Timeserio

timeserio is the missing link between pandas, scikit-learn and keras. It simplifies building end-to-end deep learning models - from a DataFrame through feature pipelines to multi-stage models with shared layers. While initially developed for tackling time series problems, it has since been used as a versatile tool for rapid ML model development and deployment.

Features

• Enable encapsulated, maintainable and reusable deep learning models

• Feed data from pandas through scikit-learn feature pipelines to multiple neural network inputs

• Manage complex architectures, layer sharing, partial freezing and re-training

• Provide collection of extensible building blocks with emphasis on time series problems

Overview

• Getting Started

Examples

• Building a Neural Movie Recommender System
• Autoencoders and multi-stage training for MNIST classification
• Modelling Solar generation across Multiple Sites - Part 1
• Modelling Solar generation across Multiple Sites - Part 2
• GroupedPipeline: applying a transformer per category

API reference

• API reference

Indices and tables

• Index

• Module Index

• Search Page