Time-Series-Forecasting-Using-LSTM
flow-forecast
Our great sponsors
Time-Series-Forecasting-Using-LSTM | flow-forecast | |
---|---|---|
1 | 13 | |
12 | 1,843 | |
- | 4.3% | |
1.8 | 9.5 | |
11 months ago | 15 days ago | |
Jupyter Notebook | Python | |
- | 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.
Time-Series-Forecasting-Using-LSTM
We haven't tracked posts mentioning Time-Series-Forecasting-Using-LSTM yet.
Tracking mentions began in Dec 2020.
flow-forecast
-
Cash-flow forecasting
-Flow
-
[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.
-
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.
-
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.
-
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.
-
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 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-seq2seq - Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
cryptocurrency-price-prediction - Cryptocurrency Price Prediction Using LSTM neural network
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
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
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
greykite - A flexible, intuitive and fast forecasting library
TensorFlow2.0_Notebooks - Implementation of a series of Neural Network architectures in TensorFow 2.0
mlforecast - Scalable machine 🤖 learning for time series forecasting.