A team of researchers from Salesforce AI has introduced Moirai to address the challenge of time series forecasting across various domains and frequencies, aiming to move toward a universal forecasting approach. Traditional deep learning models for time series forecasting are often tailored to specific datasets, leading to computational inefficiencies and the need for extensive resources. The limitations in existing models to handle diverse datasets, frequencies, and variables in a zero-shot manner require the development of a universal forecasting framework.
Deep learning models for time series forecasting are typically trained on specific datasets with fixed contexts and prediction lengths. These models often require significant computational resources and more flexibility to generalize across different domains, frequencies, and variables. In contrast, Moirai’s proposed solution introduces a universal time series forecasting model capable of addressing diverse forecasting tasks in a zero-shot manner. In Moirai’s work, there are four main issues: making a large and varied time series dataset (LOTSA); making multiple patch size projection layers to see patterns in time at different frequencies, setting up a way to deal with predictions for any variable; and using a mixture distribution to model flexible predictive distributions.
Moirai employs novel enhancements to the conventional time series transformer architecture to handle the heterogeneity of arbitrary time series data. To deal with changing frequencies, it learns multiple input and output projection layers. It also uses an any-variate attention mechanism to deal with changing dimensions, and it combines several parametric distributions to make predictions that are flexible. Through comprehensive evaluation in both in-distribution and out-of-distribution settings, Moirai demonstrates its prowess as a zero-shot forecaster, consistently delivering competitive or superior performance compared to full-shot models. The results show that Moirai does better than baselines in in-distribution tests and about as well as other models in out-of-distribution forecasting. This shows that it is reliable and flexible in a variety of situations and datasets.
In conclusion, Moirai offers a versatile and efficient approach to handling diverse forecasting tasks. As a big step forward in the field, its ability to do zero-shot forecasting across different domains, frequencies, and variables will make forecasting easier and use less computing power than traditional deep learning models. Moirai’s performance in both in-distribution and out-of-distribution settings underscores its ability to change how people forecast time series and its applicability across various domains and industries.
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