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kats: kit to analyze time series

Project description

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 Strategy team. It is available for download on PyPI.

Important links

Installation in Python [TODO]

Kats is on PyPI, so you can use pip to install it.

pip install kats

Examples

Here are a few sample snippets from a subset of Kats offerings:

Forecasting

Using Prophet model to forecast the air_passengers data set.

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")
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
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
from kats.tsfeatures.tsfeatures import TsFeatures

features = TsFeatures().transform(air_passengers_ts)

Changelog

Version 0.1 (TODO)

  • Initial release

License

Kats is licensed under the MIT license.

Project details


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