BirchKwok/PipelineTS
The simplest time series forecasting tool, supporting time series data preprocessing, feature engineering, model training, model evaluation, model prediction, etc. Based on spinesTS.
This tool helps data analysts and domain experts forecast future trends based on historical time series data. You input your past observations, and it automatically processes the data, selects the best forecasting model from a wide range of options, and provides future predictions along with confidence intervals. This is ideal for anyone who needs to predict things like sales, stock prices, energy consumption, or sensor readings without deep machine learning expertise.
Available on PyPI.
Use this if you need accurate, robust time series forecasts and want an automated system to handle data preparation, model selection, and provide clear prediction intervals.
Not ideal if you only need very basic forecasting methods or prefer to manually build and fine-tune every aspect of your model from scratch.
Stars
11
Forks
1
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Feb 16, 2026
Commits (30d)
0
Dependencies
11
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