Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Getting Things Done With Pytorch | 873 | 2 years ago | 13 | apache-2.0 | Jupyter Notebook | |||||
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BERT. | ||||||||||
Awesome Ai For Time Series Papers | 357 | 17 days ago | mit | |||||||
A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals. | ||||||||||
Scipy_con_2019 | 261 | 2 days ago | mit | Jupyter Notebook | ||||||
Tutorial Sessions for SciPy Con 2019 | ||||||||||
Pyam | 173 | 1 | 14 | 20 days ago | 25 | December 19, 2022 | 70 | apache-2.0 | Python | |
Analysis & visualization of energy & climate scenarios | ||||||||||
Timeseries | 153 | 8 months ago | 2 | mit | Jupyter Notebook | |||||
Timeseries for everyone | ||||||||||
Deep Learning Based Ecg Annotator | 92 | 3 years ago | 4 | Python | ||||||
Annotation of ECG signals using deep learning, tensorflow’ Keras | ||||||||||
Sktime Tutorial Pydata Amsterdam 2020 | 91 | a year ago | bsd-3-clause | Jupyter Notebook | ||||||
Introduction to Machine Learning with Time Series at PyData Festival Amsterdam 2020 | ||||||||||
Python Practical Application On Climate Variability Studies | 75 | 3 years ago | mit | Jupyter Notebook | ||||||
This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niño 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python. | ||||||||||
Tutorials | 51 | 3 years ago | 1 | apache-2.0 | Jupyter Notebook | |||||
tutorials of XAI project | ||||||||||
Aml Days Tda Tutorial | 20 | 4 years ago | Jupyter Notebook | |||||||
A professionally curated list of papers (with available code), tutorials, and surveys on recent AI for Time Series Analysis (AI4TS), including Time Series, Spatio-Temporal Data, Event Data, Sequence Data, Temporal Point Processes, etc., at the Top AI Conferences and Journals, which is updated ASAP (the earliest time) once the accepted papers are announced in the corresponding top AI conferences/journals. Hope this list would be helpful for researchers and engineers who are interested in AI for Time Series Analysis.
The top conferences including:
The top journals including (mainly for survey papers): CACM, PIEEE, TPAMI, TKDE, TNNLS, TITS, TIST, SPM, JMLR, JAIR, CSUR, DMKD, KAIS, IJF, arXiv(selected), etc.
If you found any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request.
For general Recent AI Advances: Tutorials and Surveys in various areas (DL, ML, DM, CV, NLP, Speech, etc.) at the Top AI Conferences and Journals, please check This Repo.
Effectively Modeling Time Series with Simple Discrete State Spaces [paper] [official code]
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis [paper] [official code]
Contrastive Learning for Unsupervised Domain Adaptation of Time Series [paper] [official code]
Recursive Time Series Data Augmentation [paper] [official code]
Multivariate Time-series Imputation with Disentangled Temporal Representations [paper] [official code]
Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths [paper] [official code]
Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise [paper] [official code]
CUTS: Neural Causal Discovery from Unstructured Time-Series Data [paper] [official code]
Temporal Dependencies in Feature Importance for Time Series Prediction [paper] [official code]
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting [paper] [official code]
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction [paper] [official code]
Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting [paper]
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [paper]
Generative Time Series Forecasting with Diffusion, Denoise and Disentanglement
Learning Latent Seasonal-Trend Representations for Time Series Forecasting
WaveBound: Dynamically Bounding Error for Stable Time Series Forecasting
Time Dimension Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting
Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting
Meta-Learning Dynamics Forecasting Using Task Inference [paper]
Conformal Prediction with Temporal Quantile Adjustments
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency, [paper] [official code]
Causal Disentanglement for Time Series
BILCO: An Efficient Algorithm for Joint Alignment of Time Series
Dynamic Sparse Network for Time Series Classification: Learning What to See
AutoST: Towards the Universal Modeling of Spatio-temporal Sequences
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks
Efficient learning of nonlinear prediction models with time-series privileged information
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models