Description
Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. Time series analysis is an essential component of Data Science and Engineering work at industry, from understanding the key statistics and characteristics, detecting regressions and anomalies, to forecasting future trends. Kats aims to provide the one-stop shop for time series analysis, including detection, forecasting, feature extraction/embedding, multivariate analysis, etc.
Kats is released by Facebook's Infrastructure Data Science team. It is available for download on PyPI.
Important links
- Homepage: https://facebookresearch.github.io/Kats/
- Kats Python package: https://pypi.org/project/kats/0.1.0/
- Facebook Engineering Blog Post: https://engineering.fb.com/2021/06/21/open-source/kats/
- Source code repository: https://github.com/facebookresearch/kats
- Contributing: https://github.com/facebookresearch/Kats/blob/master/CONTRIBUTING.md
- Tutorials: https://github.com/facebookresearch/Kats/tree/master/tutorials
Installation in Python
Kats is on PyPI, so you can use pip to install it.
pip install --upgrade pip
pip install kats
If you need only a small subset of Kats, you can install a minimal version of Kats with
MINIMAL_KATS=1 pip install kats
which omits many dependencies (everything in test_requirements.txt). However, this will disable many functionalities and cause import kats to log warnings. See setup.py for full details and options.
Examples
Here are a few sample snippets from a subset of Kats offerings:
Forecasting
Using Prophet model to forecast the air_passengers data set.
import pandas as pd
from kats.consts import TimeSeriesData
from kats.models.prophet import ProphetModel, ProphetParams
# take `air_passengers` data as an example
air_passengers_df = pd.read_csv(
"../kats/data/air_passengers.csv",
header=0,
names=["time", "passengers"],
)
# convert to TimeSeriesData object
air_passengers_ts = TimeSeriesData(air_passengers_df)
# create a model param instance
params = ProphetParams(seasonality_mode='multiplicative') # additive mode gives worse results
# create a prophet model instance
m = ProphetModel(air_passengers_ts, params)
# fit model simply by calling m.fit()
m.fit()
# make prediction for next 30 month
fcst = m.predict(steps=30, freq="MS")
Detection
Using CUSUM detection algorithm on simulated data set.
# import packages
import numpy as np
import pandas as pd
from kats.consts import TimeSeriesData
from kats.detectors.cusum_detection import CUSUMDetector
# simulate time series with increase
np.random.seed(10)
df_increase = pd.DataFrame(
{
'time': pd.date_range('2019-01-01', '2019-03-01'),
'increase':np.concatenate([np.random.normal(1,0.2,30), np.random.normal(2,0.2,30)]),
}
)
# convert to TimeSeriesData object
timeseries = TimeSeriesData(df_increase)
# run detector and find change points
change_points = CUSUMDetector(timeseries).detector()
TSFeatures
We can extract meaningful features from the given time series data
# Initiate feature extraction class
import pandas as pd
from kats.consts import TimeSeriesData
from kats.tsfeatures.tsfeatures import TsFeatures
# take `air_passengers` data as an example
air_passengers_df = pd.read_csv(
"../kats/data/air_passengers.csv",
header=0,
names=["time", "passengers"],
)
# convert to TimeSeriesData object
air_passengers_ts = TimeSeriesData(air_passengers_df)
# calculate the TsFeatures
features = TsFeatures().transform(air_passengers_ts)
Changelog
Version 0.1.0
- Initial release
Contributors
Kats is a project with several skillful researchers and engineers contributing to it.
Kats is currently maintained by Xiaodong Jiang with major contributions coming from many talented individuals in various forms and means. A non-exhaustive but growing list needs to mention: Sudeep Srivastava, Sourav Chatterjee, Jeff Handler, Rohan Bopardikar, Dawei Li, Yanjun Lin, Yang Yu, Michael Brundage, Caner Komurlu, Rakshita Nagalla, Zhichao Wang, Hechao Sun, Peng Gao, Wei Cheung, Jun Gao, Qi Wang, Morteza Kazemi, Tihamér Levendovszky, Jian Zhang, Ahmet Koylan, Kun Jiang, Aida Shoydokova, Ploy Temiyasathit, Sean Lee, Nikolay Pavlovich Laptev, Peiyi Zhang, Emre Yurtbay, Daniel Dequech, Rui Yan, and William Luo.
License
Kats is licensed under the MIT license.
762 Jan 07, 2023
1.2k Dec 27, 2022
27 Dec 09, 2022
11 Jan 07, 2022
506 Dec 19, 2022
1 Dec 26, 2022
1 Oct 20, 2021
1 Nov 18, 2021
369 Dec 30, 2022
0 Apr 25, 2022
182 Nov 11, 2022
861 Dec 29, 2022
1.2k Jan 07, 2023
1 Feb 13, 2022
1 Nov 16, 2021
7.2k Dec 30, 2022
527 Jan 02, 2023
3 Jul 02, 2022
304 Jan 02, 2023
5 Apr 28, 2022