Ottertune Change

Ottertune change the _magnitude_compare functions in kats/detetors/cumsum_detection. The original function assume there should be one day between two data points in the time series which isn't conform with ottertune change point detection scenario. We change the code so that now it doesn't make any assumption on time length between two data points.

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

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

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

# 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

License

Kats is licensed under the MIT license.