Time Series analysis and Forecasting for beginners part-1

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Time series analysis is a statistical technique that is used to identify patterns in data that varies over time. In today’s world, data is generated at a staggering rate and most of it is time-dependent. Therefore, understanding and analyzing time series data is crucial in many fields, including finance, economics, and healthcare.

Python is one of the most widely used programming languages for data analysis and provides an extensive set of libraries that make it an ideal choice for time series analysis. In this blog, we will explore the basics of time series analysis in Python and the libraries used for this purpose.

What is a Time Series?

A time series is a sequence of data points that are ordered by time. It is an important tool for analyzing trends, patterns, and behaviors in the data. Time series data is usually continuous, and the intervals between observations are uniform.

Time Series Analysis in Python

Python provides a wide range of libraries for time series analysis, including NumPy, Pandas, Matplotlib, and Statsmodels. In the following sections, we will explore these libraries in more detail.

1. NumPy

NumPy is a fundamental library for scientific computing in Python. It provides a powerful N-dimensional array object, which is used to store and manipulate large sets of data efficiently. NumPy arrays are particularly useful for working with time series data because they allow for efficient vectorized operations.

2. Pandas

Pandas is a data manipulation library that provides a range of data structures, including data frames and series. It is particularly useful for time series data because it provides functionality for working with time series data, including time-based indexing, date-range generation, and resampling.

3. Matplotlib

Matplotlib is a plotting library that is used to create a variety of visualizations in Python. It provides a range of plotting functions, including line plots, scatter plots, and bar plots. Matplotlib is particularly useful for time series data because it provides the ability to plot time series data, including the ability to plot multiple time series on a single plot.

4. Statsmodels

Statsmodels is a statistical modeling library that is used for a range of statistical analyses. It includes a range of time series models, including ARIMA (autoregressive integrated moving average) and SARIMA (seasonal autoregressive integrated moving average) models.

5. Prophet

Prophet is an open-source library developed by Facebook that is designed specifically for time-series forecasting. It offers a simple interface for users to fit and evaluate models and provides several built-in tools for visualizing and exploring time-series data.

6. Scipy

Scipy is a library that provides a wide range of mathematical functions, including tools for time-series analysis. It includes support for signal processing, time-series decomposition, and spectral analysis.

7. Pyflux

Pyflux is a library that provides a wide range of statistical models for time-series analysis, including ARMA, ARIMA, and state-space models. It includes support for data visualization, parameter estimation, and model selection.

8. Seaborn

Seaborn is a library that provides support for statistical data visualization. It includes several plot types that are useful for time-series analysis, including heatmaps and line plots.

9. Bokeh

Bokeh is a library that provides support for interactive data visualization. It can be used to create highly customizable time-series plots that allow users to zoom in and out, hover over data points, and interact with the data in real-time.

10. Plotly

Plotly is a library that provides support for interactive data visualization and can be used to create highly customizable time-series plots. It includes a wide range of chart types, including line plots, bar charts, and scatter plots, and allows users to create interactive dashboards and reports.

Conclusion

In this blog, we have explored the basics of time series analysis in Python and the libraries used for this purpose. Time series analysis is an important tool for understanding trends, patterns, and behaviors in data that vary over time. Python provides a range of libraries that are particularly useful for working with time series data, including NumPy, Pandas, Matplotlib, and Statsmodels. With these libraries, you can explore and analyze time series data to gain valuable insights and make informed decisions.

Now I hope you have good underating of basic of time series and it’s libraries in python.

What next?

Here a second part of my time series analysis with basic functions and exmple.

Time Series forecasting with ARIMA part-2

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