facebookresearch/Kats facebookresearch/Kats

  • Stars
    star
    4,387
  • Rank 9,773 (Top 0.2 %)
  • Language
    Python
  • License
    MIT License
  • Created over 3 years ago
  • Updated over 1 year ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
facebookresearch/Kats
github

facebookresearch

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

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

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)

Citing Kats

If you use Kats in your work or research, please use the following BibTeX entry.

@software{Jiang_KATS_2022,
author = {Jiang, Xiaodong and Srivastava, Sudeep and Chatterjee, Sourav and Yu, Yang and Handler, Jeffrey and Zhang, Peiyi and Bopardikar, Rohan and Li, Dawei and Lin, Yanjun and Thakore, Uttam and Brundage, Michael and Holt, Ginger and Komurlu, Caner and Nagalla, Rakshita and Wang, Zhichao and Sun, Hechao and Gao, Peng and Cheung, Wei and Gao, Jun and Wang, Qi and Guerard, Marius and Kazemi, Morteza and Chen, Yulin and Zhou, Chong and Lee, Sean and Laptev, Nikolay and Levendovszky, TihamΓ©r and Taylor, Jake and Qian, Huijun and Zhang, Jian and Shoydokova, Aida and Singh, Trisha and Zhu, Chengjun and Baz, Zeynep and Bergmeir, Christoph and Yu, Di and Koylan, Ahmet and Jiang, Kun and Temiyasathit, Ploy and Yurtbay, Emre},
license = {MIT License},
month = {3},
title = {{Kats}},
url = {https://github.com/facebookresearch/Kats},
version = {0.2.0},
year = {2022}
}

Changelog

Version 0.2.0

  • Forecasting
    • Added global model, a neural network forecasting model
    • Added global model tutorial
    • Consolidated backtesting APIs and some minor bug fixes
  • Detection
    • Added model optimizer for anomaly/ changepoint detection
    • Added evaluators for anomaly/changepoint detection
    • Improved simulators, to build synthetic data and inject anomalies
    • Added new detectors: ProphetTrendDetector, Dynamic Time Warping based detectors
    • Support for meta-learning, to recommend anomaly detection algorithms and parameters for your dataset
    • Standardized API for some of our legacy detectors: OutlierDetector, MKDetector
    • Support for Seasonality Removal in StatSigDetector
  • TsFeatures
    • Added time-based features
  • Others
    • Bug fixes, code coverage improvement, etc.

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, TihamΓ©r Levendovszky, Jian Zhang, Ahmet Koylan, Kun Jiang, Aida Shoydokova, Ploy Temiyasathit, Sean Lee, Nikolay Pavlovich Laptev, Peiyi Zhang, Emre Yurtbay, Daniel Dequech, Rui Yan, William Luo, Marius Guerard, Pietari Pulkkinen, Uttam Thakore, Trisha Singh, Huijun Qian, Chengjun Zhu, Di Yu, Zeynep Erkin Baz, and Christoph Bergmeir.

License

Kats is licensed under the MIT license.

More Repositories

1

llama

Inference code for LLaMA models
Python
44,989
star
2

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Jupyter Notebook
42,134
star
3

Detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Python
25,771
star
4

fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Python
25,718
star
5

detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
Python
25,567
star
6

fastText

Library for fast text representation and classification.
HTML
24,973
star
7

faiss

A library for efficient similarity search and clustering of dense vectors.
C++
24,035
star
8

audiocraft

Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.
Python
19,691
star
9

codellama

Inference code for CodeLlama models
Python
13,303
star
10

sam2

The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Jupyter Notebook
11,906
star
11

detr

End-to-End Object Detection with Transformers
Python
11,076
star
12

seamless_communication

Foundational Models for State-of-the-Art Speech and Text Translation
Jupyter Notebook
10,584
star
13

ParlAI

A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
Python
10,085
star
14

maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
Python
9,104
star
15

pifuhd

High-Resolution 3D Human Digitization from A Single Image.
Python
8,923
star
16

hydra

Hydra is a framework for elegantly configuring complex applications
Python
8,550
star
17

nougat

Implementation of Nougat Neural Optical Understanding for Academic Documents
Python
8,088
star
18

