Deep-Learning-Machine-Learning-Stock
tsfresh
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| Deep-Learning-Machine-Learning-Stock | tsfresh | |
|---|---|---|
| 5 | 4 | |
| 792 | 8,076 | |
| - | 0.8% | |
| 10.0 | 5.9 | |
| about 1 year ago | 4 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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Deep-Learning-Machine-Learning-Stock
tsfresh
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For deep learning practitioners in industry, is the workflow always this annoying? [D]
This is definitely a good thing to try for time-series; you can automate your feature extraction too (eg using https://github.com/blue-yonder/tsfresh ).
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[D] Incorporating external data in LSTM models for sales forecasting in e-commerce
don't forget your feature engineering -> https://github.com/blue-yonder/tsfresh
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[R] Approach to identify clusters on a time series
Rather than the exact clustering algorithm, I think the main issue here is the feature extraction for the clustering. https://github.com/blue-yonder/tsfresh might be useful for that.
- Automatic time series feature extraction based on scalable hypothesis tests
What are some alternatives?
DataScienceProjects
tsflex - Flexible time series feature extraction & processing
thesis_undergrad - Documentation: Methodology and Exploratory Data Analysis
TimeSynth - A Multipurpose Library for Synthetic Time Series Generation in Python
bulbea - :boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling
Deep_Learning_Machine_Learning_Stock - Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
Astock - Astock
SDV - Synthetic data generation for tabular data
FeatureHub - The most comprehensive library of AI/ML features across multiple domains. Our goal is to create a dataset that serves as a valuable resource for researchers and data scientists worldwide
Time-Series-Transformer - A data preprocessing package for time series data. Design for machine learning and deep learning.
TradingGym - Trading Gym is an open source project for the development of reinforcement learning algorithms in the context of trading.
darts - A python library for user-friendly forecasting and anomaly detection on time series.