PyTrendy is a robust solution for identifying and analyzing trends in time series. Unlike other trend detection packages, it is robust to noisy & flat segments, and handles for gradual & abrupt trend cases with a high precision. It aims to be the best package for trend detection in python.
Read more in the documentation: russellsb.github.io/pytrendy/main
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.
- 3 Flats.
- 0 Noise.
The best detected trend is Down between dates 2025-05-09 - 2025-06-17
Full Results:
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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 9 NaN
4 Up 2025-02-10 2025-03-14 32 24.632035 3 gradual
5 Flat 2025-03-15 2025-03-17 2 5.660173 7 NaN
6 Down 2025-03-18 2025-04-01 14 -22.721861 4 gradual
7 Up 2025-04-02 2025-05-08 36 72.611833 2 gradual
8 Down 2025-05-09 2025-06-17 39 -73.253968 1 gradual
9 Flat 2025-06-18 2025-06-30 12 3.910534 8 NaN
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