AnimatedDrawings

Code to accompany "A Method for Animating Children's Drawings of the Human Figure"
Python
8,032
star
19

ImageBind

ImageBind One Embedding Space to Bind Them All
Python
7,630
star
20

llama-recipes

Scripts for fine-tuning Llama2 with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization & question answering. Supporting a number of candid inference solutions such as HF TGI, VLLM for local or cloud deployment.Demo apps to showcase Llama2 for WhatsApp & Messenger
Jupyter Notebook
7,402
star
21

pytorch3d

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
Python
7,322
star
22

dinov2

PyTorch code and models for the DINOv2 self-supervised learning method.
Jupyter Notebook
7,278
star
23

DensePose

A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body
Jupyter Notebook
6,547
star
24

pytext

A natural language modeling framework based on PyTorch
Python
6,357
star
25

DiT

Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"
Python
5,995
star
26

metaseq

Repo for external large-scale work
Python
5,947
star
27

demucs

Code for the paper Hybrid Spectrogram and Waveform Source Separation
Python
5,886
star
28

SlowFast

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
Python
5,678
star
29

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377
Python
5,495
star
30

mmf

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)
Python
5,235
star
31

ConvNeXt

Code release for ConvNeXt model
Python
4,971
star
32

dino

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
Python
4,830
star
33

AugLy

A data augmentations library for audio, image, text, and video.
Python
4,739
star
34

DrQA

Reading Wikipedia to Answer Open-Domain Questions
Python
4,374
star
35

sapiens

High-resolution models for human tasks.
Python
4,340
star
36

xformers

Hackable and optimized Transformers building blocks, supporting a composable construction.
Python
4,191
star
37

moco

PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722
Python
4,035
star
38

StarSpace

Learning embeddings for classification, retrieval and ranking.
C++
3,856
star
39

lingua

Meta Lingua: a lean, efficient, and easy-to-hack codebase to research LLMs.
Python
3,829
star
40

fairseq-lua

Facebook AI Research Sequence-to-Sequence Toolkit
Lua
3,765
star
41

nevergrad

A Python toolbox for performing gradient-free optimization
Python
3,446
star
42

deit

Official DeiT repository
Python
3,425
star
43

dlrm

An implementation of a deep learning recommendation model (DLRM)
Python
3,417
star
44

ReAgent

A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)
Python
3,395
star
45

LASER

Language-Agnostic SEntence Representations
Python
3,308
star
46

VideoPose3D

Efficient 3D human pose estimation in video using 2D keypoint trajectories
Python
3,294
star
47

PyTorch-BigGraph

Generate embeddings from large-scale graph-structured data.
Python
3,238
star
48

deepmask

Torch implementation of DeepMask and SharpMask
Lua
3,113
star
49

MUSE

A library for Multilingual Unsupervised or Supervised word Embeddings
Python
3,094
star
50

vissl

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.
Jupyter Notebook
3,038
star
51

pytorchvideo

A deep learning library for video understanding research.
Python
2,885
star
52

XLM

PyTorch original implementation of Cross-lingual Language Model Pretraining.
Python
2,763
star
53

audio2photoreal

Code and dataset for photorealistic Codec Avatars driven from audio
Python
2,696
star
54

ijepa

Official codebase for I-JEPA, the Image-based Joint-Embedding Predictive Architecture. First outlined in the CVPR paper, "Self-supervised learning from images with a joint-embedding predictive architecture."
Python
2,670
star
55

jepa

PyTorch code and models for V-JEPA self-supervised learning from video.
Python
2,646
star
56

habitat-sim

A flexible, high-performance 3D simulator for Embodied AI research.
C++
2,621
star
57

co-tracker

CoTracker is a model for tracking any point (pixel) on a video.
Jupyter Notebook
2,564
star
58

hiplot

HiPlot makes understanding high dimensional data easy
TypeScript
2,481
star
59

fairscale

PyTorch extensions for high performance and large scale training.
Python
2,319
star
60

encodec

State-of-the-art deep learning based audio codec supporting both mono 24 kHz audio and stereo 48 kHz audio.
Python
2,313
star
61

