Time series with multiple attributes
Newbie
Hello,
I'm a bit lost as to how exactly I should create a model for my data. I have a row for each day with multiple numeric attributes and the predicted attribute is also numeric. I want to predict the next value based on the previous data (let's say previous 5 days), but also the other attributes of the predicted row.
Example:
date att1 att2 att3 val
1.1.2018 7 0.3 33 12
2.1.2018 0 0.6 31 14
3.1.2018 0.5 5 32 ?
I want to predict the value of "val" in the last row, knowing all the values in the previous two rows but also the values of all the other attributes in the last row.
I suspect windowing and other time series operators and techniques should be used, but I'm not exactly sure how. All sample processes for time series predict only one value, not using any other attributes.
I'm a bit lost as to how exactly I should create a model for my data. I have a row for each day with multiple numeric attributes and the predicted attribute is also numeric. I want to predict the next value based on the previous data (let's say previous 5 days), but also the other attributes of the predicted row.
Example:
date att1 att2 att3 val
1.1.2018 7 0.3 33 12
2.1.2018 0 0.6 31 14
3.1.2018 0.5 5 32 ?
I want to predict the value of "val" in the last row, knowing all the values in the previous two rows but also the values of all the other attributes in the last row.
I suspect windowing and other time series operators and techniques should be used, but I'm not exactly sure how. All sample processes for time series predict only one value, not using any other attributes.
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Answers
You are correct that you will need to use the windowing operator and a predictor operator. Multivariate time series forecasts can be set up by declaring your val attribute as the label. Univariate and multivariate predictions are two separate problems. You can however use a multiply operator and do both forecasts in the same process.