pytrendy 1.0.5

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

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.