Unofficial Implementation of RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019)

Overview

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series (AAAI 2019)

This repository contains python (3.5.2) implementation of RobustSTL (paper) .

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting.
RobustSTL extract trend using LAD loss with sparse regularization and non-local seasonal filtering.
Compared to previous approaches (such as traditional STL), RobustSTL has advantages on

  1. Ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder
  2. robustness of data with anomalies
  3. applicability on time series with long seasonality period.

Requirments & Run

First, install some required libraries using pip.

pip3 install -r requirments.txt
python3 main.py

Sample Results

We generate a synthetic sample (sample_generator.py) and decompose it into trend, seasonality, and remainder. In run_example.ipynb, I attach the example codes to use RobustSTL and the outputs.

Codes

  • main.py : run example code
  • RobustSTL.py: contains RobustSTL algorithm and each process.
  • utils.py: common utility functions
  • sample_generator.py: generation codes of synthetic sample.
  • l1.py : optimizer of l1 norm approximation (source: cvxopt)

Arugments of RobustSTL

  • input : input series
  • season_len : length of seasonal period
  • reg1 : first order regularization parameter for trend extraction
  • reg2 : second order regularization parameter for trend extraction
  • K : number of past season samples in seasonality extraction
  • H : number of neighborhood in seasonality extraction
  • dn1, dn2 : hyperparameter of bilateral filter in denoising step.
  • ds1, ds2 : hyperparameter of bilarteral filter in seasonality extraction step.

Shape of input sample

Basically, RobustSTL is for univariate time series sample.
However, this codes are available on multi-variate time series sample. (It apply the algorithm to each series, using multiprocessing) Each series have to have same time length.

  • Univariate Time Series: [Time] or [Time,1]
  • Multivariate Time Series: [N, Time] or [N, Time, 1]

Etc

The original paper has wrong notation in seasonality extraction.
The difference is log

Owner
Doyup Lee
Machine Learning Researcher
Doyup Lee
Library to enable Bayesian active learning in your research or labeling work.

Bayesian Active Learning (BaaL) BaaL is an active learning library developed at ElementAI. This repository contains techniques and reusable components

ElementAI 687 Dec 25, 2022
Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

CSE-Autoloss Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models

Peidong Liu(刘沛东) 54 Dec 17, 2022
Lightweight tool to perform MITM attack on local network

ARPSpy - A lightweight tool to perform MITM attack Using many library to perform ARP Spoof and auto-sniffing HTTP packet containing credential. (Never

MinhItachi 8 Aug 28, 2022
UMT is a unified and flexible framework which can handle different input modality combinations, and output video moment retrieval and/or highlight detection results.

Unified Multi-modal Transformers This repository maintains the official implementation of the paper UMT: Unified Multi-modal Transformers for Joint Vi

Applied Research Center (ARC), Tencent PCG 84 Jan 04, 2023
A New Open-Source Off-road Environment for Benchmark Generalization of Autonomous Driving

A New Open-Source Off-road Environment for Benchmark Generalization of Autonomous Driving Isaac Han, Dong-Hyeok Park, and Kyung-Joong Kim IEEE Access

13 Dec 27, 2022
Dungeons and Dragons randomized content generator

Component based Dungeons and Dragons generator Supports Entity/Monster Generation NPC Generation Weapon Generation Encounter Generation Environment Ge

Zac 3 Dec 04, 2021
Training deep models using anime, illustration images.

animeface deep models for anime images. Datasets anime-face-dataset Anime faces collected from Getchu.com. Based on Mckinsey666's dataset. 63.6K image

Tomoya Sawada 61 Dec 25, 2022
Subgraph Based Learning of Contextual Embedding

SLiCE Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks Dataset details: We use four public benchmark da

Pacific Northwest National Laboratory 27 Dec 01, 2022
A 1.3B text-to-image generation model trained on 14 million image-text pairs

minDALL-E on Conceptual Captions minDALL-E, named after minGPT, is a 1.3B text-to-image generation model trained on 14 million image-text pairs for no

Kakao Brain 604 Dec 14, 2022
Codes for our paper "SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge" (EMNLP 2020)

SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge Introduction SentiLARE is a sentiment-aware pre-trained language

74 Dec 30, 2022
FID calculation with proper image resizing and quantization steps

clean-fid: Fixing Inconsistencies in FID Project | Paper The FID calculation involves many steps that can produce inconsistencies in the final metric.

Gaurav Parmar 606 Jan 06, 2023
Public Implementation of ChIRo from "Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations"

Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations This directory contains the model architectures and experimental

35 Dec 05, 2022
Test-Time Personalization with a Transformer for Human Pose Estimation, NeurIPS 2021

Transforming Self-Supervision in Test Time for Personalizing Human Pose Estimation This is an official implementation of the NeurIPS 2021 paper: Trans

41 Nov 28, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Master Docs License Apache MXNet (incubating) is a deep learning framework designed for both efficiency an

ROCm Software Platform 29 Nov 16, 2022
Code for the paper Hybrid Spectrogram and Waveform Source Separation

Demucs Music Source Separation This is the 3rd release of Demucs (v3), featuring hybrid source separation. For the waveform only Demucs (v2): Go this

Meta Research 4.8k Jan 04, 2023
Official codebase for Pretrained Transformers as Universal Computation Engines.

universal-computation Overview Official codebase for Pretrained Transformers as Universal Computation Engines. Contains demo notebook and scripts to r

Kevin Lu 210 Dec 28, 2022
Self Driving RC Car Code

Derp Learning Derp Learning is a Python package that collects data, trains models, and then controls an RC car for track racing. Hardware You will nee

Not Karol 39 Dec 07, 2022
A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset.

A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset. This repo contains scripts to train RL agents to navigate the closed world and collect vi

MUGEN 11 Oct 22, 2022
git《USD-Seg:Learning Universal Shape Dictionary for Realtime Instance Segmentation》(2020) GitHub: [fig2]

USD-Seg This project is an implement of paper USD-Seg:Learning Universal Shape Dictionary for Realtime Instance Segmentation, based on FCOS detector f

Ruolin Ye 80 Nov 28, 2022
An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data

GLOM TensorFlow This Python package attempts to implement GLOM in TensorFlow, which allows advances made by several different groups transformers, neu

Rishit Dagli 32 Feb 21, 2022