پست اینستاگرام من در مورد سومین افزایش احتمالی قیمت بیتکوین بعد از halving، زمانی که قیمت اش ۱۷ هزار دلار بود !!
امروز قیمت بیتکوین ۵۲ هزار دلار هست 😊
@machinelearningnet2
Bitcoin havling during the years and the following jump in the price.
#bitcoin #finance #forecasting #halving #stockmarket
https://www.instagram.com/p/CEVeMargHGh/?igshid=p4t4d1wa21yv
امروز قیمت بیتکوین ۵۲ هزار دلار هست 😊
@machinelearningnet2
Bitcoin havling during the years and the following jump in the price.
#bitcoin #finance #forecasting #halving #stockmarket
https://www.instagram.com/p/CEVeMargHGh/?igshid=p4t4d1wa21yv
🌺A new post in my official Instagram account
Special issue: #Energy #forecasting using #machinelearning algorithms
This is a hot topic and we are eager to new ideas
For my recommended papers, they have 50% discount for publication 😉
https://www.instagram.com/p/CMg5r57AAtU/?igshid=1nyoujtuwcld0
Special issue: #Energy #forecasting using #machinelearning algorithms
This is a hot topic and we are eager to new ideas
For my recommended papers, they have 50% discount for publication 😉
https://www.instagram.com/p/CMg5r57AAtU/?igshid=1nyoujtuwcld0
I'm thrilled to announce the latest release of MetaTS, a #Python package that simplifies and accelerates global #timeseries #forecasting using #meta-learning.
You can find MetaTS on GitHub here: https://github.com/DrSasanBarak/metats
Meta-learning has emerged as a winning solution for recent time series #forecasting competitions, and MetaTS is designed to make #meta-learning more accessible to researchers and data scientists. With MetaTS, you can easily generate meta-features using automated feature extraction and deep unsupervised learning, implement base-forecaster models, and optimize meta-parameters using a flexible and customizable pipeline.
In addition to providing a user-friendly toolkit for meta-learning, MetaTS also unifies the available Python libraries that can be useful for time series forecasting. You can leverage the power of #Sktime, #Nixtla, #Darts, and other libraries to create base forecasters and explore different meta-model architectures, including #stacking and #ensembling.
I'm proud of what we've achieved with MetaTS, and I believe it can be a valuable resource for anyone looking to improve their time series forecasting #performance. The latest version of the package is available on GitHub, and we welcome any feedback or contributions to help make MetaTS even better.
This can not be done without a great dedication and contribution of my colleague @AmirabbasAsadi .
Be tuned about this project on my LinkedIn
Thank you for your support!
You can find MetaTS on GitHub here: https://github.com/DrSasanBarak/metats
Meta-learning has emerged as a winning solution for recent time series #forecasting competitions, and MetaTS is designed to make #meta-learning more accessible to researchers and data scientists. With MetaTS, you can easily generate meta-features using automated feature extraction and deep unsupervised learning, implement base-forecaster models, and optimize meta-parameters using a flexible and customizable pipeline.
In addition to providing a user-friendly toolkit for meta-learning, MetaTS also unifies the available Python libraries that can be useful for time series forecasting. You can leverage the power of #Sktime, #Nixtla, #Darts, and other libraries to create base forecasters and explore different meta-model architectures, including #stacking and #ensembling.
I'm proud of what we've achieved with MetaTS, and I believe it can be a valuable resource for anyone looking to improve their time series forecasting #performance. The latest version of the package is available on GitHub, and we welcome any feedback or contributions to help make MetaTS even better.
This can not be done without a great dedication and contribution of my colleague @AmirabbasAsadi .
Be tuned about this project on my LinkedIn
Thank you for your support!
GitHub
GitHub - DrSasanBarak/metats: MetaTS | Time Series Forecasting using Meta Learning
MetaTS | Time Series Forecasting using Meta Learning - DrSasanBarak/metats
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