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ARIMA Example

ARIMA Model Example

This workflow predicts the irregular component of time series (energy consumption) by autoregressive integrated moving average (ARIMA) models that aim at modeling the correlation between lagged values and controling for seasonality in time series. The number of lagged values considered in the model can be set manually, or it can be optimized by testing different combinations of AR, I, and MA components of the model. The irregular component of time series is what is left after removing the trend and first and second seasonality.

ARIMA method ARIMA components! substuting missing values with average ofprevious and nextread energydata ImputingMissing Values Partitioning Numeric Scorer Table Reader RowID Joiner ARIMA Predictor ARIMA Learner Decompose Signal Timestamp Alignment Analyze ARIMAResiduals ARIMA method ARIMA components! substuting missing values with average ofprevious and nextread energydata ImputingMissing Values Partitioning Numeric Scorer Table Reader RowID Joiner ARIMA Predictor ARIMA Learner Decompose Signal Timestamp Alignment Analyze ARIMAResiduals

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

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On NodePit since: 2020-10-08
Last update: 2024-04-27
Created with KNIME version: v4.2.1
Tags: Time SeriesEnergy UsageARIMAEducationPrediction

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