The field of time-series forecasting has witnessed a significant surge in innovation over the past year, driven by the emergence of pre-trained foundation models from prominent organizations like Google, Amazon, and Microsoft, as well as specialized companies like Nixtla. These models have made time-series forecasting more accessible and available, particularly for smaller organizations with limited resources.

The development of zero-shot foundation models has the potential to revolutionize how practitioners approach forecasting tasks. According to Cristian Challu, co-founder and chief science officer at Nixtla, these models will slowly replace traditional methods for most practical use cases as user interfaces improve and general and specialized models continue to advance. However, Challu notes that traditional approaches will still be necessary in certain tasks, such as physics-based time series, where foundation models may not outperform them.

The rise of foundation models will also shift forecasting expertise towards approaches and algorithms for evaluating accuracy and model fairness. This is similar to the trend observed in the large language model (LLM) space, where organizations need to validate models and ensure they are free from systematic bias. As forecasting models become more widespread, it will be essential to develop methods for assessing their accuracy and fairness.

The future of time-series forecasting is expected to involve a growing family of models with different strengths and weaknesses, such as performance, size, speed, and specialization. As the field evolves, we can expect to see more specialized models tailored to specific industries, purposes, and data types, offering users finer control over speed and accuracy. The options and user interfaces for interacting with these models will also become more diverse, making it more challenging to conduct "one size fits all" benchmark analyses.

Despite these challenges, the democratization of access to forecasting and anomaly detection is expected to spark a significant rise in the number of users. This increased accessibility will unlock the benefits of forecasting for more organizations, enabling more people to use data to answer important questions. However, it also means that there will be a greater need for resources and educational opportunities that provide context on forecasting, its appropriate use, and how to interpret results effectively.

To capture the full potential of this future, it will be essential to prioritize communication skills alongside development skills. This may involve training developers to expand their skill sets or recruiting individuals with strong communication skills to learn more about forecasting. By doing so, we can ensure that the benefits of time-series forecasting are accessible to everyone, regardless of their background or organization.

In conclusion, the emergence of pre-trained foundation models is poised to revolutionize the field of time-series forecasting, making it more accessible and available to smaller organizations with limited resources. As the field continues to evolve, it is essential to prioritize accuracy, fairness, and communication to ensure that the benefits of forecasting are democratized and available to all.