Auto_TS
TimeSynth
Auto_TS | TimeSynth | |
---|---|---|
6 | 1 | |
674 | 327 | |
- | 0.9% | |
6.8 | 0.0 | |
1 day ago | 6 months ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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Auto_TS
TimeSynth
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What is the best way to generate synthetic OHLC data?
I have the same question so I cant give a direct answer. However, I've been thinking of using SDV and TimeSynth python packages to produce synthetic data for backtesting.
What are some alternatives?
Deep_XF - Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
tsfresh - Automatic extraction of relevant features from time series:
Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
SDV - Synthetic data generation for tabular data
modeltime - Modeltime unlocks time series forecast models and machine learning in one framework
ta - Technical Analysis Library using Pandas and Numpy
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tempo - API for manipulating time series on top of Apache Spark: lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, downsampling, and interpolation
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
stingray - Anything can happen in the next half hour (including spectral timing made easy)!
logbrain - Parsing log files can be a tedious task, especially when dealing with complex log formats. The Log Parser aims to streamline this process by leveraging regular expressions to match and capture relevant fields from log entries. With the extracted data, users can perform further analysis, generate reports, or gain insights from their log files.
pycaret - An open-source, low-code machine learning library in Python