Data for: Where did the time (series) go? Estimation of marginal emission factors with autoregressive components

NIAID Data Ecosystem2026-03-12 收录

电力碳排放估计

时序建模

数据链接：

https://data.mendeley.com/datasets/kt9gpd9tbm 数据链接链接失效反馈

官方服务：

资源简介：

time series data on emissions and electricity output

创建时间：

2021-04-27

MNIST预测数据集

视频帧预测

时序建模

该数据集在视频预测与时空序列建模领域具有基准评估价值。它通过 ConvLSTM 模型生成的长达 64 帧的高维数据，为衡量深度学习算法捕捉复杂非线性动力学特征的能力提供了量化依据。在数据科学研究中，该数据集不仅可用于评估模型在减少累积误差（即“预测漂移”）方面的鲁棒性，还可作为训练更高级生成式模型（如生成对抗网络 GANs 或扩散模型）的对照基准。它能够帮助研究人员深入探讨时空解耦、运动一致性以及

国家基础学科公共科学数据中心120

Accounting for time series errors in partially linear model with single- or multiple-run

时序建模

磁共振影像分析

This paper concerns statistical estimation of the partially linear model (PLM) for time course measurements, which are temporally correlated and allow multiple-run for repeated measurements to enhance

NIAID Data Ecosystem70

Candidate models for modeling GDP using ARIMA.

GDP增长预测

时序建模

Background Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country’s economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and p

NIAID Data Ecosystem30

Text-attributed Temporal Graph Benchmark Datasets

时序建模

用户行为分析

These are six real-world temporal graph datasets with text attributes, varying in size and originating from different domains. Specifically, they are collected from culinary recipe feedback, movie rev

NIAID Data Ecosystem80

Whittle Networks datasets

时序建模

深度似然模型

Datasets for paper "Whittle Networks: A Deep Likelihood Model for Time Series" Paper at http://proceedings.mlr.press/v139/yu21c.html Code at...

B2FIND60