AI Time-Series Forecasting Techniques: A Complete Guide

Your historical data is telling a story about your business. The question is, are you equipped to understand what it's saying about the future? While traditional forecasting can give you a rough outline, AI can fill in the details with incredible precision. It can account for seasonality, market trends, and other hidden factors to produce a much richer, more reliable prediction. But getting there requires more than just data; it requires the right models and the right infrastructure to support them. In this guide, we’ll explore the foundational time-series forecasting techniques for AI, showing you how to build a system that turns raw data into a clear view of what’s to come.

Key takeaways

• AI offers a strategic advantage, not just a better guess: It moves beyond simple historical trends to analyze complex, real-world variables, creating more accurate and adaptive predictions that help you anticipate market shifts.
• The best technique is the one that fits your situation: There's no one-size-fits-all model. The right choice depends on balancing your data's complexity with your business goals and the technical resources you have to support the solution.
• A successful model depends on a strong foundation: The algorithm is just one piece. Long-term success requires a commitment to clean data, the right tech stack, and continuous monitoring to ensure your forecasts remain reliable over time.

What is time-series forecasting in AI?

At its core, time-series forecasting is about using historical data to make educated guesses about the future. Think of it like looking at your company's sales figures from the last three years to predict future sales for the next quarter. Businesses have been doing this for ages to make smarter decisions about everything from managing inventory and staffing levels to setting prices. It’s the foundation of strategic planning.

So, where does AI fit in? While traditional forecasting methods rely on simpler statistical models, AI brings a whole new level of sophistication to the table. AI-powered forecasting can analyze vast amounts of data, identify incredibly complex patterns, and even account for external factors that older models would miss, e.g, how a social media trend might impact demand for a product. This allows for more accurate and reliable predictions. Instead of just looking at past sales, an AI model can simultaneously analyze market trends, competitor pricing, and even weather patterns to create a much richer, more nuanced forecast. For businesses looking to get ahead, managing the entire stack from compute infrastructure to the models themselves is key to turning all that data into a real competitive advantage.

IN DEPTH: Implementing time-series analytics with Cake

What makes up time-series data?

Time-series data is simply a sequence of data points collected over time. The key ingredient is that each data point has a timestamp. Imagine tracking your website’s daily visitors, your store’s hourly sales, or the monthly temperature in your city. All of these are examples of time-series data because the information is organized chronologically. This order is crucial because it reveals trends, seasonality, and cycles. By understanding these patterns from the past, you can start to predict what might happen next. It’s less about the individual data points and more about the story they tell together over a specific period.

Why AI is a game-changer for forecasting

AI completely transforms forecasting because it learns and adapts. Traditional forecasting models are often static; you build them, and they don't change unless you manually update them. AI models, on the other hand, continuously learn from new data as it comes in. This means your forecasts get progressively more accurate over time. AI can also handle a much greater level of complexity, finding subtle relationships in your data that would be impossible to spot manually. This ability to anticipate market trends and optimize operations gives businesses a powerful tool for making proactive, data-driven decisions instead of just reacting to what’s already happened.

AI forecasting myths, busted

One of the biggest myths is that AI is a crystal ball that will tell you exactly what’s going to happen. In reality, AI forecasting is about probability, not certainty. It provides a highly educated guess based on data, significantly reducing your uncertainty but never eliminating it completely. Another common misconception is that AI works on its own without any human help. The truth is, AI is a powerful tool that augments human expertise. You still need people to prepare the data, interpret the results, and make the final strategic decisions. It’s a partnership between human intelligence and artificial intelligence, not a replacement.

Your guide to AI forecasting techniques

Once you’re ready to get started, you’ll find that AI forecasting isn’t a one-size-fits-all solution. Different techniques work better for different types of data and business problems. Think of it like a toolbox—you need to pick the right tool for the job. The main approaches fall into a few key categories, from classic statistical models to more advanced deep learning solutions. Let's walk through the most common techniques so you can get a feel for what might work best for you.

Statistical methods

Before we had complex neural networks, we had statistical methods. These are the foundational, math-based models that have been used for decades. A classic example is ARIMA (AutoRegressive Integrated Moving Average), which is great for simpler time-series data that shows clear trends or seasonal patterns. While powerful, these traditional methods often look at just one or a few factors at a time. They can be a fantastic starting point, especially if your data is straightforward, but they might miss the bigger picture that more complex AI forecasting techniques can capture by analyzing many variables at once.

