Informer2020
flow-forecast
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Informer2020 | flow-forecast | |
---|---|---|
2 | 13 | |
4,863 | 1,874 | |
- | 3.9% | |
0.6 | 9.5 | |
about 1 month ago | 7 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 only |
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Informer2020
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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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.
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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.
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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.
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What is a good way to attract contributors?
Link is here
Was offline most of today. It's here. Basically Flow Forecast is a deep learning for time series framework built in PyTorch that aims to make it easy to use recent models from research conferences in a production/business context as well as conduct research.
What are some alternatives?
pytorch-forecasting - Time series forecasting with PyTorch
darts - A python library for user-friendly forecasting and anomaly detection on time series.
neural_prophet - NeuralProphet: A simple forecasting package
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
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
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
greykite - A flexible, intuitive and fast forecasting library
mlforecast - Scalable machine 🤖 learning for time series forecasting.
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification