What is Forecasting? A Comprehensive Guide to the Basics and Beyond

In ancient Greece, people who sought answers about what the future held for them would make the long journey to the temple of Apollo at Delphi. After lengthy interviews with the temple priests, the pilgrims would bring their questions to the Oracle, a priestess who was believed to have a direct connection to the gods. Entering a trance-like state, the Oracle supposedly delivered the wisdom of the gods. The supplicants would return home believing they had received the guidance they needed to face whatever they were told would be in their futures.

Fast-forward nearly 3,000 years, and people still look for answers about what the future may hold for them. This is especially true in the business world, where leaders look for information that they hope will steer them toward effective decision-making. They want to know how much inventory they should order, which products and services they should concentrate their efforts on, which new markets will bear the most fruit and more. This is what makes business forecasting such an important element of planning.

Before the digital age took hold, business leaders relied on a number of qualitative forecasting methods to help them look into the future.  Among these was the Delphi method, which systematically gathered input from a panel of experts through multiple rounds of anonymous surveys, refining forecasts based on iterative feedback. However, the advent of the computer made it possible to utilize a more data-driven quantitative forecasting approach.

The Oracle of today doesn’t reside in a temple made of marble, but rather buried deep within the mountains of data businesses collect and analyze every day. Although the guidance it provides doesn’t claim to come from divine inspiration, it offers a lot more transparency into how it reaches its conclusions. With new developments in artificial intelligence and machine learning continuing to push the envelope, it’s never been easier for business leaders to look to the horizon and see what’s likely coming their way.

Statistical and Quantitative Forecasting Models

Today’s quantitative approach to business and financial forecasting takes many different forms. Among the most-used forecasting techniques found in modern forecasting systems are:  ‍

• ‍Naïve approach: This only looks to the most-recent data set and makes predictions without adjusting the information at all. ‍
• Moving averages: By collecting data from a pre-determined number of previous time periods, this approach seeks to smooth out any fluctuations in the information that may have been caused by anomalies or one-off events. ‍
• Exponential smoothing: This approach considers data from a range of different periods but prioritizes the most-recent data sets as being most indicative of where conditions may be trending. ‍
• Trend projection: The objective of this technique is to comb through data for signs of upward or downward trends and extrapolates them based on what is believed to be most likely to occur. ‍
• Autoregressive Integrated Moving Average (ARIMA):  This statistical model predicts future values by analyzing past data points, identifying trends, and correcting for seasonality and noise.

Thanks to the calculation capabilities of modern computers, these techniques have been easily integrated into many business forecasting processes. However, technology rarely stands still, and new innovations are leading to more complex and capable systems for forecasting.

Predictive Analytics and Machine Learning in Forecasting

Just as today’s modern forecasting would not be possible without the development of computer technology, advancements in new forms of computing continue to transform the process. In particular, the rise of artificial intelligence is powering new modes of predictive analytics that are capable of highly accurate forecasts without the need for human intervention. By leveraging vast amounts of current and historical data, these programs and platforms can spot patterns in an instant. By adding advanced machine learning algorithms into the mix, the software becomes capable of adapting to what it has seen and carrying that knowledge over into future tasks.

At the core, predictive analytics is no different from the process of forecasting as it has been known for all of history. The process involves using information from the past to predict the future. It begins by determining the problem that needs to be solved — for example, how much inventory should a store carry of a particular product in the lead-up to the holiday shopping season?

From there, historical data is collected and fed into the software. In the example mentioned earlier, the retailer would gather information about how many units of that product it sold in the months of November and December, as well as supplemental details such as how any promotions or discounts may have impacted those sales. In general, the more granular the information is, the more effective and accurate the analytics will become. However, in this case the retailer also should be careful to avoid supplying junk data that could skew the results. Care must be taken to remove any outliers or anomalies that may be the result of errors.  

Once all the data has been gathered and cleaned, the predictive models in the analytical software get to work. Based on past sales data from similar time periods, the algorithm should provide a reasonably accurate model of what the retailer should expect in terms of sales for that specific product. Finally, the retailer can make an informed decision about how many units should be stocked to satisfy consumer demand without carrying too much inventory.

Implementing machine learning into forecasting workflows isn’t a simple plug-and-play experience and should be approached with care. Businesses that want to leverage everything artificial intelligence has to offer should be cautious about the data they gather for training the model, ensuring it is clean and free from anomalies, omissions and outliers that could steer the algorithm in the wrong directions.  

