Using AI to Automatically Predict the Future

At Xyonix, we specialize in building custom AI models that automatically predict future values from time series data.

Our AI models help forecast patient arrivals, optimize labor plans, and predict market demand by uncovering complex temporal patterns, empowering you to make data-driven decisions that cut costs and improve efficiency.

In the top row, the model is still in early training, so forecasts (red) diverge from actual data (green). In the bottom row, after more training, forecasts closely align with reality [example taken from this article].

What is Time Series Data?

Time series data is data where a particular variable, like the number of patients arriving at a hospital or the closing value of the stock market, is sampled at some time interval (e.g., hourly, weekly). Many high value problems are time series in nature, including predicting airline bookings, retail demand, or patient arrivals. Using AI, we train models on historical data to understand intricate patterns and relationships that are otherwise undetectable, enabling precise and impactful forecasting.

Why Xyonix for Time Series Forecasting?

Deep Data Insights

We fuse historical data (e.g., bookings, sales) with external variables like weather or market indicators.
Advanced algorithms (LSTM, Temporal Convolutional Networks, N-BEATS) detect hidden trends and cyclical patterns.

Explainable AI

Tools like SHAP provide transparency, enabling your teams to understand why specific outcomes are predicted.
We highlight the key drivers behind each forecast, helping you build trust and refine your strategies.

Seamless Integration

Our solutions are designed to integrate seamlessly into your existing systems through APIs or containerized deployments.
Automated monitoring ensures scalability, so your forecasting grows with your operations.

Industry Experience

Everywhere from healthcare to hospitality, we’ve built AI models that forecast patient volumes, optimize staff scheduling, and anticipate consumer demand.
We ensure compliance with industry-specific regulations (e.g., HIPAA), guaranteeing secure and reliable solutions.

Check out our projects to get a taste of our capabilities:

projects

Patient Arrivals Forecaster

Xyonix partnered with a leading healthcare solutions provider to develop an AI-powered patient census forecasting system. This solution leverages time series models to predict patient arrivals up to five days in advance, enabling hospitals to optimize staffing and resource planning. With explainability features like SHAP analysis and intuitive error metrics, the system builds trust and provides teams with data-driven insights. The project included robust data preparation, fusing multiple data sources while adhering to strict HIPAA compliance, and hands-on guidance in product design and organizational integration. [Read more]

Customer Behavior Analyzer

We worked very closely with executives of a major dental insurance company to help better understand their customers. We used historic data to predict future customer behavior and purchase selections. Our models predict which customers are likely to lapse, and which are likely to remain loyal. Our models are also explainable, so it is easier to understand which attributes of a customer are indicative of loyalty or lapse tendencies. In addition to understanding high level customer characterizations, our explainable models describe why a specific customer is predicted to be a lapser or loyalist. [Read more]

Ready to Predict the Future with AI?

want to dig in deeper? check out some of our articles below

Inside the Black Box: Developing Explainable AI Models Using SHAP

Explainable AI refers to the ability to interpret model outcomes in a way that is easily understood by human beings. We explore why this matters, and discuss in detail tools that help shine light inside the AI "black box" -- we wish to not just understand feature importance at the population level, but to actually quantify feature importance on a per-outcome basis.

Predicting Covid-19 like the Weather, Using AI to Harness a Network of Health Sensors

The national weather service operates a massive sensor array including sensors located on weather balloons, in airplanes, on land, in sea buoys, and from satellites. Just like a confluence of variables can indicate a hurricane is on the way, we believe an infectious disease forecasting service powered by AI can predict the likelihood of a new wave of C19 and other infectious diseases.

Using AI to Predict Influenza, C19 and Other Infectious Disease Rates

AI can be used to generate regional forecasts of infectious disease rates that, in turn, empower government and other leaders to make prudent social distancing and other preemptive modifications. As a case study, we will look at influenza data supplied by the Centers for Disease Control. Our goal is to use ILI data to train a model that will accurately predict future seasonal flu levels.

Featured

“I don’t think a five out of five really encapsulates the work that they do. The work is top-notch. It’s what we ask for and more. They go the extra mile in terms of letting us know that whatever we need, they’re there for us to lend their expertise, to be in a meeting if they need to, to explain the project in more detail. So it’s really going above and beyond.”