Icon

03_​SARIMA_​Models

SARIMA Models - Exercise

This workflow predicts the residual of time series (energy consumption) by seasonal autoregressive integrated moving average (SARIMA) models that aim at modeling the correlation between lagged values and controling for seasonality in time series.






Time Series Analysis03. SARIMA ModelsSummary:In this exercise we'll train and score our first SARIMA models.Instructions:1) Run the workflow up through the Decompose Signal component, we’ll start thisexercise from here2) Partition the data using the Partioning node. Let’s use an 80/20 split. Make sureyou check the box to take data from the top. This is important with time series data. 3) Apply the SARIMA Learner to the residual column in the output from theDecompose Signal component. Note that that the SARIMA component can take sometime to run so keep the settings low for now.4) Use an SARIMA Predictor component after the learner, you can configure thenumber of values you want to forecast here.5) Attach the Forecast output from the SARIMA Predictor to the top port of the scoringmetanode and the other half of our Partitioning node to the bottom. Run the scoringmetanode and look at the results. Try this with different numbers of forecasted values.Do the scores change?6) Analyze the residuals of the SARIMA model with the Analyze ARIMA Residualscomponent. What can you say about the residuals? Data Loading Data Preparation ACF Plot & seasonality removal Model Training Note! The "Residual"column shows thetime series afterremoving the trendand first and secondseasonality. In thefollowing you buildmodels to predictvalues in this column. convertdate/timeinto Date&Time objectssubstuting missing values with average ofprevious and nextIntroducemissinghoursEnergyusagedatatop port: from predictorbottom port: from bottom partition String to Date&Time ImputingMissing Values Column Filter Timestamp Alignment CSV Reader Decompose Signal Scorer Time Series Analysis03. SARIMA ModelsSummary:In this exercise we'll train and score our first SARIMA models.Instructions:1) Run the workflow up through the Decompose Signal component, we’ll start thisexercise from here2) Partition the data using the Partioning node. Let’s use an 80/20 split. Make sureyou check the box to take data from the top. This is important with time series data. 3) Apply the SARIMA Learner to the residual column in the output from theDecompose Signal component. Note that that the SARIMA component can take sometime to run so keep the settings low for now.4) Use an SARIMA Predictor component after the learner, you can configure thenumber of values you want to forecast here.5) Attach the Forecast output from the SARIMA Predictor to the top port of the scoringmetanode and the other half of our Partitioning node to the bottom. Run the scoringmetanode and look at the results. Try this with different numbers of forecasted values.Do the scores change?6) Analyze the residuals of the SARIMA model with the Analyze ARIMA Residualscomponent. What can you say about the residuals? Data Loading Data Preparation ACF Plot & seasonality removal Model Training Note! The "Residual"column shows thetime series afterremoving the trendand first and secondseasonality. In thefollowing you buildmodels to predictvalues in this column. convertdate/timeinto Date&Time objectssubstuting missing values with average ofprevious and nextIntroducemissinghoursEnergyusagedatatop port: from predictorbottom port: from bottom partition String to Date&Time ImputingMissing Values Column Filter Timestamp Alignment CSV Reader Decompose Signal Scorer

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
On NodePit since: 2024-03-22
Last update: 2024-04-27
Created with KNIME version: v4.4.0
Tags: Time SeriesEnergy UsageEducationPredictionSARIMA

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!