ForecastGPT

ForecastGPT is a time-series forecasting model based on a large transformer architecture. The model is designed to take in multiple time-series histories, such as sales, weather, and traffic data, and provide accurate, multimodal forecasts. The architecture is based on a combination of fully connected layers, attention layers, and transformer layers, which allow for rich context-aware representations of data. ForecastGPT uses a variational autoencoder to encode long sequences of past data and to make predictions based on this encoded representation. By providing a probabilistic model of the data, ForecastGPT can generate a range of predictions with associated confidence intervals, making it suitable for a range of applications, including volumes forecasting, anomaly detection, and sequence prediction.

ForecastGPT offers several advantages over traditional time-series forecasting techniques. Its transformer architecture allows for excellent contextual awareness, leading to better forecasting accuracy. Additionally, its variational autoencoder provides an end-to-end, probabilistic model of the data, which enables the prediction of detailed confidence intervals. It is also versatile and can be used to forecast a wide variety of data, such as sales, traffic, and weather data. Additionally, it has been designed with scalability and optimization in mind, making it suitable for large-scale forecasting problems.