Data collected over a certain period of time is called Time-series data. These data points are usually collected at adjacent intervals and have some correlation with the target. There are certain datasets that contain columns with date, month or days that are important for making predictions like sales datasets, stock price prediction etc. But the problem here is how to use the time-series data and convert them into a format the machine can understand? Python made this process a lot simpler by introducing a package called Darts.

In this article, we will learn about Darts, implement this over a time-series dataset.

Introduction to Darts

For a number of datasets, forecasting the time-series columns plays an important role in the decision making process for the model. Unit8.co developed a library to make the forecasting of time-series easy called darts. The idea behind this was to make darts as simple to use as sklearn for time-series. Darts attempts to smooth the overall process of using time series in machine learning.

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