learn-temporal-python-SDK
pytorch-forecasting
| learn-temporal-python-SDK | pytorch-forecasting | |
|---|---|---|
| 1 | 9 | |
| 2 | 4,089 | |
| - | 1.7% | |
| 3.5 | 9.1 | |
| almost 2 years ago | 8 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
learn-temporal-python-SDK
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Python SDK: Your First Application
I hope this helps you get started with the Temporal Python SDK but if not, I’ll see you on the forum and if you’re keen to look at version 1.0 of my own poker application, feel free. For me, the next steps in learning Temporal are to dive into child workflows, signals, and queries to the poker application.
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?
samples-python - Samples for working with the Temporal Python SDK
darts - A python library for user-friendly forecasting and anomaly detection on time series.
documentation - Temporal documentation
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
add-thin - This is the reference implementation of our NeurIPS 2023 paper "Add and Thin: Diffusion for Temporal Point Processes"
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
golfdb - GolfDB is a video database for Golf Swing Sequencing, which involves detecting 8 golf swing events in trimmed golf swing videos. This repo demos the baseline model, SwingNet.
nixtla - TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.
TS-TCC - [IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"
snntorch - Deep and online learning with spiking neural networks in Python
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification
Informer2020 - The GitHub repository for the paper "Informer" accepted by AAAI 2021.