ai4ts
AI for Time Series
Decision gist · record as of 2026-08-14
Yes. AI4TS is actively maintained, carries no security vulnerabilities, and is licensed permissively under Apache 2.0. It has low install friction and supports current Python versions. Install it if you work with incomplete or irregularly sampled time-series data and need a framework to handle imputation, classification, clustering, or forecasting. The lack of runtime dependencies is a practical advantage. Verify the API documentation and examples match your specific use case before committing to it in production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with no runtime dependencies.
- The package is marked as Production/Stable and has been actively maintained with a recent commit on 2026-03-14.
- Supports Python 3.8 through 3.11.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache License Version 2.0, a permissive license allowing commercial and derivative use with minimal restrictions. You must include a copy of the license and note any modifications to the source code.
last release 2024-09-14 (699 days) · last repo commit 2026-03-14 · 13 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 117,433 downloads/mo, #12,162 on PyPI
Alternatives
Verify before relying
pip install ai4ts
import ai4ts
# Framework ready for time-series model building- What specific neural network architectures or algorithms are included in the framework
- Whether the package includes pre-trained models or requires training from scratch
- API documentation and examples beyond the homepage
- Performance characteristics on large-scale or real-time time-series data
What it is and what it does
AI4TS is a Python framework for building artificial intelligence models that analyze time-series data. It is designed to handle real-world time-series challenges like missing values, irregular sampling intervals, and incomplete observations—situations common in healthcare, sensor networks, and financial data. The framework supports multiple analysis tasks: imputation and interpolation to fill gaps, classification to categorize sequences, clustering to group similar patterns, and forecasting to predict future values.
The package is positioned as an application framework for researchers and developers working with time-series machine learning. It has no external runtime dependencies, making installation straightforward. It targets Python 3.8 and later and is classified as Production/Stable, indicating it has reached a mature state suitable for production use. The framework appears to integrate neural network approaches with time-series-specific handling.
Use it for
- Fill missing values in sensor or IoT data streams before feeding them into downstream analytics pipelines
- Classify medical time-series records (e.g., ECG, EEG) despite gaps or irregular sampling in the raw signal
- Forecast future values in financial or weather time-series with incomplete historical observations
- Cluster similar time-series patterns in large datasets to identify behavioral groups or anomalies
- Preprocess irregularly sampled time-series data for use in standard machine learning models
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
AI4TS is actively maintained, carries no security vulnerabilities, and is licensed permissively under Apache 2.0. It has low install friction and supports current Python versions. Install it if you work with incomplete or irregularly sampled time-series data and need a framework to handle imputation, classification, clustering, or forecasting. The lack of runtime dependencies is a practical advantage. Verify the API documentation and examples match your specific use case before committing to it in production.
Install
ai4ts on PyPI
Before you install
Low install friction with no runtime dependencies. The package is marked as Production/Stable and has been actively maintained with a recent commit on 2026-03-14. Supports Python 3.8 through 3.11.
License in practice
Licensed under Apache License Version 2.0, a permissive license allowing commercial and derivative use with minimal restrictions. You must include a copy of the license and note any modifications to the source code.
Quickstart
pip install ai4ts
import ai4ts
# Framework ready for time-series model buildingVerify before relying
- What specific neural network architectures or algorithms are included in the framework
- Whether the package includes pre-trained models or requires training from scratch
- API documentation and examples beyond the homepage
- Performance characteristics on large-scale or real-time time-series data
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 699 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 117,433 / month, #12,162 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Libraries :: Application Frameworks |
Evidence: ai4ts-0.0.3-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “incomplete time series analysis”
- ai4tsAI4TS provides a framework for building machine learning models that…
- pypotsPyPOTS provides machine learning algorithms for time series with…
- benchpotsBenchPOTS provides standardized preprocessing pipelines and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also pypots · time-aware-imputer · tsfresh · benchpots · pygrinder · tsdb · tslearn · sktime · neuralforecast · nfoursid