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5 Unexplored Python Libraries for Time Series Analysis

Good to have these gems in your bucket

Photo by Giallo from Pexels
Photo by Giallo from Pexels

Time series analysis is one of the most common problems that data scientists work with. The majority of time series solutions deal with economic prediction, resource demand forecasting, stock market analysis, and sales analysis.

Developing complex models for a large amount of time-related data can be an uphill task for programmers if done from scratch. This is where Python comes into action with its amazing libraries written especially for time series analysis.

This article will discuss five such libraries which might help you if you are interested in solving time series related problems. Some of these libraries are using a deep learning approach to find the best pattern in your data.

Still, I would suggest trying each of these libraries one by one with your data and then observe which model can help you catch the pattern in a better way. You can also combine the result of each model to get a consolidated result – which sometimes provides us with a better result.


1. AutoTS

As the name suggests it is a Python library for automated time series analysis. AutoTS allows us to train multiple time series models with just one line of code so that we can choose the best model that fits our problem.

This library is a part of autoML whose goal is to automate libraries for the convenience of beginners.

Dependencies

  • Python 3.6+
  • Numpy
  • Pandas
  • Sklearn
  • Statsmodels

Installation

pip install autoTS

You can learn more about this library here.

2. Prophet

Prophet is an excellent library developed by Facebook’s Data Science team for solving time series related problems which can be used using both R and python.

This can be especially helpful for dealing with time series that has a strong seasonal effect like buying behaviour or sales forecasting. Also, it works with messy data finely without any manual effort.

Installation

pip install prophet

You can learn more about this library here.

3. Darts

Darts is a scikit-learn friendly Python package for forecasting time series developed by Unit8.co. It contains a large number of models ranging from ARIMA to deep neural networks for working with data related to date and time.

The best thing about the library is that it also supports multidimensional classes for working with neural networks.

It also lets users combine predictions from several models and external regressors which makes it easier to backtest models.

Installation

pip install darts

You can learn more about this library here.

4. Pyflux

Pyflux is an open-source time-series library built for python. Pyflux chooses more of a probabilistic approach for solving the time series problems. This approach is especially very advantageous for tasks like forecasting where a more complete picture of uncertainty is needed.

Users can build a probabilistic model where the data and latent variables are treated as random variables through a joint probability.

Installation

pip install pyflux

You can learn more about this library here.

5. Sktime

Sktime is a Python library that comes with time-series algorithms and tools which are compatible with scikit-learn. It also has models for classification, regression and time series forecasting. The main goal of this library is to make models that can interoperate with scikit-learn.

Installation

pip install sktime

You can learn more about this library here.


Conclusion

These were a few Python libraries/frameworks which can be used while working with time series problems. There are some more cool time series libraries are there on the internet like tsfresh, atspy, kats – You can check them out as well.

The main goal is to choose a library according to your need i.e. the library that can match the requirements of your problem statement. For learning more about these libraries you can definitely check out their respective documentations provided with them as most of them are completely open-source.


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