Machine learning approaches

This is where things get more interesting. Machine learning (ML) models take forecasting a step further by learning from your historical data to predict future trends. Unlike traditional statistical methods, ML models are pros at spotting complex patterns and relationships that are nearly impossible for a person to see. They can process tons of different data points simultaneously—from sales numbers and website traffic to economic news and social media sentiment. This ability to learn from the data makes them more adaptable and often more accurate, providing a solid and flexible foundation for your business forecasting needs.

Deep learning solutions

If ML is a step up, deep learning is a giant leap. These models use complex architectures called neural networks, which are inspired by the human brain. Techniques like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks are specifically designed to recognize intricate patterns in sequential data, making them perfect for time-series forecasting. More recently, time-series transformers have emerged. Think of them like the tech behind ChatGPT, but instead of predicting the next word in a sentence, they’re built to predict the next number in a sequence, offering even more powerful predictive capabilities.

BLOG: A guide to detecting outliers in time-series data

Hybrid and ensemble methods

Why choose just one method when you can combine them? Hybrid and ensemble approaches do exactly that. Hybrid models blend the strengths of different techniques, like pairing a traditional statistical model with an ML algorithm to improve overall accuracy. Ensemble methods take it a step further by combining the predictions from several different models. The idea is that the collective "wisdom" of multiple models is often more reliable than a single one. This approach can lead to more robust and accurate forecasts, smoothing out the weaknesses of any individual model.

How AI improves forecasting accuracy

Traditional forecasting methods often rely on historical averages and linear assumptions, which can miss the mark in a world full of complex variables. AI, on the other hand, fundamentally changes the game by processing vast amounts of data and identifying relationships that are invisible to the human eye. It’s not just about making a better guess; it’s about building a more intelligent and responsive forecasting engine for your business.

By moving beyond simple trend lines, AI-powered forecasting gives you a clearer picture of what’s to come. This allows for more precise inventory management, smarter resource allocation, and more strategic financial planning. Instead of just reacting to the market, you can start anticipating its moves. Let’s break down exactly how AI delivers this step-change in accuracy.

One of the biggest strengths of AI is its ability to find the needle in the haystack. AI models, especially those using deep learning, are brilliant at spotting patterns that are too subtle or complex for people or older statistical methods to catch.

Uncovering hidden patterns

One of the biggest strengths of AI is its ability to find the needle in the haystack. AI models, especially those using deep learning, are brilliant at spotting patterns that are too subtle or complex for people or older statistical methods to catch. This could be a minor seasonal dip that happens every 18 months, a recurring cycle in customer behavior, or a faint connection between weather patterns and product sales. AI learns these intricate relationships directly from your data, allowing it to build a much more nuanced and accurate picture of the future.

Putting analysis on autopilot

Let’s be honest: a huge chunk of forecasting work is spent on cleaning, processing, and analyzing data. It’s time-consuming and tedious. AI forecasting automates much of the data processing and analysis that traditionally bogs down your team. This frees up your experts to focus on what they do best: interpreting the results and making strategic decisions. With AI handling the heavy lifting, you can generate forecasts, set budgets, and plan for the future much more quickly and with greater confidence in the numbers you’re seeing.

Adapting to changes on the fly

Markets change, customer preferences shift, and unexpected events happen. A static forecast quickly becomes obsolete. AI models, however, are designed to be dynamic. They can continuously keep learning and updating as new data flows in. This means your forecasts can adjust in near real-time when a new trend emerges or an external factor disrupts the status quo. This adaptability is incredibly valuable in fast-moving industries like retail or finance, where staying ahead of sudden shifts can make all the difference.

BLOG: How AI transformed data analytics forever 

Using smarter, more complex models

AI forecasting isn’t a single technique; it’s a whole toolkit of advanced methods, including machine learning, deep learning, and sophisticated statistical models. Even better, you can combine these approaches. For instance, hybrid AI models can merge the strengths of machine learning with the reliability of traditional statistics to produce forecasts that are more accurate than either method could achieve on its own. This ability to use more complex and layered models allows you to capture the true complexity of your business environment.

