xinychen/transdim

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1289 stars · 305 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

transdim is a Python/Jupyter Notebook project providing machine learning models for transportation data imputation and spatiotemporal time series prediction. It implements tensor learning models (e.g., low-rank autoregressive tensor completion) to handle various missing data patterns in traffic sensor data.

Use cases

• impute missing traffic sensor data
• forecast road network traffic states
• handle missing values in time series forecasting
• low-rank tensor completion for spatiotemporal data
• benchmark missing data patterns like random and blockout missing
• learn tensor decomposition models for urban mobility data

When to choose

• you need to impute missing spatiotemporal traffic data with well-studied missing patterns
• you want research-grade Python implementations of tensor completion and forecasting models
• you are working on time series prediction in the presence of missing values
• you need open transportation datasets and reproducible notebooks

When to avoid

• you need a production-ready, pip-installable library with stable APIs
• your data is not spatiotemporal or traffic-related
• you need real-time or streaming imputation at scale
• you want a general-purpose time series forecasting toolkit without a missing-data focus

Facets

library · maturity active

machine-learning data-science nlp machine-learning data-science python cross-platform tensor-completion time-series-forecasting missing-data-imputation spatiotemporal traffic-data jupyter-notebooks research-code transportation

Member repositories

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markdown · JSON · MCP: product_card(name="xinychen/transdim")