awesome-time-series
list of papers, code, and other resources
https://github.com/cuge1995/awesome-time-series
Last synced: about 2 hours ago
JSON representation
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Papers
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2020
- Adversarial Sparse Transformer for Time Series Forecasting
- Benchmarking Deep Learning Interpretability in Time Series Predictions
- Deep reconstruction of strange attractors from time series
- Active Model Selection for Positive Unlabeled Time Series Classification
- Unsupervised Phase Learning and Extraction from Quasiperiodic Multidimensional Time-series Data
- Connecting the Dots: Multivariate Time Series Forecasting withGraph Neural Networks
- Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study
- RobustTAD: Robust Time Series Anomaly Detection viaDecomposition and Convolutional Neural Networks
- Neural Controlled Differential Equations forIrregular Time Series
- Time Series Forecasting With Deep Learning: A Survey
- Neural forecasting: Introduction and literature overview
- Anomaly detection for Cybersecurity: time series forecasting and deep learning
- Event-Driven Continuous Time Bayesian Networks
- Time Series Data Augmentation for Deep Learning: A Survey
- Modeling time series when some observations are zero
- Meta-learning framework with applications to zero-shot time-series forecasting
- Harmonic Recurrent Process for Time Series Forecasting
- Learnings from Kaggle's Forecasting Competitions
- An Industry Case of Large-Scale Demand Forecasting of Hierarchical Components
- Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
- ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting
- Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting
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- Active Model Selection for Positive Unlabeled Time Series Classification
- Modeling time series when some observations are zero
- Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline
- Neural forecasting: Introduction and literature overview
- RobustTAD: Robust Time Series Anomaly Detection viaDecomposition and Convolutional Neural Networks
- Neural Controlled Differential Equations forIrregular Time Series
- Time Series Forecasting With Deep Learning: A Survey
- Time Series Data Augmentation for Deep Learning: A Survey
- Meta-learning framework with applications to zero-shot time-series forecasting
- Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
- ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting
- Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting
- Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study
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2023
- MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting
- Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
- Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting
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- SAITS: Self-Attention-based Imputation for Time Series
- A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
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2021
- A machine learning approach for forecasting hierarchical time series
- Probabilistic Transformer For Time Series Analysis
- Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
- CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
- Variational Inference for Continuous-Time Switching Dynamical Systems
- MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
- Online false discovery rate control for anomaly detection in time series
- Adjusting for Autocorrelated Errors in Neural Networks for Time Series
- Whittle Networks: A Deep Likelihood Model for Time Series
- Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
- Long Horizon Forecasting With Temporal Point Processes
- Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
- Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
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- Deep Learning for Time Series Forecasting: A Survey
- A machine learning approach for forecasting hierarchical time series
- Deep Explicit Duration Switching Models for Time Series
- Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
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2024
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- UniTS: Building a Unified Time Series Model
- UniTS: Building a Unified Time Series Model
- TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
- Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
- TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
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2022
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Conferences
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Theory-Resource
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2020
- Time Series Analysis, MIT
- Time Series Forecasting, Udacity
- Practical Time Series Analysis, Cousera
- Sequences, Time Series and Prediction
- Intro to Time Series Analysis in R, Cousera
- Anomaly Detection in Time Series Data with Keras, Corsera
- Applying Data Analytics in Finance, Coursera
- Time Series Forecasting using Python
- STAT 510: Applied Time Series Analysis, PSU
- Policy Analysis Using Interrupted Time Series, edx
- Time Series Forecasting in Python
- time-series-transformers-review
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Code-Resource
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2020
- Predicting/hypothesizing the findings of the M4 Competition
- Time Series Forecasting Best Practices & Examples
- PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
- FOST from microsoft
- pyWATTS: Python Workflow Automation Tool for Time-Series
- Seglearn: A Python Package for Learning Sequences and Time Series
- tsflex: Flexible Time Series Processing & Feature Extraction
- cesium: Open-Source Platform for Time Series Inference
- A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, n-beats, gan, kalman-filter
- Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series
- PyFlux
- HyperTS: A Full-Pipeline Automated Time Series Analysis Toolkit
- List of tools & datasets for anomaly detection on time-series data
- python packages for time series analysis
- plotly-resampler: Visualize large time series data with plotly.py
- time series visualization tools
- 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
- ARCH models in Python
- 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
- Flow Forecast: A deep learning framework for time series forecasting, classification and anomaly detection built in PyTorch
- Predicting/hypothesizing the findings of the M4 Competition
- A scikit-learn compatible Python toolbox for machine learning with time series
- PyTorch Forecasting: A Python Package for time series forecasting with PyTorch
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Datasets
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2020
- U.S. Air Pollution Data
- U.S. Chronic Disease Data
- Air quality from UCI
- Youth Tobacco Survey Data
- Singapore Population
- Airlines Delay
- Airplane Crashes
- Electricity dataset from UCI
- Traffic dataset from UCI
- City of Baltimore Crime Data
- Discover The Menu
- Global Climate Change Data
- Global Health Nutrition Data
- Beijing PM2.5 Data Set
- Government Finance Statistics
- Airline Passengers dataset
- Historical Public Debt Data
- Kansas City Crime Data
- NYC Crime Data
- TSDB: A Python Toolbox to Ease Loading Open-Source Time-Series Datasets (supporting 119 datasets)
- SkyCam: A Dataset of Sky Images and their Irradiance values
- Air quality from UCI
- Seattle freeway traffic speed
- Kaggle-Web Traffic Time Series Forecasting
- Electricity dataset from UCI
- SkyCam: A Dataset of Sky Images and their Irradiance values
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M4-competition
- Weighted ensemble of statistical models
- FFORMA: Feature-based forecast model averaging
- 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
- M4
- 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
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Kaggle-time-series-competition
Programming Languages
Categories
Keywords
time-series
28
machine-learning
15
forecasting
14
deep-learning
12
python
8
anomaly-detection
8
pytorch
7
data-science
7
time-series-forecasting
7
transformer
6
time-series-analysis
6
classification
5
data-mining
5
neural-network
4
imputation
4
timeseries
4
partially-observed-time-series
3
neural-networks
3
tensorflow
3
transformers
3
self-attention
2
missing-values
2
multivariate
2
interpolation
2
awesome
2
database
2
pandas
2
data-analysis
2
awesome-list
2
time-series-regression
2
clustering
2
prediction
2
deep-neural-networks
2
statistics
2
ai
2
industrial
1
incomplete
1
rough-paths
1
healthcare
1
neural-differential-equations
1
dynamical-systems
1
missingness
1
science-research
1
feature-engineering
1
feature-extraction
1
multimodal
1
processing
1
window-stride
1
keras-forecasting
1
keras-prediction
1