How to choose the right forecasting technique

Picking the right forecasting technique feels a bit like choosing the right tool for a job. You wouldn't use a sledgehammer to hang a picture frame, right? The same logic applies here. The best method depends entirely on your data, your goals, and the resources you have on hand. There’s no single "best" technique, but there is a best technique for you. It’s all about finding the perfect match between the problem you’re trying to solve and the model you use to solve it.

Thinking through a few key questions can help you narrow down the options and land on a method that will deliver accurate, actionable results without overcomplicating things.

Let’s walk through the questions your team needs to consider:

Q1: What does your data look like?

First, take a good, hard look at your data. Is it relatively stable with a clear, predictable trend? Or is it complex, with multiple layers of seasonality and subtle patterns? The nature of your data is the biggest clue you have for choosing a model. Simple, linear data might do just fine with a traditional statistical method. But if you’re dealing with something more intricate, you’ll want a more powerful approach.

As one guide to AI time-series forecasting puts it, "AI models, especially advanced ones like deep learning, are great at spotting patterns that are hard for humans or older methods to see. This includes things like seasonal changes, repeating cycles, or small connections between data points." If your data has these kinds of hidden relationships, an AI model will be much better equipped to find and use them.

Q2: What are your business goals?

What are you trying to achieve with this forecast? Are you making a high-level strategic decision for the next year, or do you need to optimize inventory for every single product on a daily basis? Your business objective should directly inform your choice of technique. A simple forecast might be enough for long-term planning, but for operational efficiency, you’ll need something more precise and dynamic.

The goal is to move from simply reacting to what’s happened to proactively preparing for what’s next. If your goal is real-time optimization, AI is the clear winner.

Q3: What are your resource limitations?

Let’s be practical. Implementing a sophisticated deep learning model requires significant resources: clean data, powerful computing infrastructure, and a team with specialized skills. It’s important to be realistic about what you can support. A model is only useful if you can actually build it, run it, and maintain it over time.

Before you commit to a complex solution, you need to weigh the potential benefits against the investment. As one comprehensive guide explains, "Businesses must consider challenges like data quality, model accuracy, ethical considerations, and cost when implementing AI forecasting." This is where having a managed AI platform can make a huge difference, by handling the infrastructure and operational heavy lifting so your team can focus on building the best model.

BLOG: A practical guide to real-time analytics

Balancing complexity and performance

It can be tempting to reach for the most complex, state-of-the-art model available, but more complex doesn’t always mean better. You need to find the sweet spot between a model’s performance and its complexity. Sometimes, a slightly less accurate model that is easy to understand, explain, and maintain is a much better choice for your business than a "black box" you can’t interpret.

A great strategy is to start simple and add complexity as needed. You can also explore hybrid approaches. According to experts on essential AI forecasting techniques, "AI models continuously learn from new data, refining forecasts and reducing errors compared to static models. Hybrid AI models combine machine learning techniques with traditional statistical methods to improve forecasting accuracy." This allows you to get the best of both worlds—the reliability of statistical methods with the pattern-recognition power of AI.

Tips for a successful implementation

Having a solid grasp of AI forecasting techniques is a great start, but turning that knowledge into real-world results is where the magic happens. A successful implementation isn't just about picking a fancy algorithm; it's about building a solid foundation with your data, making smart choices along the way, and keeping a close eye on performance. Think of it as a process, not a one-time project. When you get the implementation right, you create a reliable forecasting engine that can become a core asset for your business. Let’s walk through the key steps to make sure your forecasting model is set up for success from day one.

When you get the implementation right, you create a reliable forecasting engine that can become a core asset for your business.

Preparing your data for success

Your AI model is only as good as the data you feed it. Before you even think about algorithms, you need to focus on data preparation. This means ensuring your time-series data is clean, relevant, and complete. Start by handling any missing values, correcting inaccuracies, and removing outliers that could skew your results. It’s also crucial to make sure the data you’ve collected is actually relevant to what you’re trying to predict. A thorough data cleaning process is non-negotiable; skipping this step is like building a house on a shaky foundation. Taking the time to get your data in order will pay off immensely in the accuracy and reliability of your forecasts.

