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

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PyTrendy

alt-text

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')
display(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

181 rows × 4 columns

Run trend detection & plot the results.

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

alt-text

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.

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

By default, trends are sorted by there change_rank. This is ranks higher duration and magnitude of change to describe a trend's gravity erlative to others. You can sort by time index instead with filter_segments.

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

As well as filter only for a specific direction.

results.filter_segments(direction='Up')
time_index direction segmenth_length start end trend_class change pct_change days total_change SNR change_rank
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
1 Up 17 2025-01-02 2025-01-24 gradual 14.013348 1.044080 22 14.013348 22.207980 5

Upcoming

  • More DEMO examples.
  • Automated testing in CI/CD pipeline.
  • Documentation, moving more verbose tutorials to there.

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