2 个仓库
Awesome GitHub RepositoriesFlat Dictionary Serialization
Conversion of nested object structures into single-level dictionaries using keyed paths.
Distinct from Model-to-Dictionary Serialization: Distinct from model-to-dictionary serialization as it focuses on structural flattening for general object preservation.
Explore 2 awesome GitHub repositories matching data & databases · Flat Dictionary Serialization. Refine with filters or upvote what's useful.
Awesome Flat Dictionary Serialization GitHub Repositories
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- awslabs/gluonts
awslabs/gluonts
5,199在 GitHub 上查看GluonTS 是一个概率时间序列库和深度学习预测框架。它提供了一套工具包，用于构建、训练和评估神经网络架构，通过将未来值预测为概率分布来量化不确定性。 该项目的独特之处在于支持零样本（zero-shot）预测，并集成了多种建模方法，包括深度概率神经网络以及对 Prophet 和 R forecast 等外部统计库的封装。它实现了因果卷积和可逆残差网络等专门的架构原语，以防止信息泄露并将潜在表示映射为有效的概率分布。 该框架涵盖了全面的数据工程功能，包括时间序列缩放、双射变换和分层建模。它利用 Apache Arrow 和 Parquet 进行高性能数据集流式传输和随机访问管理。在模型评估方面，它包含一套评估套件，使用分位数损失（quantile loss）和连续排名概率分数（CRPS）等指标来衡量预测准确性和概率覆盖率。 该库支持通过集成 Amazon SageMaker 进行模型部署。
Converts nested object structures into single-level dictionaries with dotted keys to simplify storage.
Pythonartificial-intelligenceawsdata-science - awslabs/gluon-ts
awslabs/gluon-ts
5,200在 GitHub 上查看GluonTS is a framework for probabilistic time series forecasting, designed to predict future values as probability distributions with confidence intervals. It supports both traditional model training and zero-shot forecasting, where pretrained models generate predictions for new series without additional training. The project distinguishes itself by integrating a wide variety of forecasting approaches into a unified workflow. This includes deep learning architectures such as recurrent neural networks and causal convolutions, as well as the integration of external statistical models, the Proph
Converts nested objects into single-level dictionaries with keyed paths to preserve hierarchical structure during serialization.
Python
