cuge1995 / Awesome Time Series
Labels
Projects that are alternatives of or similar to Awesome Time Series
List of state of the art papers, code, and other resources focus on time series forecasting.
Table of Contents
- M4 competition
- Kaggle time series competition
- Papers
- Conferences
- Theory-Resource
- Code Resource
- Datasets
M4-competition
papers
- The M4 Competition: 100,000 time series and 61 forecasting methods
- A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
- Weighted ensemble of statistical models
- FFORMA: Feature-based forecast model averaging
Kaggle-time-series-competition
- Walmart Store Sales Forecasting (2014)
- Walmart Sales in Stormy Weather (2015)
- Rossmann Store Sales (2015)
- Wikipedia Web Traffic Forecasting (2017)
- Corporación Favorita Grocery Sales Forecasting (2018)
- Recruit Restaurant Visitor Forecasting (2018)
- COVID19 Global Forecasting (2020)
Papers
2021
-
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
AAAI 2021 best paper
- Zhou, et al.
- Code
-
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
AAAI 2021
- Ye, et al.
- Code
2020
-
Adversarial Sparse Transformer for Time Series Forecasting
NeurIPS 2020
- Wu, et al.
- Code not yet
-
Benchmarking Deep Learning Interpretability in Time Series Predictions
NeurIPS 2020
- Ismail, et al.
- [Code]
-
Deep reconstruction of strange attractors from time series
NeurIPS 2020
- Gilpin, et al.
- [Code]
-
Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline
classification
- Tang, et al.
- [Code]
-
Active Model Selection for Positive Unlabeled Time Series Classification
- Liang, et al.
- [Code]
-
Unsupervised Phase Learning and Extraction from Quasiperiodic Multidimensional Time-series Data
- Prayook, et al.
- [Code]
-
Connecting the Dots: Multivariate Time Series Forecasting withGraph Neural Networks
- Wu, et al.
- [Code]
-
- Löning, et al.
- Code not yet
-
RobustTAD: Robust Time Series Anomaly Detection viaDecomposition and Convolutional Neural Networks
- Gao, et al.
- Code not yet
-
Neural Controlled Differential Equations forIrregular Time Series
- Patrick Kidger, et al.
University of Oxford
- [Code]
-
Time Series Forecasting With Deep Learning: A Survey
- Lim, et al.
- Code not yet
-
Neural forecasting: Introduction and literature overview
- Benidis, et al.
Amazon Research
- Code not yet.
-
Time Series Data Augmentation for Deep Learning: A Survey
- Wen, et al.
- Code not yet
-
Modeling time series when some observations are zero
Journal of Econometrics 2020
- Andrew Harveyand Ryoko Ito.
- Code not yet
-
Meta-learning framework with applications to zero-shot time-series forecasting
- Oreshkin, et al.
- Code not yet.
-
Harmonic Recurrent Process for Time Series Forecasting
- Shao-Qun Zhang and Zhi-Hua Zhou.
- Code not yet.
-
Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting
AAAI 2020
- QIQUAN SHI, et al.
- Code not yet
-
Learnings from Kaggle's Forecasting Competitions
- Casper Solheim Bojer, et al.
- Code not yet.
-
An Industry Case of Large-Scale Demand Forecasting of Hierarchical Components
- Rodrigo Rivera-Castro, et al.
- Code not yet.
-
Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
- Kashif Rasul, et al.
- Code not yet.
-
- Joel Janek Dabrowski, et al.
- Code not yet.
-
Anomaly detection for Cybersecurity: time series forecasting and deep learning
Good review about forecasting
- Giordano Colò.
- Code not yet.
-
Event-Driven Continuous Time Bayesian Networks
- Debarun Bhattacharjya, et al.
Research AI, IBM
- Code not yet.
Conferences
Theory-Resource
Code-Resource
-
Seglearn: A Python Package for Learning Sequences and Time Series
-
PyTorch Forecasting: A Python Package for time series forecasting with PyTorch
-
List of tools & datasets for anomaly detection on time-series data
-
A scikit-learn compatible Python toolbox for machine learning with time series
-
A statistical library designed to fill the void in Python's time series analysis capabilities
-
RNN based Time-series Anomaly detector model implemented in Pytorch
-
A Python toolkit for rule-based/unsupervised anomaly detection in time series
-
A curated list of awesome time series databases, benchmarks and papers
-
Matrix Profile analysis methods in Python for clustering, pattern mining, and anomaly detection