Forecasting is a cornerstone of business strategy, providing organizations with a glimpse into the future, allowing them to prepare for uncertainties, make informed decisions, and remain competitive.
The field of business forecasting has long been dominated by traditional methods linear models, spreadsheets, and manual work. While forecasting has proven invaluable, it's important to recognize the limitations of simplistic linear models, which often fall short of capturing the complexities of the business world.
Linear models assume that relationships between variables are linear and constant over time. In reality, business dynamics are often nonlinear and subject to change. Oversimplifying complex systems can lead to inaccurate predictions.
Businesses operate in multifaceted ecosystems where variables interact and influence each other. Linear models struggle to capture these intricate relationships, potentially leading to erroneous forecasts.
Lack of Adaptability:
Linear models typically do not adapt well to sudden changes or disruptions, such as the impact of a global pandemic or technological breakthroughs. They may fail to account for outlier events that can have a profound impact on business operations.
Linear models may overlook emerging trends and opportunities that nonlinear models or more advanced forecasting techniques can detect. Relying solely on linear models can result in missed growth prospects.
Luckily, recent advancements in deep learning have opened up new possibilities for more accurate and efficient forecasting. One such innovation is DeepFeatTimeGPT, a multimodal, probabilistic forecasting system based on a neural network architecture.
What sets DeepFeatTimeGPT apart from other forecasting models is its ability to incorporate multiple data sources and modalities. The neural network can analyze both structured and unstructured data to generate more accurate predictions. Furthermore, the system is probabilistic, meaning it can provide a range of possible outcomes and their associated probabilities, rather than a single-point estimate and thereby account for uncertainty.
In our recent benchmarking study, we showed, that DeepFeatTimeGPT outperforms top-tier consulting firms and investment banks by huge margins in terms of accuracy, transparency, speed, efficiency, and costs.
Our forecasting models are fully integrated into decision workflows into DeOS - the Decision Optimization system, letting you apply this powerful model right out of the box for your business data without the need for technical knowledge.