uncertainty-baselines
pytorch-forecasting
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| uncertainty-baselines | pytorch-forecasting | |
|---|---|---|
| 3 | 9 | |
| 1,362 | 3,611 | |
| 1.8% | - | |
| 5.5 | 8.6 | |
| 21 days ago | 2 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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uncertainty-baselines
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Google AI Introduces ‘Uncertainty Baselines Library’ For Uncertainty and Robustness in Deep Learning
Code for https://arxiv.org/abs/2106.04015 found: https://github.com/google/uncertainty-baselines
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[D] Mixed Precision Training Tips
I know Reddit likes dumping on TensorFlow but it's actually really easy. Set a policy and upcast your final logits to float32.
pytorch-forecasting
- FLaNK Stack Weekly for 14 Aug 2023
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Pytorch Lstm
Source: Conversation with Bing, 4/5/2023 (1) jdb78/pytorch-forecasting: Time series forecasting with PyTorch - GitHub. https://github.com/jdb78/pytorch-forecasting. (2) Time Series Prediction with LSTM Using PyTorch - Colaboratory. https://colab.research.google.com/github/dlmacedo/starter-academic/blob/master/content/courses/deeplearning/notebooks/pytorch/Time_Series_Prediction_with_LSTM_Using_PyTorch.ipynb. (3) time-series-classification · GitHub Topics · GitHub. https://github.com/topics/time-series-classification. (4) PyTorch: Dataloader for time series task - Stack Overflow. https://stackoverflow.com/questions/57893415/pytorch-dataloader-for-time-series-task.
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[D] What is the best approach to create embeddings for time series with additional historical events to use with Transformers model?
Temporal fusion transformer https://github.com/jdb78/pytorch-forecasting
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LSTM/CNN architectures for time series forecasting[Discussion]
Pytorch-forecasting
- Can someone help me with this? It's been days that i struggle with this problem, Forecasting w DeepAR
- Can someone help me with this? it's been days that i struggle with this problem
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[P] Beware of false (FB-)Prophets: Introducing the fastest implementation of auto ARIMA [ever].
To name a few: https://github.com/jdb78/pytorch-forecasting, https://github.com/unit8co/darts, https://github.com/Nixtla/neuralforecast
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When to go for an 'easy' time-series model vs. using a complex deep learning model (when having experience with the latter)
I'm a data trainee at this organisation. I wrote my master thesis about using an event clustering mechanism to enrich an existing dataset to improve short-term demand predictions, using Pytorch Forecasting using the temporal fusion transformer component, and LightGBM (and compare the models with and w/o the event feature, so 4 runs in total).
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A python library for easy manipulation and forecasting of time series.
Darts is a pretty nice one. I've recently been using pytorch-forecasting for larger models like the Temporal Fusion Transformer. https://github.com/jdb78/pytorch-forecasting
What are some alternatives?
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
darts - A python library for user-friendly forecasting and anomaly detection on time series.
Bayeslite - BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
probability - Probabilistic reasoning and statistical analysis in TensorFlow
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
deep_learning_and_the_game_of_go - Code and other material for the book "Deep Learning and the Game of Go"
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
roc_comparison - The fast version of DeLong's method for computing the covariance of unadjusted AUC.
tslearn - The machine learning toolkit for time series analysis in Python