A Guide to Obtaining Time Series Datasets in Python
Datasets from real-world scenarios are important for building and testing machine learning models. You may just want to have some data to experiment with an algorithm. You may also want to evaluate your model by setting up a benchmark or determining its weaknesses using different sets of data. Sometimes, you may also want to create synthetic datasets, where you can test your algorithms under controlled conditions by adding noise, correlations, or redundant information to the data.
In this post, we’ll illustrate how you can use Python to fetch some real-world time-series data from different sources. We’ll also create synthetic time-series data using Python’s libraries.
After completing this tutorial, you will know:
- How to use the
pandas_datareader - How to call