Time series projects are among the most challenging in the world of data science today. But for organizations striving to become AI-driven, understanding how to approach those projects is essential. While traditional modeling relies on classification, regression, and static data, the data for time series is far more fluid. And taking this from raw data to accurate predictions not only requires a hefty investment in talent and technology but also a deep understanding of the business and the tools available to deal with unforeseen anomalies.
The Status Quo
Before beginning to tackle time series problems, organizations face several challenges. They must battle the status quo, of legacy models that work “just fine,” and the everyday issues surrounding the scale of predictions requirements. Alongside this, they face the technical complexity of implementation, especially when struggling to source accurate training data. So, why bother to address these complex problems?
Time series provides high-value solutions. In today’s world, consumers rely on the right products being available at the right time to suit their needs. Failing to meet these expectations leaves businesses floundering. In the retail industry alone, over and under-stock issues equate to an annual loss of $1.1 trillion worldwide (IHL Group, Retail’s $1.1 Trillion Inventory Distortion Problem, Greg Buzek, 2015). While a daunting prospect, time series can help solve these problems using demand forecasting and anomaly detection models. And automation makes the process manageable, streamlined, and scalable.
Demand forecasting allows companies to optimize the operations of their essential business units, resulting in straightforward capacity planning, inventory allocation, and product replenishment. And it’s not just for retailers; hospitals and event organizers rely on accurate staffing models to keep their businesses running smoothly and visitors safe. Demand forecasting allows organizations to predict for a specific time period, arming customers with the information they’re looking for when they want it.
But what do organizations do when something big happens? When black swan events like the COVID-19 pandemic occur, it’s essential to have a trusted platform to fall back on, one that can respond in real time to anomalous events. DataRobot’s Automated Time Series anomaly detection uses unsupervised machine learning workflows to detect anomalies and provides the blueprints to identify those anomalies.
Whether it’s anomaly detection, demand forecasting, or use cases that center on time, businesses can see huge returns from both effective and automated time series applications.
DataRobot: An AI Solution for the Real World
Organizations are struggling to make forecasting decisions for reasons ranging from changing market conditions to real-world issues and anomalies. DataRobot’s enterprise AI platform can bridge this gap and help realize the huge benefits of time series.
Time series automation is specifically suited to organizations’ largest scale and most complex problems. DataRobot’s Automated Time Series product takes these problems and breaks them down into thousands of smaller parts, organizes and prioritizes that data to combat demand, and provides a cost-benefit analysis to help organizations choose the right use cases for optimal improvement.
We bring you:
- Accurate and Intuitive AI – DataRobot’s time series techniques provide you with out-of-the-box accuracy, meaning you don’t need to know data science, just your business.
- Scalable AI – We offer AI that takes data from millions of sources simultaneously to scale and automate model forecasting.
- Real-World AI – DataRobot’s AI forecasting handles real-world complexity, allowing you to incorporate environmental changes and factors into your forecast.
- Actionable AI – Forecasting is essential for business success. So, we ensure your AI-driven modeling forecasts are put into production in a trusted and impactful way for your business.
The result? Models that are built for reality, using cutting edge approaches, allowing you to control your models and produce results that are scalable across your organization.
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