Update README.md

Author: krzjoa

README.md

@@ -32,10 +32,10 @@
 	- [JAX](#jax)
 - [Others](#others)
 - [Automated Machine Learning](#automated-machine-learning)
-- [Time Series](#time-series)
 - [Natural Language Processing](#natural-language-processing)
 - [Computer Audition](#computer-audition)
 - [Computer Vision](#computer-vision)
+- [Time Series](#time-series)
 - [Reinforcement Learning](#reinforcement-learning)
 - [Graph Machine Learning](#graph-machine-learning)
 - [Learning-to-Rank & Recommender Systems](#learning-to-rank-&-recommender-systems)
@@ -182,23 +182,6 @@
 * [TPOT](https://github.com/rhiever/tpot) - AutoML tool that optimizes machine learning pipelines using genetic programming. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [MLBox](https://github.com/AxeldeRomblay/MLBox) - A powerful Automated Machine Learning python library.
 
-## Time Series
-* [sktime](https://github.com/alan-turing-institute/sktime) - A unified framework for machine learning with time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [darts](https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series.
-* [statsforecast](https://github.com/Nixtla/statsforecast) - Lightning fast forecasting with statistical and econometric models.
-* [mlforecast](https://github.com/Nixtla/mlforecast) - Scalable machine learning-based time series forecasting.
-* [neuralforecast](https://github.com/Nixtla/neuralforecast) - Scalable machine learning-based time series forecasting.
-* [tslearn](https://github.com/rtavenar/tslearn) - Machine learning toolkit dedicated to time-series data. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [tick](https://github.com/X-DataInitiative/tick) - Module for statistical learning, with a particular emphasis on time-dependent modeling. <img height="20" src="img/sklearn_big.png" alt="sklearn">
-* [greykite](https://github.com/linkedin/greykite) - A flexible, intuitive, and fast forecasting library next.
-* [Prophet](https://github.com/facebook/prophet) - Automatic Forecasting Procedure.
-* [PyFlux](https://github.com/RJT1990/pyflux) - Open source time series library for Python.
-* [bayesloop](https://github.com/christophmark/bayesloop) - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
-* [luminol](https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library.
-* [dateutil](https://dateutil.readthedocs.io/en/stable/) - Powerful extensions to the standard datetime module
-* [maya](https://github.com/timofurrer/maya) - makes it very easy to parse a string and for changing timezones
-* [Chaos Genius](https://github.com/chaos-genius/chaos_genius) - ML powered analytics engine for outlier/anomaly detection and root cause analysis
-
 ## Natural Language Processing
 * [torchtext](https://github.com/pytorch/text) - Data loaders and abstractions for text and NLP. <img height="20" src="img/pytorch_big2.png" alt="PyTorch based/compatible">
 * [gluon-nlp](https://github.com/dmlc/gluon-nlp) - NLP made easy. <img height="20" src="img/mxnet_big.png" alt="MXNet based">
@@ -238,6 +221,23 @@
 * [albumentations](https://github.com/albu/albumentations) - Fast image augmentation library and easy-to-use wrapper around other libraries.
 * [LAVIS](https://github.com/salesforce/LAVIS) - A One-stop Library for Language-Vision Intelligence.
 
+## Time Series
+* [sktime](https://github.com/alan-turing-institute/sktime) - A unified framework for machine learning with time series. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [darts](https://github.com/unit8co/darts) - A python library for easy manipulation and forecasting of time series.
+* [statsforecast](https://github.com/Nixtla/statsforecast) - Lightning fast forecasting with statistical and econometric models.
+* [mlforecast](https://github.com/Nixtla/mlforecast) - Scalable machine learning-based time series forecasting.
+* [neuralforecast](https://github.com/Nixtla/neuralforecast) - Scalable machine learning-based time series forecasting.
+* [tslearn](https://github.com/rtavenar/tslearn) - Machine learning toolkit dedicated to time-series data. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [tick](https://github.com/X-DataInitiative/tick) - Module for statistical learning, with a particular emphasis on time-dependent modeling. <img height="20" src="img/sklearn_big.png" alt="sklearn">
+* [greykite](https://github.com/linkedin/greykite) - A flexible, intuitive, and fast forecasting library next.
+* [Prophet](https://github.com/facebook/prophet) - Automatic Forecasting Procedure.
+* [PyFlux](https://github.com/RJT1990/pyflux) - Open source time series library for Python.
+* [bayesloop](https://github.com/christophmark/bayesloop) - Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.
+* [luminol](https://github.com/linkedin/luminol) - Anomaly Detection and Correlation library.
+* [dateutil](https://dateutil.readthedocs.io/en/stable/) - Powerful extensions to the standard datetime module
+* [maya](https://github.com/timofurrer/maya) - makes it very easy to parse a string and for changing timezones
+* [Chaos Genius](https://github.com/chaos-genius/chaos_genius) - ML powered analytics engine for outlier/anomaly detection and root cause analysis
+
 ## Reinforcement Learning
 * [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) - An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly [Gym](https://github.com/openai/gym)).
 * [PettingZoo](https://github.com/Farama-Foundation/PettingZoo) - An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities.