ETSformer
flow-forecast
| ETSformer | flow-forecast | |
|---|---|---|
| 1 | 13 | |
| 240 | 1,912 | |
| 6.7% | 3.0% | |
| 0.0 | 9.5 | |
| over 1 year ago | 4 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 only |
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ETSformer
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?
time-series-transformers-review - A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
Crossformer - Official implementation of our ICLR 2023 paper "Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate 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
orbit - A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
neural_prophet - NeuralProphet: A simple forecasting package
LTSF-Linear - [AAAI-23 Oral] Official implementation of the paper "Are Transformers Effective for Time Series Forecasting?"
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