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Prediction ARIMA Random Forest Seasonality Hyper Parameter Linear Regression Parameter Optimization
  1. Workflow
    ARIMA Model Example
    Time Series Energy Usage ARIMA
    +2
    This workflow predicts the irregular component of time series (energy consumption) by autoregressive integrated moving average (A…
    corey > Public > ARIMA Example
    1
    corey
  2. Workflow
    Exercise 1: Loading and Exploring Data
    Time Series Energy Usage Education
    +2
    This workflow accesses, preprocesses, and visualizes time series (energy consumption) data by - converting time values from Strin…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Exercises > Session_1 > 01_Load_Clean_and_Explore
    1
    knime
  3. Workflow
    Solution to the Exercise 1: Loading and Exploring Data
    Time Series Energy Usage Education
    +2
    This workflow accesses, preprocesses, and visualizes time series (energy consumption) data by - converting time values from Strin…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Solutions > Session_1 > 01_Load_Clean_and_Explore
    0
    knime
  4. Workflow
    Solution to the Exercise 3: SARIMA Models
    Time Series Energy Usage Education
    +2
    This workflow predicts the residual of time series (energy consumption) by seasonal autoregressive integrated moving average (ARI…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Solutions > Session_3 > 03_SARIMA_Models
    0
    knime
  5. Workflow
    Solution to the Exercise 4: ARIMA Models
    Time Series Energy Usage ARIMA
    +2
    This workflow predicts the irregular component of time series (energy consumption) by autoregressive integrated moving average (A…
    maarit > Public > 04_ARIMA_Models_test
    0
    maarit
  6. Workflow
    Solution to the Exercise 3: ARIMA Models
    Time Series Energy Usage ARIMA
    +2
    This workflow predicts the residual of time series (energy consumption) by autoregressive integrated moving average (ARIMA) model…
    giavo > Public > 03_ARIMA_Models
    0
    giavo
  7. Workflow
    Solution to the Exercise 4: ARIMA Models
    Time Series Energy Usage ARIMA
    +2
    This workflow predicts the irregular component of time series (energy consumption) by autoregressive integrated moving average (A…
    watcharee > Public > 04_ARIMA_Models_test
    0
    watcharee
  8. Workflow
    Exercise 5: Hyper Parameter Optimization
    Time Series Prediction Energy Usage
    +4
    This workflow optimizes the parameters of a machine learning model that predicts the residual of time series (energy consumption)…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Exercises > Session_4 > 05_Model_Optimization
    0
    knime
  9. Workflow
    Exercise 4: Machine Learning
    Time Series Prediction Energy Usage
    +3
    This workflow predicts the residual of time series (energy consumption) by machine learning models that use lagged values as pred…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Exercises > Session_4 > 04_Machine_Learning
    0
    knime
  10. Workflow
    Exercise 3: SARIMA Models
    Time Series Energy Usage Education
    +2
    This workflow predicts the residual of time series (energy consumption) by seasonal autoregressive integrated moving average (SAR…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Exercises > Session_3 > 03_SARIMA_Models
    0
    knime
  11. Workflow
    03_Deployment_and_Signal_Reconstruction
    Time Series Energy Usage Out-of-Sample
    +4
    This workflow performs out-of-sample forecasting of hourly energy consumption. It accesses a pretrained machine learning model th…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Supplementary Workflows > 03_Deployment_and_Signal_Reconstruction
    0
    knime
  12. Workflow
    Exercise 2: Inspecting and Removing Seasonality
    Time Series Energy Usage Seasonality
    +1
    This workflow shows the seasonality of time series (energy consumption) in an autocorrelation plot. The seasonality is removed by…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Exercises > Session_2 > 02_Inspect_and_Remove_Seasonality
    0
    knime
  13. Workflow
    Solution to the Exercise 2: Inspecting and Removing Seasonality
    Time Series Energy Usage Seasonality
    +1
    This workflow shows the seasonality of time series (energy consumption) in an autocorrelation plot. The seasonality is removed by…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Solutions > Session_2 > 02_Inspect_and_Remove_Seasonality
    0
    knime
  14. Workflow
    Solution to the Exercise 4: Machine Learning
    Time Series Prediction Energy Usage
    +3
    This workflow predicts the residual of time series (energy consumption) by machine learning models that use lagged values as pred…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Solutions > Session_4 > 04_Machine_Learning
    0
    knime
  15. Workflow
    Solution to the Exercise 5: Hyper Parameter Optimization
    Time Series Prediction Energy Usage
    +4
    This workflow optimizes the parameters of a machine learning model that predicts the residual of time series (energy consumption)…
    knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Solutions > Session_4 > 05_Model_Optimization
    0
    knime