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03_​ARIMA_​Models

Solution to the Exercise 3: ARIMA Models
Data Loading Data Preparation ACF Plot & seasonality removal Model TrainingComparing two ARIMA models.One with (p,d,q) manually set and one going through the auto-ARIMA function. Time Series Analysis03. ARIMA ModelsSummary:In this exercise we'll train and score two ARIMA 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 both the ARIMA Learner and Auto ARIMA Learner components to the residualcolumn in the output from the Decompose Signal component. Note that the AutoARIMA can take quite a while to run, so be careful to keep the settings low for now.4) Use an ARIMA Predictor component after the learners, you can configure thenumber of values you want to forecast here.5) Attach the Forecast output from the ARIMA 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 ARIMA model with the Analyze ARIMA Residualscomponent. What can you say about the residuals? Note! The "Residual"column shows thetime series afterremoving the trendand first and secondseasonality. In thefollowing you buildmodels to predictvalues in this column. Energyusagedataconvertdate/timeinto Date&Time objectssubstuting missing values with average ofprevious and nextIntroducemissingdate times ARIMA Predictor File Reader String to Date&Time ImputingMissing Values Column Filter Partitioning RowID Joiner Numeric Scorer Joiner Numeric Scorer Timestamp Alignment Decompose Signal ARIMA Learner Auto ARIMA Learner ARIMA Predictor Analyze ARIMAResiduals Data Loading Data Preparation ACF Plot & seasonality removal Model TrainingComparing two ARIMA models.One with (p,d,q) manually set and one going through the auto-ARIMA function. Time Series Analysis03. ARIMA ModelsSummary:In this exercise we'll train and score two ARIMA 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 both the ARIMA Learner and Auto ARIMA Learner components to the residualcolumn in the output from the Decompose Signal component. Note that the AutoARIMA can take quite a while to run, so be careful to keep the settings low for now.4) Use an ARIMA Predictor component after the learners, you can configure thenumber of values you want to forecast here.5) Attach the Forecast output from the ARIMA 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 ARIMA model with the Analyze ARIMA Residualscomponent. What can you say about the residuals? Note! The "Residual"column shows thetime series afterremoving the trendand first and secondseasonality. In thefollowing you buildmodels to predictvalues in this column. Energyusagedataconvertdate/timeinto Date&Time objectssubstuting missing values with average ofprevious and nextIntroducemissingdate times ARIMA Predictor File Reader String to Date&Time ImputingMissing Values Column Filter Partitioning RowID Joiner Numeric Scorer Joiner Numeric Scorer Timestamp Alignment Decompose Signal ARIMA Learner Auto ARIMA Learner ARIMA Predictor Analyze ARIMAResiduals

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On NodePit since: 2020-09-21
Last update: 2024-04-27
Created with KNIME version: v4.2.0
Tags: Time SeriesEnergy UsageARIMAEducationPrediction

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