Trend Detection in Python. Applicable for real-world industry use cases in time series.

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PyTrendy

PyTrendy is a robust solution for identifying and analyzing trends in time series. Unlike other trend detection packages, it considers post-processing. 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.

Quickstart

Install the package from PyPi.

pip install pytrendy

Import pytrendy.

import pytrendy as pt

Load daily time series data. In this case, we're using one of pytrendy's custom examples.

df = pt.load_data('series_synthetic')
print(df)

#             date     abrupt    gradual  gradual-noisy-20
#  0    2025-01-01  19.578066  12.500000         27.514106
#  1    2025-01-02  19.358378  13.421717         -6.620099
#  2    2025-01-03  19.228408  13.474026         22.122134
#  3    2025-01-04  19.727130  13.474026         13.863735
#  4    2025-01-05  20.773716  14.505772          8.884535
#  ..          ...        ...        ...               ...
#  176  2025-06-26   4.718725  20.616883         19.790026
#  177  2025-06-27   4.242065  20.978084         19.181404
#  178  2025-06-28   6.012296  22.449495         -6.563936
#  179  2025-06-29   4.603068  23.486652         48.291088
#  180  2025-06-30   4.435105  22.240260          3.343233

Run trend detection & plot the results.

results = pt.detect_trends(df, date_col='date', value_col='gradual', plot=True)

The results object can be used to summarise, further analyse, and generally inspect the trend detections.

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:
#  -------------------------------------------------------------------------------
#              direction       start         end  days  total_change  change_rank
#  time_index                                                                   
#  9               Down  2025-05-09  2025-06-17    39    -73.253968            1
#  8                 Up  2025-04-02  2025-05-08    36     72.611833            2
#  5                 Up  2025-02-10  2025-03-14    32     24.632035            3
#  7               Down  2025-03-18  2025-04-01    14    -22.721861            4
#  1                 Up  2025-01-02  2025-01-24    22     14.013348            5
#  3               Down  2025-01-25  2025-02-05    11    -13.564214            6
#  4               Flat  2025-02-06  2025-02-09     3           NaN            7
#  6               Flat  2025-03-15  2025-03-17     2           NaN            8
#  10              Flat  2025-06-18  2025-06-29    11           NaN            9 
#  -------------------------------------------------------------------------------

You can directly call the object as a pandas dataframe. By default, it is sorted by change_rank which prioritises long duration and high magnitude of change.

results.segments_df
time_index direction segmenth_length start end trend_class change pct_change days total_change SNR change_rank
9 Down 38 2025-05-09 2025-06-17 gradual -73.253968 -0.805442 39 -73.253968 21.122099 1
8 Up 34 2025-04-02 2025-05-08 gradual 73.687771 3.944243 36 72.611833 21.701162 2
5 Up 22 2025-02-10 2025-03-14 gradual 26.015512 1.974942 32 24.632035 18.871430 3
7 Down 14 2025-03-18 2025-04-01 gradual -22.721861 -0.591909 14 -22.721861 16.762790 4
1 Up 17 2025-01-02 2025-01-24 gradual 14.013348 1.044080 22 14.013348 22.207980 5
3 Down 10 2025-01-25 2025-02-05 gradual -13.564214 -0.554982 11 -13.564214 17.360657 6
4 Flat 9 2025-02-06 2025-02-09 NaN NaN NaN 3 NaN 20.126008 7
6 Flat 4 2025-03-15 2025-03-17 NaN NaN NaN 2 NaN 17.350339 8
10 Flat 13 2025-06-18 2025-06-29 NaN NaN NaN 11 NaN 19.039273 9

Upcoming

  • More DEMO examples.
  • Automated testing in CI/CD pipeline.
  • Full documentation with all features.
  • Customising more options for windows.

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