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.
Workflow 
Accessing, Transforming and Modeling Time Series
External resources
Used extensions & nodes
Created with KNIME Analytics Platform version 4.5.2
- Go to item
KNIME AG, Zurich, Switzerland
Version 4.5.2
- Go to item
KNIME AG, Zurich, Switzerland
Version 4.5.0
- Go to item
KNIME AG, Zurich, Switzerland
Version 4.5.2
- Go to item
KNIME AG, Zurich, Switzerland
Version 4.5.1
- Go to item
KNIME AG, Zurich, Switzerland
Version 4.5.2
- Go to item
KNIME AG, Zurich, Switzerland
Version 4.5.0
- Go to item
KNIME AG, Zurich, Switzerland
Version 4.5.0
- Go to item
KNIME AG, Zurich, Switzerland
Version 4.5.2
- Go to item
KNIME AG, Zurich, Switzerland
Version 4.5.2
- Go to item
KNIME AG, Zurich, Switzerland
Version 4.5.0
Loading deployments
Loading ad hoc executions
Legal
By using or downloading the workflow, you agree to our terms and conditions.