity metrics, with an analysis on 8-bit and 4-bit quantization, alongside static code quality assessment. Results: While smaller LLMs can generate functional code, benchmark performance is limited
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
qwen3.5 vs gemma4 vs cloud llms in python turtle
I have found python turtle to be a pretty good test for a model. All of these models have received the same prompt: "write a python turtle program that draws a cat" you can actually see similarity in gemma's and gemini pro's outputs, they share the color pallete and minimalist approach in terms of details. I have a 16 gb vram gpu so couldn't test bigger versions of qwen and gemma without quantisation. gemma_4_31B_it_UD_IQ3_XXS.gguf Qwen3_5_9B_Q8_0.gguf Qwen_3_5_27B_Opus_Distilled_Q4_K_S.gguf deepseek from web browser with reasoning claude sonnet 4.6 extended gemini pro from web browser with thinking submitted by /u/SirKvil [link] [comments]
![[Benchmark] Altered Riddles: Can LLMs ignore what they've memorised?](https://external-preview.redd.it/mX2OY_FVeaecjQBAWXivFgSAkTu5esw_Cjh_pFQevUA.png?width=140&height=75&auto=webp&s=e11af534a579cc92bb2fb8c5eb2dea0fba70f8da)
[Benchmark] Altered Riddles: Can LLMs ignore what they've memorised?
In the past year you may have encountered the following prompt: The surgeon, who is the boy's father, says, 'I cannot operate on this boy—he's my son!'. Who is the surgeon to the boy? If you try to give this prompt to an LLM right now you will probably still receive “The mother” as an answer, even though the text explicitly states that the surgeon is the boy’s father; this is probably due to the fact that this prompt is an alteration of a very common “riddle”, to which the answer is, in fact, the mother: A man and his son are in a terrible accident and are rushed to the hospital in critical condition. The doctor looks at the boy and exclaims, "I can't operate on this boy; he's my son!" How could this be? Working on this failure mode, I initially decided to create a small dataset of altered
A Multi-Language Perspective on the Robustness of LLM Code Generation
arXiv:2504.19108v5 Announce Type: replace Abstract: Large language models have gained significant traction and popularity in recent times, extending their usage to code-generation tasks. While this field has garnered considerable attention, the exploration of testing and evaluating the robustness of code generation models remains an ongoing endeavor. Previous studies have primarily focused on code generation models specifically for the Python language, overlooking other widely used programming languages. In this work, we conduct a comprehensive comparative analysis to assess the robustness performance of several prominent code generation models and investigate whether robustness can be improved by repairing perturbed docstrings using an LLM. Furthermore, we investigate how their performanc
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!