How to engineer effective features

Feature engineering is the creative part of the process where you transform your raw data into inputs that better communicate the underlying patterns to your model. For time-series data, this could mean extracting features like the day of the week, month, or year from a timestamp to capture seasonality. You could also create rolling averages to smooth out noise or lag features to show the model what happened in previous time steps. The goal of feature engineering is to make the important signals in your data more obvious. This helps even powerful deep learning models learn faster and perform better, as you’re giving them the most relevant information to work with.

How to select the right model

With so many models to choose from, it’s easy to get overwhelmed. A great strategy is to start simple. Use traditional statistical methods like ARIMA to establish a baseline prediction. This gives you a benchmark to beat. From there, you can explore more complex machine learning and deep learning models to see if they can improve upon that baseline. The best model for you depends on your specific data and business goals. You don't always need the most complex solution; sometimes, a simpler, more interpretable model is the right choice. The key is to test different approaches and see what works best for your unique situation.

Validating your model's performance

Once you’ve trained a model, you need to make sure it actually works. Validation is how you test your model's predictive power on data it has never seen before. This step is critical for ensuring your model can generalize to new, real-world scenarios and isn't just "memorizing" the training data—a problem known as "overfitting." You can use techniques like a hold-out validation set or backtesting, where you pretend to go back in time and see how well your model would have predicted the future. Regularly assessing your model’s performance against actual outcomes is the only way to build trust in its forecasts and confirm it’s ready for production.

Monitoring your model post-launch

Getting your model into production isn't the finish line. The world is constantly changing, and so is your data. A model that was accurate last quarter might not be so reliable today. This is why continuous monitoring is essential. You need to track your model's performance over time to catch any degradation in accuracy, a phenomenon often called "model drift." Setting up a system to automatically monitor performance and alert you when it's time to retrain the model is key for long-term success. Managing the entire stack for continuous improvement is complex, which is why AI development platforms like Cake exist to streamline the infrastructure and keep your AI initiatives running smoothly.

Getting your model into production isn't the finish line. The world is constantly changing, and so is your data.

Setting up your tech stack for success

Building powerful AI forecasting models is one thing, but running them effectively is another challenge entirely. Your success depends heavily on the technology you have in place to support the entire process, from gathering data to deploying your final model. Think of it as building a house: you wouldn't start putting up walls without a solid foundation. Your tech stack is that foundation. It’s the combination of hardware and software that will store your data, power your computations, and ultimately bring your forecasts to life.

Getting this stack right means thinking through every layer. You need the raw power to train complex models, the right storage to handle massive datasets, and the frameworks to build and manage everything efficiently. Each component plays a critical role, and a weak link in the chain can slow down your progress or limit the accuracy of your results. While the steps to implement AI forecasting include data collection and model selection, the underlying technology is what makes it all possible. As you plan your project, considering these technical requirements from the start will save you headaches down the road and set your team up for a much smoother journey.

Computing infrastructure

AI models, especially deep learning ones, are hungry for computational power. Training a model on years of historical time-series data requires a lot of muscle, which is where your computing infrastructure comes in. This is the engine that runs all the complex calculations. For most AI applications, this means using Graphics Processing Units (GPUs), which are designed to handle the parallel processing needed for model training far more efficiently than traditional CPUs. Your choice here often comes down to using on-premise hardware versus cloud-based services. While on-premise gives you full control, the cloud offers incredible flexibility and scalability, allowing you to spin up powerful resources when you need them and scale back down to manage costs. This power is what enables you to improve predictive decision-making by analyzing data at a scale that was previously impossible.

Storage solutions

Time-series data can accumulate incredibly fast. Whether it’s daily sales figures, hourly website traffic, or sensor readings by the second, you need a storage solution that can keep up. Your storage needs to be not only large enough to hold everything but also fast enough to allow your models to access the data without creating bottlenecks. This is why businesses are moving toward solutions like data lakes or data warehouses. These systems are built to handle vast amounts of structured and unstructured data, making them a perfect fit for AI projects. A well-designed storage system is the key to shifting your forecasting from a reactive process to a proactive one, giving you the ability to analyze vast amounts of data for real-time insights.

Processing capabilities

Between your raw data storage and your AI models, you need a way to process and prepare all that information. Raw data is rarely ready for analysis right out of the box. It needs to be cleaned, transformed, and structured in a way your model can understand—a process known as feature engineering. This is where data processing platforms come into play. Tools like Apache Spark are designed to perform these large-scale data transformations quickly and efficiently across distributed computing clusters. Having robust processing capabilities ensures that your data pipeline is fast and reliable, which is a critical step when you implement AI for time series forecasting in a production environment.

