Forecasting and Reconstructing Time Series

Workflow

Forecasting and Reconstructing Time Series

dynamic deployment recursive loop ARIMA restore seasonality restore trend

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This workflow forecasts the monthly average sales in 2017 based on monthly average sales between 2014 and 2016 using dynamic deployment. The forecasting model is an ARIMA (0,1,4) model. The forecasted sales values consist of the forecasted residuals and restored seasonality and trend components.

External resources

Time Series Analysis with Components
Sample-Superstore.xls data on Tableau Community page
Building a Time Series Application

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License (CC-BY-4.0)

Forecasting and Reconstructing Time Series

External resources

Used extensions & nodes

KNIME Core Trusted extension

KNIME Data Generation Trusted extension

KNIME JavaScript Views Trusted extension

KNIME Math Expression (JEP) Trusted extension

KNIME Python Integration Trusted extension

KNIME Quick Forms Trusted extension

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KNIME Core

KNIME Data Generation

KNIME JavaScript Views

KNIME Math Expression (JEP)

KNIME Python Integration

KNIME Quick Forms