DeepADoTS
Informer2020
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| DeepADoTS | Informer2020 | |
|---|---|---|
| 1 | 2 | |
| 538 | 4,890 | |
| 0.0% | - | |
| 0.0 | 0.6 | |
| almost 2 years ago | about 2 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
DeepADoTS
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[D] Anomaly detection without training set?
Other than that, if you're looking for more complicated models you could try and adapt one of the models from this this GitHub repo for your task (this is an open source repo of some SOTA anomaly detection models).
Informer2020
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[R] Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Code for https://arxiv.org/abs/2012.07436 found: https://github.com/zhouhaoyi/Informer2020
- [R][D] Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. Zhou et al. AAAI21 Best Paper. ProbSparse self-attention reduces complexity to O(nlogn), generative style decoder to obtainsequence output in one step, and self-attention distilling for further reducing memory
What are some alternatives?
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
pytorch-forecasting - Time series forecasting with PyTorch
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
neural_prophet - NeuralProphet: A simple forecasting package
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
luminaire - Luminaire is a python package that provides ML driven solutions for monitoring time series data.
SAITS - The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516