Salesforce AI Research Introduces Moirai-MoE: A Basic MoE Time Series Model That Autonomously Achieves Token-Level Model Specialization

Time series forecasting has long been an integral part of finance, healthcare, meteorology, and supply chain management. Its main goal is to predict future data points based on historical observations, which can be challenging due to the complex and variable nature of time series data. Recent advances in machine learning, particularly basic models, have transformed this domain by creating generalized models capable of handling multiple time series without specialized, case-specific training. These core models mark a significant shift from traditional approaches that required multiple models tailored to specific data sets. However, diversity in time series characteristics, such as variations in frequency, seasonality, and underlying patterns, continues to present substantial challenges for training unified models.

A key problem in time series forecasting is the effective handling of data heterogeneity. Time series data from different sources vary significantly in frequency, distribution, and structure. Current forecast models often rely on human-defined frequency-based specialization to address this diversity. However, frequency alone is not a reliable indicator of a time series pattern, as data with similar frequencies may exhibit different behaviors. On the contrary, data with different frequencies may show similar patterns. This approach should capture the complexity and diversity inherent in real-world time series. Another challenge lies in the non-stationary nature of time series data, where the statistical properties of the data change over time, making it difficult to accurately model with frequency-based grouping.

Existing time series forecasting methods attempt to address data variability with varied approaches. For example, models such as TEMPO and UniTime incorporate language-based cues to help the model discern different data sources, achieving limited specialization at the data set level. Other models, such as TimesFM, maintain built-in dictionaries of specific frequencies to help distinguish between data types based on frequency. However, many models, including the widely recognized Chronos series, opt for a generalized structure without specialized modules, which increases model complexity and high parameter demands. The challenge with these methods is their inability to fully capture the diverse nature of time series data, as frequency alone only sometimes correlates with underlying data patterns, leading to inefficiencies and compromising model accuracy. .

Researchers from Salesforce ai Research, the National University of Singapore, and the Hong Kong University of Science and technology introduced an innovative model called MOIRAI-MoE. MOIRAI-MoE integrates sparse matching of experts (MoE) within its Transformer architecture, enabling token-level specialization without human-defined frequency heuristics. This data-driven approach minimizes reliance on predefined frequency-based layers and uses a single input/output projection layer, allowing the model to automatically capture and represent diverse patterns. By achieving token-level specialization, MOIRAI-MoE provides a more flexible and efficient solution capable of better representing the unique characteristics of varied time series data without requiring separate models for each frequency category.

The MOIRAI-MoE architecture leverages an activation function that assigns each token to an appropriate expert within the Transformer layers based on token pooling derived from a pre-trained model. This clustering approach is guided by the Euclidean distance to centroids, allowing the same expert to process tokens with similar patterns, while specialized experts handle diverse tokens. By incorporating 32 expert networks, each focusing on unique time series characteristics, MOIRAI-MoE effectively reduces computational overhead while improving its ability to generalize across different types of data. This approach allows MOIRAI-MoE to excel at representing non-stationary time series data by dynamically adapting to changing patterns within the data.

Extensive testing on 39 data sets demonstrated the superior performance of MOIRAI-MoE in both distributed forecasting and zero-forecasting scenarios. For forecasting in distribution, MOIRAI-MoE outperformed its dense model counterpart by up to 17%, showing a significant improvement in accuracy while using up to 65 times fewer activated parameters than other leading models, including TimesFM and Chronos. In zero-shot forecasting, where the model was tested on data sets not included in the training data, the performance of MOIRAI-MoE outperformed traditional models. In these tests, MOIRAI-MoE achieved a 3-14% improvement in continuous ranked probability score (CRPS) and an 8-16% improvement in mean absolute scaled error (MASE) over previous models. These results underline the strong generalization ability of the model without requiring task-specific training.

This research presents key findings that highlight the advances that MOIRAI-MoE brings to time series forecasting:

1. Data-driven specialization: By achieving token-level specialization through a sparse combination of experts, MOIRAI-MoE overcomes the limitations of human-defined frequency specialization, allowing for a more nuanced representation of time series diversity.
2. Computational efficiency: Sparse expert model activation dramatically reduces computational demands, achieving up to 65 times fewer activated parameters while maintaining high accuracy.
3. Performance gains: Testing on various data sets confirmed that MOIRAI-MoE outperforms dense models and fundamental models such as TimesFM and Chronos, achieving a 17% improvement over its dense counterparts in distribution tests.
4. Scalability and generalization: MOIRAI-MoE demonstrates strong zero performance, making it highly applicable to real-world forecasting tasks without requiring specialized training for each application, which is critical in various applications such as finance, healthcare, and climate modeling.

In conclusion, MOIRAI-MoE represents a significant advance in time series forecasting by introducing a flexible data-driven approach that overcomes the limitations of frequency-based specialization. With its sparse combination of expert architecture, MOIRAI-MoE addresses the diverse and non-stationary nature of time series data and achieves significant performance and computational efficiency gains. This novel approach underscores the potential of token-level specialization, paving the way for future improvements to basic time series models and expanding the utility of zero-shot forecasting across various industries and applications.

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