tsfeatures
flow-forecast
tsfeatures | flow-forecast | |
---|---|---|
5 | 13 | |
323 | 1,900 | |
2.5% | 2.4% | |
5.0 | 9.5 | |
10 days ago | 8 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 only |
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.
tsfeatures
- tsfeatures: NEW Data - star count:212.0
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[P] Deep Learning for time series forecasting (neuralforecast, python package)
GluonTS Differences: -GluonTS is written in mxnet, which reduces its adoption. In contrast, NeuralForecast is written in PyTorch. -Including new models in GluonTS tends to be challenging because mxnet 's and the library structure's learning curve are steep. PyTorch-Forecasting Differences: -NeuralForecast hosts some models from our research, including N-HiTS and Transformer-based (Autoformer, Informer, Transformer, etc.) methods specialized in long-horizon forecasting (https://arxiv.org/abs/2201.12886). -And the exogenous variables extension of N-BEATS, the NBEATSx (https://arxiv.org/abs/2104.05522). Extra Features: -NeuralForecast has a wide range of curated datasets used in research to develop and test new models, such as Tourism, M3, M4, M5, EPF, ILI, Traffic, Weather, etc. -NeuralForecast models include reasonable hyperparameter spaces to speed up hyperparameter search, based on our experience. -We include an experiment module that makes it easy to put the entire time series forecasting pipeline into production. -Finally, NeuralForecast is part of a larger ecosystem of time-series analysis and forecasting that includes feature creation (tsfeatures, https://github.com/Nixtla/tsfeatures), machine learning models (mlforecast, https://github.com/Nixtla/mlforecast) and statistical models (statsforecast, https://github.com/Nixtla/statsforecast).
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Automated Time Series Processing and Forecasting
Thanks for your comments.
We agree that in most cases prophet is not a good benchmark; however, we wanted to use it because it is one of the most used libraries in forecasting. For that reason, we also tested the solution against AWS Forecast obtaining better results.
Besides the better performance and scalability, the pipeline we created considering all the stages of time series forecasting: preprocessing (e.g. missing value imputation), creation of static and dynamic features, forecast generation, and finally evaluation using data sets of important competencies. (https://github.com/Nixtla/tsfeatures)
On the deployment side, the entire pipeline can be quickly deployed in the user's cloud using terraform. This allows for less development time. (https://github.com/Nixtla/nixtla)
flow-forecast
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Cash-flow forecasting
-Flow
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[P] Deep Learning for time series forecasting (neuralforecast, python package)
How does it compare to Flow Forecast? Honestly people rarely mention FF but I've found it much better than pytorch_forecasting and the like.
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PyTorch Forecasting lr_find out of bounds - request for help
PyTorch forecasting has a lot of bugs in it. You should try posting on issue on the actual repository though. Also, I've found Flow Forecast to be an all around much better deep learning for time series forecasting/classification framework.
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Hello reddit, what time series forecasting tools are you using?
If you want to use deep learning then Flow Forecast is the best. Many of the latest deep learning models and easy hyper-parameter sweeps.
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Understanding LSTM predictions
I haven't personally tried it, but here's a Github Repo called LIME for Time. I'm not sure about the state of attention visualization for timeseries but this repo has several models using attention.
- Flow Forecasting: A state of the deep learning for time series library
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Flow Forecast (deep learning for time series forecasting framework) Version 0.95 Released: New Deep Learning Models, Better Interpretability Support, and Several Bug Fixes
Hey everyone, just released a new Flow Forecast a deep learning for time series forecasting framework written in PyTorch. For this new version we added models, fixed several annoying bugs, and created better error messages. See the improved framework and tutorials Link
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Forecasting multiple time series ideas
This is actually a good case for deep learning techniques that create a learned time series embedding id and/or graph convolutions. The advantage of these methods is the can [learn spatial temporal dependencies across several time series](Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation). There are some implementations that you can find of these models that you can find in the repository that deep learning for time series repo I maintain.
- Deep Learning for Time Series Forecasting with Flow Forecast (Built in PyTorch)
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Financial time-series data forecasting - any other tools besides Prophet?
I mean a lot of deep learning models are more interpretable than you would think. There are a lot of methods to explain model predictions. In the deep learning for time series forecasting framework that I help maintain for example we automatically create SHAP plots to show relevant features. You can also visualize the attention mechanism directly.
What are some alternatives?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
mlforecast - Scalable machine 🤖 learning for time series forecasting.
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
nixtla - Python SDK for TimeGPT, a foundational time series model
neural_prophet - NeuralProphet: A simple forecasting package
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
xgboost-survival-embeddings - Improving XGBoost survival analysis with embeddings and debiased estimators
Time-Series-Forecasting-Using-LSTM - Time-Series Forecasting on Stock Prices using LSTM