Everything You Need To Know About Automated Predictive Engines

Automation is far more than a buzzword; it has become the centre of our lives. Many of us subconsciously do not notice, but we are looking for automation in many aspects of our lives. We are striving for simplicity, and thankfully nowadays complex business processes are often being handled by automation. Digitalisation has changed the whole process of means operations and functions. This is especially true thanks to the evolution of artificial intelligence (AI) and machine learning (ML). When it comes to analytics and predictions, the importance of automated predictive engine is essential. They provide deep analysis of real-time and prediction about what might possibly happen in the future. In this blog, we will explain everything you need to know about automated predictive engines.

WHAT IS AN AUTOMATED PREDICTIVE ENGINE?

An automated predictive engine is a complex software application that predicts, suggests, discovers and adapts according to the modelling. Many consider it the brain of prediction analytics. It collects data from multiple sources and organises the data in the optimum way for future analyses. The main goal is to start the analysis process and reach a conclusion that will allow the possibility of taking actions in advance.

HOW DO AUTOMATED PREDICTIVE ENGINES WORK?

The working principal of the automated predictive engines is using models (which can also be referred to as algorithms) to analyse the relationship between defined variables. This can be a relationship focusing on a performance, attributes or features. When the process is complete, the model estimates the likelihood of the relationship in comparison to a sample that has been designated earlier.

HOW ARE AUTOMATED PREDICTIVE ENGINES USED?

With the rise of automation, the means of processes have changed forever. Beginning with day-to-day tasks to intense and complex business processes, automation is widely accepted and benefited from.

Automated predictive engines are used for countless functions, from software applications and mobile phones to less data-oriented business functions. Health, tourism, finance, logistics, renewable energy industries and many more sectors are investing in it. It is fair to say that we are now experiencing a shift which is making businesses and industries dependent on automative predictive engines.

THE BENEFITS OF AUTOMATED PREDICTIVE ENGINES

As is clear from the heading, the main benefit of automated predictive engines is the prediction; the ability to understand and prevent things before events occur. Through this, users are able to make critical decisions in critical moments. Moreover, it allows businesses and industries the ability to analyse their processes and organisation. With data coming from deep and precise analysis, it is possible to improve efficiency, forecast management, and new product/process development.

AUTOMATED ≠ AUTOMATIC

The term ‘automated’ refers to a process conducted by large automatic equipment. For the predictive engine, this means a combination of automated processes and modelling. An engine needs periodic check-ups and improvements, which means human interaction. Experts spend a huge amount of time modelling the predictive engine, as it can be modelled and tailor-made for many occasions. As it is a complex and sensitive process, usually experienced data scientists are involved by monitoring the algorithms.

WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Artificial intelligence refers to machines that are able to perform “cognitive” functions which mimic those that humans and animals are naturally capable of. Machines  equipped with artificial intelligence are capable of functions such as learning, problem solving, analysis, adaptation, and self-learning.

AI is so valuable because it can analyse deeper data with accuracy. The users then have a better understanding of the huge amounts of data they have, also known as big data.

WHAT IS MACHINE LEARNING (ML)?

Machine Learning is a component of AI. It is the process of developing computer algorithms. It aims to improve automation through experience and history of data usage. The basic idea is using sample data in order to make assumptions and predictions about future occurrences.

Es ist wichtig zu betonen, dass künstliche Intelligenz und maschinelles Lernen nicht dasselbe sind. Es handelt sich um zwei unterschiedliche Funktionen, die sich gegenseitig ergänzen und unterstützen.

APPROACHES TO PREDICTIVE ENGINE MODELLING

There are various models available for predictive engines. Here, we’ll outline the most important and widely accepted ones. The two main types are regression techniques and machine learning techniques.

Regression models are the backbone of predictive engines. The main goal is to model and represent the correlation(s) between at least two different variables. There is a wide variety of regression techniques available, but it is usually not necessary to apply them all at once. Among the most common are linear regression model, logistic regression, and time series models are among the most used ones.

ML techniques are processes which use various statistical methods for regression and classification to define a wide variety of data. Thus, specific practises of machine learning such as deep learning, reinforced learning and forecasting are important functions. The purpose of those practices is to maximise the learning via historical data and optimising the precision of the models.

ANNEA & AUTOMATED PREDICTIVE ENGINE

Beim ANNEA, we start the whole process by acquiring and analysing the data through IoT, digital twin, edge computing and automated predictions. In the final stage of the process, we visualise the data in the ANNEA dashboard.

Automatische prädiktive Engines helfen uns, die Daten weiter zu analysieren, sie zu optimieren und an verschiedene Situationen anzupassen und vor allem die aktuellen Ereignisse zu verstehen und zukünftige Ereignisse vorherzusagen. Die automatischen prädiktiven Engines bei ANNEA sind mit modernster Technologie ausgestattet und werden ständig auf mögliche Verbesserungen überprüft, um einen nahtlosen Prozess zu gewährleisten.

Unser Team besteht aus Datenwissenschaftlern, Ingenieuren und Spezialisten für maschinelles Lernen, die über Fachwissen in den Bereichen Vorhersagemodelle/Algorithmen, künstliche Intelligenz und maschinelles Lernen verfügen und sich voll und ganz auf die Extraktion und Darstellung optimaler und wertvoller Daten konzentrieren. Setzen Sie sich mit den ANNEA-Experten in Verbindung, um mehr zu lernen!

WHAT IS NEXT?

We are close to bringing you to the end of a holistic process. The next topic will focus on how to deliver and visualise the final version of the data being analysed and process through our next generation platform: The ANNEA Dashboard.