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Hello, Is it possible to analyze data with low granularity (i.e. min by min) or they should be summarized before using them for time series forecasting using the notebooks provided in Dataiku.
related to an answer for: Time series methods in DSS
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Hi, It depends on what kind of time series analysis you want to perform. Could you provide more details regarding your goals? Is it about clustering? Anomaly detection? Forecasting? At what time granularity do you want to provide answers?
Hi, Did you solve your problem?
Not yet. I’m trying to forecast time series. The dataset is by minute, and I like to be able to forecast it a few days or weeks into the future. Then I want to use the forecast data and predict another variable using regression. Is this possible?

1 Answer

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Hi,

This is possible using some code along with the visual ML component of Dataiku.

You can use this example: http://gallery.dataiku.com/projects/TIMESERIES/flow/ as a starting point. The code element would mostly deal with slicing windows of the data, since ML models would only forecast one step ahead.

Let me know if this helps,

Alex
answered by
This is a great guide for Univariate Time Series time. However, I need to perform Multivariate Time Seriese Forecasting.  Any suggestions on univariate ts forecasting using i.e. Vector Auto Regression (VAR)?
In Python I would advise using https://www.statsmodels.org/dev/vector_ar.html. In R I would advise using https://otexts.org/fpp2/VAR.html.
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