Accessing, Transforming and Modeling Time Series

This workflow shows how to access time series data, make it equally-spaced, impute missing values, aggregate it at a greater granularity, and explore it visually. After these steps, the time series is decomposed into trend, seasonality, and residual. The residual is modeled with an ARIMA model, and deployment data are saved for testing the model's out-of-sample forecast accuracy.

This is a companion discussion topic for the original entry at

hi there,

how i can have the data’s example?
thank you


Hello @SimoneDePaoli,

if haven’t found input data is in _data folder one level up.