955 results
 "Time Series" exercise for advanced Life Science User Training  Extract granularities from a timestamp  Aggregate by time granularities  Calculate moving average  Calculate moving aggregation
 This workflow forecasts the monthly average sales in 2017 based on monthly average sales between 2014 and 2016 using dynamic deployment. The forecasting model is an ARIMA (0,1,4) model. The forecaste…
 Solution for "Time Series" exercise for advanced Life Science User Training  Extract granularities from a timestamp  Aggregate by time granularities  Calculate moving average  Calculate moving ag…
 This workflow demonstrates different time series functionality. As the usage of various time series nodes for analyzing currency exchange rates.
 This workflow performs a time series prediction using a recursive loop. In the workflow the first metanode generates some time series data for the saw tooth wave to train a linear regression model. I…
 Creates a time series of length n (depending on the number of rows in the input data) by repeating one of the four patterns and adds this time series in the last column of the input data. The four po…Manipulator
 This workflow builds an autoregressive model to predict energy usage. The first week of the time series is used as a template for seasonality correction: the data are differenced by subtracting the …
 This workflow shows how to access time series data, make it equallyspaced, impute missing values, aggregate it at a greater granularity, and explore it visually. After these steps, the time series i…
 Solution for "Time Series" exercise for advanced Life Science User Training  Extract granularities from a timestamp  Aggregate by time granularities  Calculate moving average  Calculate moving ag…
 This workflow shows the usage of quickforms to make a configurable user experience. In the first node, the user gets asked to provide a number of rows and features. Using this information, a table co…
 This workflow forecasts the monthly average sales in 2017 based on monthly average sales between 2014 and 2016 using dynamic deployment. The forecasting model is an ARIMA (0,1,4) model. The forecaste…
 The theme ingredient in this workflow is Energy Consumption Time Series. What kind of variables can we extract from energy consumption data? Here we work on: 1. Usage Measures  average and in % for …
 This workflow demonstrates different time series functionality. As the usage of various time series nodes for analyzing currency exchange rates.
 This workflow demonstrates how a recursive loop can be used to do rolling predictions, i.e. use some existing data to bootstrap the prediction and then use the predicted values themselves as features…

 This workflow shows how to access time series data, make it equallyspaced, impute missing values, aggregate it at a greater granularity, and explore it visually. After these steps, the time series i…
 Estimates parameters of an ARIMA time series model.Learner
 This workflow tests the performance of previously trained autoregressive models for anomaly detection:  Filter the data to the maintenance window  Loop over each frequency column  Apply the previ…
 This workflow deploys a previously trained autoregressive model for anomaly detection:  Select the date for deployment. Two months of its past values must be available.  Loop over each frequency c…
 This workflow trains an autoregressive model for anomaly detection:  Filter the data to training data covering only normal functioning  Loop over each frequency column at a time  Train an autore…