https://www.dataquest.io/blog/15-python-libraries-for-data-science/

Let me recommend this framework for working with time series and forecast data:

Conference Paper Data Formats and Visual Tools for Forecast Evaluation in Cyb...

The framework is based on simple table structures to store data and is therefore capable of working with datasets containing observations of any frequencies and any number of time series met in practice.

The objective is to explore time series and forecasts and to evaluate and compare forecasting performance.

Hi Milad Cheraghzade

Python Environment for Time Series Forecasting

The Python ecosystem is growing and may become the dominant platform for applied machine learning.

The primary rationale for adopting Python for time series forecasting is because it is a general-purpose programming language that you can use both for R&D and in production.

In this post, you will discover the Python ecosystem for time series forecasting.

After reading this post, you will know:

• The three standard Python libraries that are critical for time series forecasting.
• How to install and setup the Python and SciPy environment for development.
• How to confirm your environment is working correctly and ready for time series forecasting.

Python is a general-purpose interpreted programming language (unlike R or Matlab).It is easy to learn and use primarily because the language focuses on readability.It is a popular language in general, consistently appearing in the top 10 programming languages in surveys on StackOverflow (for example, the 2015 survey results).Python is a dynamic language and very suited to interactive development and quick prototyping with the power to support the development of large applications.Python is also widely used for machine learning and data science because of the excellent library support. It has quickly become one of the dominant platforms for machine learning and data science practitioners and is in greater demand than even the R platform by employers (see the graph below).

This is a simple and very important consideration.

It means that you can perform your research and development (figuring out what models to use) in the same programming language that you use in operations, greatly simplifying the transition from development to operations.

Python Libraries for Time Series

SciPy is an ecosystem of Python libraries for mathematics, science, and engineering. It is an add-on to Python that you will need for time series forecasting.

Two SciPy libraries provide a foundation for most others; they are NumPy for providing efficient array operations and Matplotlib for plotting data.There are three higher-level SciPy libraries that provide the key features for time series forecasting in Python.

They are pandas, statsmodels, and scikit-learn for data handling, time series modeling, and machine learning respectively.