**Python is one of the most popular programming languages for applied finance and machine learning. In the financial sector, a lot of data is available as time series.**
For data scientists or quant scientists, it is therefore essential to master time series analysis techniques. Time series data have unique properties and need different algorithms.
We have written an [**introduction article on time series analysis**](/data-science/time-series-analysis-a-beginners-guide-with-statsmodels). In this article, you learn everything you need to know about time series analysis with statsmodels.
There are several time series models, but in this tutorial, we will focus on ETS models. As a data scientist in the financial sector, it is essential to know these models.
We’ll discuss the following topics:
* **Theory of Error-Trend-Seasonality (ETS) models**
* **Technical requirements**
* **Practical Use Case**
* **Description of the Use Case**
* **Working with a financial API in Python**
* **Data Preparation**
* **Data Visualization**
* **ETS models with Python**
* **Time Series decomposition**
* **Simple exponential smoothing**
* **Holt-Winters Method**
* **Conclusion**
The [full article](https://steady.page/en/tinztwins-hub/posts/f8ea5ab5-4da2-4282-9632-808a3e2204c6) is available in our members-only area.
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