Time series Python

Python is the most popular platform for applied Machine Learning (ML). Python was chosen for time series forecasting because it is a general-purpose programming language that can be used for both experimentation and production. It is simple to learn and use, owing to the language’s emphasis on readability. Python is a dynamic programming language that is well suited to interactive development and rapid prototyping, as well as the ability to enable the development of big applications.

Python is also commonly used for machine learning and data research due to its excellent library support. It provides libraries for time series such as NumPy, pandas, SciPy, scikit-learn, statsmodels, Matplotlib, date time, Keras, and many more. In this article, I will take a closer look at these key Python time series modules.

Time series Python

SciPy is a Python-based open-source software ecosystem for mathematics, science, and engineering. NumPy (a basic n-dimensional array package), Matplotlib (a comprehensive library for 2D charting), IPython (an upgraded interactive terminal), SymPy (a symbolic mathematics library), and pandas (a data structure and analysis library) are among the essential packages.

NumPy and Matplotlib are two SciPy libraries that serve as the foundation for the majority of others. NumPy is the foundational Python library for scientific computing. It includes, among other things, the following:

1. A powerful n-dimensional array object.
2. Sophisticated (broadcasting) functions.
3. Tools for integrating C/C++ and Fortran code.
4. Useful linear algebra, Fourier transform, and random number capabilities.

Matplotlib is a Python plotting toolkit that generates high-quality figures in a range of hardcopy and interactive formats across platforms. Matplotlib is a Python library that may be used in Python scripts, the Python and IPython shells, the Jupyter Notebook, web application servers, and four graphical user interface toolkits. Matplotlib can generate plots, histograms, power spectra, bar charts, error charts, scatterplots, and other graphics with a few lines of code.

Moreover, there are three higher-level SciPy libraries that provide the key features for time series forecasting in Python, namely pandas, stats models, and scikit-learn for data handling, time series modeling, and Machine Learning, respectively:

• Pandas is an open-source, BSD-licensed library for the Python programming language that provides high-performance, user-friendly data structures and data analysis tools. Python has long been excellent at data munging and preparation, but it has lagged behind in data analysis and modeling. Pandas bridges this gap, allowing you to complete your entire data analysis workflow in Python without switching to a more domain-specific language like R. The most recent pandas documentation is available at https://pandas.pydata.org/docs/. Pandas is a NumFOCUS-sponsored initiative that will help assure pandas’ success as a world-class open-source project. Outside of linear and panel regression, Pandas does not have major modeling capability; for this, see stats models and scikit-learn, as indicated below.

• Scikit-learn is a straightforward and effective data mining and analysis tool. Using a uniform interface, this library implements a variety of Machine Learning, pre-processing, cross-validation, and visualization techniques. It is based on NumPy, SciPy, and Matplotlib and is distributed under the Modified BSD (three-clause) license. Scikit-learn is a data modeling framework for Machine Learning. It is not concerned with data loading, handling, manipulation, or visualization. As a result, data scientists typically integrate scikit-learn with other libraries for data handling, pre-processing, and visualization, such as NumPy, pandas, and matplotlib. The latest scikit-learn documentation is available at https://scikit-learn.org/stable/user_guide.html.

• Statsmodels is a Python module that includes classes and functions for estimating various statistical models, as well as running statistical tests and exploring statistical data. Each estimator has a comprehensive set of result statistics. To guarantee that the results are correct, they are verified against current statistical software. The package is distributed under the Modified BSD (three-clause) open-source license. The most recent statsmodels documentation is available in the user’s handbook (https://www.statsmodels.org/stable/index.html).

Conclusion

For data scientists and industry specialists with varied levels of forecasting knowledge, this article outlines world-class Python frameworks and open-source forecasting best practices. I’ve covered the following topics in this article:

The greatest Python libraries for creating forecasting solutions.

Recent advancements in open source frameworks to design and operationalize high-performance forecasting solutions.

You will be able to dramatically minimize the “time to market” of your time series forecasting solutions by using the given open-source frameworks.