MMTransformer: a multivariate time-series resource forecasting model for multi-component applications | JoVE Visualize

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Efficient resource forecasting in complex multi-component applications is challenging due to inter-component dependencies and resource interactions.
Existing univariate and single-step prediction models fail to capture the intricate dynamics of these systems, leading to suboptimal performance.
The need for advanced multivariate time series forecasting models that account for system complexity is critical for effective resource management.

Purpose of the Study:

To introduce MMTransformer, a novel multivariate time series forecasting model specifically designed for multi-component applications.
To address the limitations of existing methods by incorporating inter-component dependencies and dynamic information variations.
To significantly enhance the accuracy of resource prediction in complex application environments.

Main Methods:

Developed MMTransformer, a multivariate time series forecasting model.
Implemented a segmented embedding strategy for effective sequence feature capture.
Utilized a multi-stage attention mechanism to model inter-variable dependencies.
Employed a multi-scale encoder-decoder structure to adapt to local and global information dynamics.

Main Results:

MMTransformer demonstrated significant improvements over traditional models (LSTM, GRU, RNN), with average reductions of 42.15% in Mean Squared Error (MSE) and 35.37% in Mean Absolute Error (MAE).
Compared to state-of-the-art models (Fedformer, Autoformer, Informer), MMTransformer achieved average reductions of 27.14% in MSE and 25.55% in MAE.
Experimental results on courseware production and digital human video creation systems validate the model's superior performance in resource prediction.

Conclusions:

MMTransformer effectively captures sequence features and inter-variable dependencies crucial for accurate resource forecasting in multi-component applications.
The model's multi-scale architecture allows adaptation to dynamic information variations, leading to enhanced prediction accuracy.
MMTransformer represents a significant advancement in multivariate time series forecasting for complex computational systems.