BLOG: The best enterprise AI forecasting technologies

Development frameworks

Your data scientists need the right tools to build, train, and test their forecasting models. Development frameworks are libraries of pre-written code that give your team a head start. Instead of building everything from scratch, they can use popular frameworks like TensorFlow, PyTorch, or scikit-learn to implement sophisticated algorithms. These tools streamline the entire model development lifecycle, from experimenting with different approaches to preparing a model for deployment. They provide the essential building blocks that allow your team to focus on creating the most accurate and effective forecasting solutions for your specific business needs, rather than getting bogged down in low-level coding.

Security considerations

When you’re working with business data (especially sales, financial, or customer data) security has to be a top priority. Your tech stack must include strong security measures at every level to protect sensitive information. This includes managing who has access to the data, encrypting data both in transit and at rest, and securing the models themselves from potential threats. Beyond just technical security, it's also important to think about data governance and ethics. As you implement AI forecasting, you'll face challenges around data quality and model accuracy, and having a secure, well-governed framework ensures you’re building solutions that are not only powerful but also responsible and trustworthy.

AI forecasting in the real world

Theory is great, but seeing AI forecasting in action is what really shows its power. Across industries, businesses are using these techniques to make smarter, faster, and more accurate predictions. From managing stock portfolios to stocking store shelves, AI is helping companies get ahead of what’s next. Let's look at a few examples of how different sectors are putting these models to work.

Finance and trading

The financial markets are notoriously volatile, but AI is bringing a new level of clarity. By analyzing complex, ever-changing market data, AI models can make more accurate predictions than traditional methods. This is a huge deal for traders and financial analysts who rely on precise forecasts to make critical decisions. AI is fundamentally changing time series forecasting in finance by offering a much clearer picture in situations where every second and every piece of data counts, helping firms manage risk and spot opportunities.

BLOG: 8 transformative use cases for predictive analytics

Retail operations

For retailers, knowing what customers will want to buy next is the key to success. AI forecasting helps by looking at more than just past sales. It can process tons of different data points at once (e.g., economic news, weather patterns, and even social media sentiment) to create a detailed picture of future demand. This allows retailers to optimize inventory, reduce waste, and make sure popular items are always in stock. The result is a more efficient business and happier customers who can always find what they’re looking for.

Energy management

Energy demand can swing wildly based on weather, time of day, or unexpected events. AI models are perfect for this challenge because they can learn and update their predictions as new information comes in. This adaptability is crucial for energy companies that need to balance the power grid and avoid shortages or waste. Because AI can quickly adjust its forecasts when trends change, it helps ensure a stable and efficient energy supply for everyone, even in fast-moving situations.

Healthcare analytics

In healthcare, accurate forecasting can have a life-changing impact. By predicting patient admission rates, disease outbreaks, or demand for medical supplies, hospitals can allocate resources more effectively. This proactive approach means better staffing, shorter wait times, and improved patient care. AI demand forecasting helps healthcare organizations become more data-driven in a way that wasn't possible before, leading to better outcomes for patients and more efficient operations for providers. It’s about using data to anticipate needs before they become critical.

Supply chain optimization

A smooth supply chain relies on knowing exactly how much product you'll need and when. AI helps companies predict future demand with incredible accuracy by using machine learning to analyze huge amounts of data. These models can spot complex patterns that people might miss, leading to smarter inventory management and lower operational costs. By getting a better handle on demand forecasting, businesses can reduce the risk of stockouts, cut down on excess inventory, and make their entire supply chain more resilient and efficient.

What's next for AI forecasting?

The world of AI forecasting is anything but static. As technology evolves, so do the methods we use to predict the future. The core goal remains the same: to get a clearer picture of what’s ahead. The tools, however, are becoming sharper, smarter, and more integrated into our daily operations. We’re moving beyond just building models to creating intelligent systems that learn and adapt on their own. The next wave of innovation is focused on making forecasting more powerful, transparent, and automated, allowing businesses to make better decisions with greater confidence. Let's look at what the future holds.