InferSent

InferSent sentence embeddings
Jupyter Notebook
2,264
star
62

Pearl

A Production-ready Reinforcement Learning AI Agent Library brought by the Applied Reinforcement Learning team at Meta.
Python
2,193
star
63

pyrobot

PyRobot: An Open Source Robotics Research Platform
Python
2,109
star
64

darkforestGo

DarkForest, the Facebook Go engine.
C
2,108
star
65

ELF

An End-To-End, Lightweight and Flexible Platform for Game Research
C++
2,089
star
66

pycls

Codebase for Image Classification Research, written in PyTorch.
Python
2,053
star
67

esm

Evolutionary Scale Modeling (esm): Pretrained language models for proteins
Python
2,026
star
68

frankmocap

A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
Python
1,972
star
69

video-nonlocal-net

Non-local Neural Networks for Video Classification
Python
1,931
star
70

SentEval

A python tool for evaluating the quality of sentence embeddings.
Python
1,930
star
71

habitat-lab

A modular high-level library to train embodied AI agents across a variety of tasks and environments.
Python
1,867
star
72

ResNeXt

Implementation of a classification framework from the paper Aggregated Residual Transformations for Deep Neural Networks
Lua
1,863
star
73

SparseConvNet

Submanifold sparse convolutional networks
C++
1,847
star
74

schedule_free

Schedule-Free Optimization in PyTorch
Python
1,842
star
75

chameleon

Repository for Meta Chameleon, a mixed-modal early-fusion foundation model from FAIR.
Python
1,811
star
76

swav

PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882
Python
1,790
star
77

TensorComprehensions

A domain specific language to express machine learning workloads.
C++
1,747
star
78

Mask2Former

Code release for "Masked-attention Mask Transformer for Universal Image Segmentation"
Python
1,638
star
79

fvcore

Collection of common code that's shared among different research projects in FAIR computer vision team.
Python
1,623
star
80

TransCoder

Public release of the TransCoder research project https://arxiv.org/pdf/2006.03511.pdf
Python
1,611
star
81

poincare-embeddings

PyTorch implementation of the NIPS-17 paper "PoincarΓ© Embeddings for Learning Hierarchical Representations"
Python
1,587
star
82

votenet

Deep Hough Voting for 3D Object Detection in Point Clouds
Python
1,563
star
83

pytorch_GAN_zoo

A mix of GAN implementations including progressive growing
Python
1,554
star
84

ClassyVision

An end-to-end PyTorch framework for image and video classification
Python
1,552
star
85

deepcluster

Deep Clustering for Unsupervised Learning of Visual Features
Python
1,544
star
86

higher

higher is a pytorch library allowing users to obtain higher order gradients over losses spanning training loops rather than individual training steps.
Python
1,524
star
87

UnsupervisedMT

Phrase-Based & Neural Unsupervised Machine Translation
Python
1,496
star
88

consistent_depth

We estimate dense, flicker-free, geometrically consistent depth from monocular video, for example hand-held cell phone video.
Python
1,479
star
89

ConvNeXt-V2

Code release for ConvNeXt V2 model
Python
1,454
star
90

Detic

Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".
Python
1,446
star
91

end-to-end-negotiator

Deal or No Deal? End-to-End Learning for Negotiation Dialogues
Python
1,368
star
92

DomainBed

DomainBed is a suite to test domain generalization algorithms
Python
1,355
star
93

multipathnet

A Torch implementation of the object detection network from "A MultiPath Network for Object Detection" (https://arxiv.org/abs/1604.02135)
Lua
1,349
star
94

CommAI-env

A platform for developing AI systems as described in A Roadmap towards Machine Intelligence - http://arxiv.org/abs/1511.08130
1,324
star
95

theseus

A library for differentiable nonlinear optimization
Python
1,306
star
96

DPR

Dense Passage Retriever - is a set of tools and models for open domain Q&A task.
Python
1,292
star
97

CrypTen

A framework for Privacy Preserving Machine Learning
Python
1,283
star
98

denoiser

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
Python
1,272
star
99

DeepSDF

Learning Continuous Signed Distance Functions for Shape Representation
Python
1,191
star
100

TimeSformer

The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"
Python
1,172
star