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02_​Example_​for_​Predicting_​Time_​Series

Workflow

Simple auto-regressive model to predict a time series
- Simple means just raw data: no seasonality correction, stationarity assumption - Auto means usage of past of the same time series for prediction. No other exogenous time series/data used. - Regressive refers to the model: either a linear or a polynomial regression
Simple auto-regressive model to predict a time series - Simple means just raw data: no seasonality correction, stationarity assumption - auto means usage of past of the same time series for prediction. No other exogenous time series/data used. - Regressive refers to the model: either a linear or a polynomial regression polynomial AR(lag)linear AR(lag)Here is where the time series gets selected andrenamed to "cluster"from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)read clusteredtime seriesreal vs. predictedreal vs. predictedfrom x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag) Partitioning Poly Regression Linear Regression Prepare Data Lag Column File Reader Line Plot(JavaScript) Line Plot(JavaScript) Lag Column Simple auto-regressive model to predict a time series - Simple means just raw data: no seasonality correction, stationarity assumption - auto means usage of past of the same time series for prediction. No other exogenous time series/data used. - Regressive refers to the model: either a linear or a polynomial regression polynomial AR(lag)linear AR(lag)Here is where the time series gets selected andrenamed to "cluster"from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)read clusteredtime seriesreal vs. predictedreal vs. predictedfrom x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag) Partitioning Poly Regression Linear Regression Prepare Data Lag Column File Reader Line Plot(JavaScript) Line Plot(JavaScript) Lag Column

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Nodes

02_​Example_​for_​Predicting_​Time_​Series consists of the following 21 nodes(s):

Plugins

02_​Example_​for_​Predicting_​Time_​Series contains nodes provided by the following 4 plugin(s):