New tech on the horizon

Get ready for forecasting models that are more connected and intelligent than ever before. A major trend is the integration of data from the Internet of Things (IoT). Think about sensors on manufacturing equipment or in delivery trucks providing real-time information. Future AI models will be able to process this constant stream of data to make incredibly precise, up-to-the-minute forecasts. We're also seeing the rise of fully autonomous forecasting systems that can not only generate predictions but also adjust their own parameters without human help. These advancements in predictive analytics are paving the way for systems that don't just forecast what will happen but can also recommend the best course of action.

A push for clearer models

As AI models become more complex, there's a growing demand for transparency. It’s no longer enough for a model to be accurate; you also need to understand why it’s making a particular prediction. This is where explainable AI (XAI) comes in. Businesses need to trust their forecasting tools, especially when making critical decisions about inventory, staffing, or finances. Clearer models help you identify potential biases, troubleshoot errors, and gain stakeholder buy-in. Addressing these ethical considerations isn't just about compliance; it's about building robust and reliable systems that you can stand behind. The future is less "black box" and more glass box.

More automation, less manual work

One of the most practical shifts in AI forecasting is the move toward greater automation. Traditionally, a huge amount of time is spent on data preparation, feature engineering, and model tuning—all critical but time-consuming tasks. AI-driven forecasting automates much of this heavy lifting. This means your data science and analytics teams can spend less time on repetitive data processing and more time on strategic interpretation and decision-making. This trend, often part of a broader movement called AutoML, allows teams to test hundreds of models simultaneously to find the best one, dramatically speeding up the development cycle and letting your experts focus on what they do best.

Forecasting on an even bigger scale

The amount of data available to businesses is growing exponentially, and future forecasting techniques are being built to handle this scale. Instead of just relying on historical sales data, models will increasingly pull from vast and diverse datasets to create a more holistic view. AI is uniquely capable of sifting through this noise to find meaningful patterns that would be impossible for a human to spot. By analyzing these vast datasets, forecasting models can generate more nuanced and accurate predictions, giving businesses a significant competitive edge in understanding market dynamics and customer behavior.

Related articles

• In Depth: Predictive Analytics & Forecasting
• Implementing Forecasting Functionality With Cake
• Building Out Time-Series Analysis
• Key Applications of Artificial Intelligence Today

Frequently asked questions

Do I need a massive amount of data to start with AI forecasting?

Not necessarily. While more high-quality data is always helpful, you don't need years and years of perfect records to begin. The key is consistency. Even a year or two of clean, consistent data can be enough for an AI model to start identifying meaningful patterns. It's more important to have reliable data than a massive, messy dataset. You can always start with the data you have and build from there as your model learns and your data collection improves.

What's the first practical step I should take to prepare my business for AI forecasting?

Before you even think about models or algorithms, focus on your data quality. The most practical first step is to conduct a data audit. Identify where your time-series data lives, check it for completeness and accuracy, and start a process for cleaning it up. This means addressing missing values and correcting obvious errors. A clean, trustworthy dataset is the foundation of any successful forecasting project, and getting this right from the start will save you countless headaches later on.

Is a more complex model like deep learning always the better choice?

It's tempting to think that the most advanced model will give you the best results, but that's often not the case. The best model is the one that fits your specific problem. Sometimes, a simpler machine learning or even a traditional statistical model can provide excellent, reliable forecasts, especially if your data isn't overly complex. These simpler models are also easier to understand and maintain. A good strategy is to start simple to set a performance benchmark, and only move to more complex deep learning solutions if you need to capture more intricate patterns to improve accuracy.

How do AI models handle sudden, unexpected events that aren't in my historical data?

This is a great question because it highlights a key point: AI is not a crystal ball. Models learn from historical data, so they can't predict truly unprecedented events like a sudden global crisis. However, what they can do is adapt very quickly once a new event starts generating data. Because AI models can continuously learn, they can adjust to the "new normal" much faster than traditional methods, helping you understand the impact of the disruption and recalibrate your forecasts in near real-time.

Do I need to build an entire tech stack from scratch to support this?

Building the entire infrastructure for AI (from compute power to data storage and processing) can be a huge undertaking. While you can build it yourself, you don't have to. This is where managed AI platforms come in. They provide the pre-built, production-ready infrastructure needed to run your models effectively. Using a platform can handle the heavy lifting of managing the tech stack, which allows your team to focus their energy on what really matters: building accurate models and using those insights to make smarter business decisions.