Join us for an exciting webinar, where we will explore how Upalgo Labeling revolutionizes the process of annotating time series and sensor data.

In the world of Machine Learning, two prominent techniques emerge: Supervised and Unsupervised learning. While Supervised learning models often achieve higher accuracy, they heavily rely on labeled datasets. On the other hand, Unsupervised learning leverages unlabeled datasets, posing challenges in obtaining accurate labels for training.

Enter Upalgo Labeling, the game-changing solution that simplifies and accelerates the annotation of time series and sensor data. Designed with data scientists and data engineers in mind, Upalgo Labeling boasts an efficient user interface and cutting-edge functionalities such as the "candidate generator" and the "label propagator." These powerful features significantly enhance productivity and eliminate the painstaking effort associated with obtaining precise annotations.

During this webinar, Ezako Co-Founder & Managing Director Bora Kizil will walk you through the incredible capabilities of Upalgo Labeling. You will learn how this tool streamlines the annotation process, reduces time consumption, and empowers you to unlock the full potential of your time series and sensor data. We will showcase real-world use cases, highlighting how Upalgo Labeling has revolutionized the way data scientists and data engineers approach their projects.

Topics covered include:

• Upalgo Labeling software overview and capabilities

• Real-world use cases

• Live Demo

• Live audience Q&A

Don't miss this opportunity to discover the fastest and easiest way to annotate time series and sensor data. Join us for the webinar and embark on a journey to master time series and sensor data annotation with Upalgo Labeling. Reserve your spot today!

I have spent the past few days learning the basics of RapidMiner and looking through the tutorials and existing posts. There are a lot of examples on time series prediction comparing existing data against predicted/forecasted data and scoring/ranking the two streams against each other.

I am yet to find any discussion on “predicting into the future” where there is no available data. Eg use only the past few years of daily data to predict the next 100 days. As always, my question relates to the stock price of a single company.

I have setup my design view to use an ANN as per below.

However I would like RapidMiner to predict the next 100 days etc into the future where I have no data as shown in the blue drawn line below. Has anyone done this before or would know of an example I can examine?

Attend to learn about Tangent Works' Instant Machine Learning InstantML technology through its TIM Studio, a best-in-class time series data analytics tool that helps users automate the process to create data models for forecasting and anomaly detection that help them make better-informed business decisions.

Tangent Works’ TIM InstantML technology empowers users to drive business value from time series predictive analytics, make better-informed business and data decisions, create faster and more accurate data models. In addition, its TIM Studio allows users to quickly and efficiently explore the predictive value of large amounts of time series data, automate and manage predictions, and build powerful predictions through automated, explainable artificial intelligence (AI). The solutions also let users rebuild and recalibrate models easily, giving them the ability to continuously adjust model parameters and identify new features.

Topics covered include:

• TIM Studio software overview 

• Software capabilities and use case examples

• Live demo

• Live audience Q&A

Who should attend?
CheiCIO/CDO, IT Manager, Production Manager/Planner, Produce Manager, Maintenance Manager, Reliability Manager, Supply Chain Manager/Planners, Logistic Managers/Planner, Business/Data Analyst, Data Scientist/Engineer, Innovation/IoT Manager. 

I am not able to build the right forecasting model to predict for the next 1 month.

Please provide solution on this!

I'm having trouble understanding how the Windowing operator works, I have a few questions:

1) What goes into selecting a window size? If I want to forecast Premium over the next 12 months, is my window size 12? And if so, why do I get 12 attributes for each original attribute in my data set (the original Premium amount in one of these 12)? I get that this is supposed to explain the corresponding label value (which is just the next row's original Premium, not sure why this is happening either), but where are these numbers coming from and why does RapidMiner generate these?

2) What does the option "create single attributes" do?

3) The horizon field: If this is the distance between the last window value and the value to predict, does this mean I can't at once predict the next 12 months of data? Even if I enter the horizon as 1 (which I take to mean, give me the prediction for January 2016 since the last data point is for December 2015), then why is there no label value for December 2015 or January 2016 in the output when I run the process?

I'm a beginner, and I would really appreciate any help!