# Sales Forecast with recursive loop **URL:** \ **Category:** KNIME Analytics Platform\ **Created:** [December 29, 2020, 8:15pm UTC](https://forum.knime.com/t/sales-forecast-with-recursive-loop/29717 "2020-12-29T20:15:43Z")\ **Posts on this page:** 1\ **Showing post:** 3
**Author:** ![mlauber71](https://forum-cdn.knime.com/user_avatar/forum.knime.com/mlauber71/32/73590_2.png) [@mlauber71](https://forum.knime.com/u/mlauber71)\ **Post date:** [December 30, 2020, 9:18am UTC](https://forum.knime.com/t/sales-forecast-with-recursive-loop/29717/3 "2020-12-30T09:18:01Z")
@Patrick92 welcome to the KNIME forum Like @Snowy said: it would be helpful if you could give us some more details. Then. We had a somewhat similar discussion about the prediction of numeric targets (unemployment in the UK) here: > [@Can KNIME be used to show the employment and unemployment rate in the UK from the year 2008-2018, showing the prediction for the next year?](https://forum.knime.com/t/can-knime-be-used-to-show-the-employment-and-unemployment-rate-in-the-uk-from-the-year-2008-2018-showing-the-prediction-for-the-next-year/20252/11): > > I set up an environment that first transforms you data from 2008-2016 into a table that has the Area (region) as an ID since I understand that is the unit you would want to predict. The Target is taken from 2017 “Emp UK percent”. If you want Unemployment you would have to change that. The years are transformed into columns, while the first year of the data becomes \_0 and the year before that \_1 and so on. [image] Then I construct a Test dataset using the data of past 9 years from … This resulted in a collection of workflows (please consider the whole workflow group): > **[compare several regression models on historical unemployment data – mlauber71](https://hub.knime.com/mlauber71/spaces/Public/forum/kn_forum_unemployment_uk/m_020_build_model~eD7J0NznPG9RhM-4/current-state)** > > compare several regression models on historical unemployment data Or this: > [@Unsupervised machine learning (gradient boosting regression)](https://forum.knime.com/t/unsupervised-machine-learning-gradient-boosting-regression/20382/9): > > I set up an example that should look like your data (if not feel free to adapt and inform). We have two years of data (from one city) and data from January and want to predict the coefficient for Feb 2020. 2019 serves as training and 2018 as test data. You might have more complex settings with cross-validation if you want. And also since you have the data you might provide coefficients of some more past months. Each line represents its own time series so to speak. Of course, such a se… Then there is this collection about machine learning methods. In your case a regression model might be the right one. Or you might have to use something to predict based on time series (no collection for that, yet): > **[Machine Learning Meta Collection (with KNIME) – mlauber71](https://hub.knime.com/mlauber71/spaces/Public/_machine_learning_meta_collection~--zu7353LMafrYQ_/current-state)** > > Machine Learning Meta Collection (with KNIME) This meta collection is about machine learning. It contains links to some examples demonstrating several types of… Often it does make sense to familiarise yourself with several methods and especially think about your business case and the data you have available and if what you want to find is somewhere in the data you have. --- _[View the full topic](https://forum.knime.com/t/sales-forecast-with-recursive-loop/29717)._