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PyTrendy

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PyTrendy is a robust solution for identifying and analysing trends in time series. Unlike other trend detection packages, it is robust to noisy and flat segments, and handles gradual and abrupt trend cases with high precision. It aims to be the best package for trend detection in Python.

Read more in the documentation: russellsb.github.io/pytrendy/main

Why PyTrendy?

Most time series tools give you either a "trend component" (via decomposition) or "changepoints" (the moments of shift). PyTrendy is built for automated trend detection, answering what trends existed, how strong were they, and when did they start and end?

  • Beyond step changes - ruptures is the gold standard for abrupt shifts, but it doesn't handle gradual slope changes (digital marketing, stocks, energy). PyTrendy detects both in a single run.
  • The flat/noise problem - closest peers (pytrendseries, trendet, tstrends) over-fit trends on flat or noisy periods. PyTrendy's signal-processing and post-processing logic ensures trends are only detected when they are precise and valid.
  • Strategic value - where dozens of time series interact, knowing how they align or confound at specific times is invaluable for experiment design.

Features

Quickstart

Install the package from PyPi.

pip install pytrendy

Import pytrendy, and apply trend detection on daily time series data.

import pytrendy as pt
df = pt.load_data('series_synthetic')
results = pt.detect_trends(df, date_col='date', value_col='gradual', plot=True)
results.print_summary()


Detected: 
- 3 Uptrends. 
- 3 Downtrends.
- 2 Flats.
- 0 Noise.

The best detected trend is Down between dates 2025-05-09 - 2025-06-17

Full Results:
-------------------------------------------------------------------------------
            direction       start         end  days  total_change  change_rank trend_class
time_index                                                                               
1                 Up  2025-01-02  2025-01-24    22     14.013348            5     gradual
2               Down  2025-01-25  2025-02-05    11    -13.564214            6     gradual
3               Flat  2025-02-06  2025-02-09     3     -1.168831            8         NaN
4                 Up  2025-02-10  2025-03-17    35     26.190476            3     gradual
5               Down  2025-03-18  2025-04-01    14    -22.721861            4     gradual
6                 Up  2025-04-02  2025-05-08    36     72.611833            2     gradual
7               Down  2025-05-09  2025-06-17    39    -73.253968            1     gradual
8               Flat  2025-06-18  2025-06-30    12      3.910534            7         NaN
-------------------------------------------------------------------------------

Explore the strongest uptrends:

results.filter_segments(direction='Up', sort_by='change_rank')[:3]
time_index direction start end trend_class change pct_change days total_change SNR change_rank
7 Up 2025-04-02 2025-05-08 gradual 72.61 367.50% 36 72.61 21.70 2
4 Up 2025-02-10 2025-03-17 gradual 26.19 179.93% 35 26.19 18.61 3
1 Up 2025-01-02 2025-01-24 gradual 14.01 104.41% 22 14.01 22.21 5

filter_segments ranks segments by magnitude (change_rank). See the API reference for all filter and sort options.

For the full per-segment metrics table, use results.df.

For more examples on interpreting the results, see Detect Gradual Trends.

Project links

Data verified by PyPI on Aug 24, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
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  • Released:
Stable release available (1.3.1)

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for BarshaDeb from gravatar.com BarshaDeb Avatar for russellsb from gravatar.com russellsb

Credits

Author/Maintainer: Russell Sammut Bonnici

GitHub Statistics

Data verified by PyPI on Aug 24, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT License)

Requires

Python >=3.10

Provides Extra

dev docs

Tags

trend detection time series time series analysis signal processing uptrend downtrend changepoint detection trend classification stock analysis marketing analytics causal inference

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