**30**
results

**30**
results

- Go to itemThis workflow builds an auto-regressive model to predict energy usage. The first week of the time series is used as a template fo…knime > Examples > 50_Applications > 10_Energy_Usage > 01_Energy_Usage_Time_Series_Prediction0
- Go to itemThis workflow demonstrates visual analytics techniques for time series (energy consumption) data.maarit > Public > Webinar > TSA_Webinar > 1_Load_Clean_and_Explore0
- Go to itemWorkflow
###### Solution to the Exercise 2: Inspecting and Removing Seasonality

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_Seasonality0 - Go to itemThis 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_Seasonality0
- Go to itemThis 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_Explore0
- Go to itemWorkflow
###### Solution to the Exercise 1: Loading and Exploring Data

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_Explore0 - Go to itemThis workflow demonstrates how to predict time series (energy consumption) with an autoregressive integrated moving average (ARIM…maarit > Public > Webinar > TSA_Webinar > 2_ARIMA_Models0
- Go to itemThis 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_Models0
- Go to itemThis 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_Models0
- Go to itemThis workflow shows an example of dynamic out-of sample forecasting of time series.maarit > Public > Webinar > TSA_Webinar > 4_Deployment_and_Signal_Reconstruction0
- Go to itemThis 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_Learning0
- Go to itemThis 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_Learning0
- Go to itemThis 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_Optimization0
- Go to itemWorkflow
###### Solution to the Exercise 5: Hyper Parameter Optimization

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_Optimization0 - Go to itemThis 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_Reconstruction0
- Go to itemThis workflow predicts time series (energy consumption) by an LSTM network with lagged values as input. The trained model is then…knime > Education > Courses > L4-TS Introduction to Time Series Analysis > Supplementary Workflows > 02_LSTM_Network0
- Go to itemThis workflow demonstrates how to predict time series (energy consumption) by a Random Forest model using lagged values as predic…maarit > Public > Webinar > TSA_Webinar > 3_Machine_Learning0
- Go to itemThis workflow predicts the residual of time series (energy consumption) by autoregressive integrated moving average (ARIMA) model…giavo > Public > 03_ARIMA_Models0
- Go to itemThis workflow predicts the irregular component of time series (energy consumption) by autoregressive integrated moving average (A…maarit > Public > 04_ARIMA_Models_test0
- Go to itemThis workflow predicts the irregular component of time series (energy consumption) by autoregressive integrated moving average (A…watcharee > Public > 04_ARIMA_Models_test0

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