Choosing the right machine learning algorithm is another important consideration. Some of the most commonly used models include: ‍

• ‍Random forest: This creates a series of decision trees to reach a single answer. ‍
• Neural network: Designed to mimic the way the human brain processes information, this machine learning model uses connected nodes of data to identify patterns and make predictions.‍
• Gradient boosting: This model compares predictions made by a series of models to actual results, using what it has learned to refine its predictive capabilities.  

Once the system begins its work, it’s important for businesses to keep a close eye on the results. Any new data sets and inputs should be introduced into the model as soon as possible to ensure it has the opportunity to recalibrate, as well.

As the resources devoted to AI and machine learning scale up, it’s anticipated that the technology will become a much more prominent element of business forecasting. The ability of machine learning to analyze enormous amounts of data quickly and develop extremely detailed models mean it will serve an essential role in helping businesses plan for the future and strengthen their positions in the marketplace.

Time Series Analysis in Forecasting

No matter what type of model is used, most forecasting commonly utilized in business is based on the concept of time series analysis. This is a method that seeks to predict future trends by examining data collected in regular intervals. These increments can be by the year, the month, the day or even the hour depending on the type of predictions being made.

In general, time series analysis seeks to make predictions based on seasonal patterns based on the time of year or cyclical patterns related to a broader set of economic conditions. For instance, a retailer may decide to make purchasing decisions based on seasonal demand. A simple example of this is a grocery store ordering more frozen turkeys in anticipation of the Thanksgiving holiday. On the other hand, cyclical analysis may help that same grocer lower orders of higher-end products due to expected economic downturns. In either case, the decision is made by examining past data and making a prediction about what may occur in the near future.

As mentioned earlier, ARIMA is a common technique used in forecasting with time series analysis. There’s also a variant of ARIMA known as Seasonal Autoregressive Integrated Moving Average (SARIMA), which is designed specifically for data with expected seasonal patterns in addition to other patterns. This means it not only compares current data points with those that preceded them, but also considers the seasonality of the information.

With the current developments in machine learning and AI sweeping through the world of forecasting, new models are being created such as Long Short-Term Memory networks (LSTMs). These are variants of neural networks that have the ability to learn long-term dependencies in sequential data. This makes them extremely effective for business forecasting as well as predicting stock prices and weather patterns.

Challenges in Forecasting and Solutions

No system is entirely airtight, and even the most advanced forecasting models can break down and provide incorrect or incomplete guidance without proper handling. Businesses that want to get the most out of their forecasting processes need to be aware of the most common obstacles that impede these efforts as well as strategies for overcoming them. Some of the most frequently encountered challenges companies face as they work to improve their forecasting accuracy include:

• ‍Data quality: The phrase “garbage in, garbage out” applies to forecasting, as well. Data sets that are riddled with inconsistencies, omissions and inaccuracies can derail even the most sophisticated algorithm. This is why taking the time to pre-process and clean data is a critical step. ‍
• Model selection: Many businesses find themselves struggling because the forecasting model they have selected turns out to be inadequate for the task at hand. Not all models are created equal, and companies need to fully understand what each one is capable of doing as well as their limitations before making a final choice. ‍
• External factors: Forecasting is the process of predicting the future, but the unexpected can throw any predictions out the window. Sudden economic shifts or geopolitical instability can make certain predictions practically useless, which is why it’s important to bear in mind that forecasting is not an exact science even as it becomes more nuanced and accurate.

Change the Game With AI-Driven Forecasting Solutions from ketteQ

You don’t need an Oracle to look into the future anymore. Forecasting has come a long way in the last few decades, with gut feelings and educated guesses giving way to data-driven insights based on complex algorithms. Maintaining your competitive edge in the modern business landscape means utilizing the most effective methods backed by the latest technologies.

As a leader in AI-driven forecasting, ketteQ offers a powerful software platform that empowers businesses to adapt to their evolving challenges. Built with the most sophisticated and robust predictive analytics, real-time data processing and machine learning capabilities, ketteQ gives businesses like yours the actionable insights you need to improve your decision-making processes.

To learn more about how you can future-proof your business with our state-of-the-art forecasting solution, reach out and speak with a member of our team today.

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