ProSeNet: An Interpretable Multivariate Time Series Prediction Framework Based on Prototype Networks

ProSeNet (Prototype-based Sequence Network) is an interpretable deep learning framework that combines prototype learning and multimodal technology, proposed to solve the black-box problem of deep learning models in time series prediction. It achieves similarity-based prediction explanations by learning representative time series prototypes and integrates text embeddings to improve prediction accuracy and transparency, making it suitable for high-risk decision-making fields such as finance, healthcare, and energy.

Time series prediction is widely used in finance, energy, healthcare, and other fields. However, while deep learning models (e.g., LSTM, Transformer) improve accuracy, they reduce interpretability. As 'black boxes', deep neural networks are hard to explain the basis of predictions, which is a critical flaw in high-risk scenarios (e.g., predicting patient deterioration). ProSeNet is a solution designed to address this issue.

Prototype learning is inspired by human cognition—humans recognize new things by comparing them with typical samples (prototypes). ProSeNet formalizes this mechanism into a deep learning architecture: it learns representative patterns (prototypes) from training data, and generates prediction results by calculating the similarity between input and prototypes during prediction. Its advantages include: intrinsic interpretability (predictions are linked to specific prototypes), knowledge discovery (prototypes reveal data patterns), and robustness (more resistant to noise).

Multivariate Time Series Encoding

Convert multivariate time series into low-dimensional embeddings via CNN or Transformer encoders, capturing local patterns and long-range dependencies, and modeling interactions between variables.

Prototype Layer Design

Maintain learnable prototype vectors, calculate the distance between input embeddings and prototypes and convert it into similarity, then generate predictions by weighted combination of prototype features. Prototypes are constrained to be close to embeddings of real training samples to ensure they correspond to actual patterns.

Multimodal Extension

Integrate text information (e.g., news, event descriptions), encode it into embeddings using pre-trained language models, fuse with time series embeddings, and learn joint prototypes of 'numerical patterns + text context' (e.g., holiday sales peaks).

Interpretability Mechanisms

• Similarity-based explanation: Output prediction results, the most similar prototype (e.g., 87% similarity to the 2023 Spring Festival pattern), and a similarity heatmap.
• Prototype visualization: Map back to the original space to display typical time series shapes (e.g., morning peak of electricity load on workdays).
• Counterfactual explanation: Analyze changes in predictions when the input is more like a certain prototype.

Application Scenarios

• Financial risk control: Analyze transaction time series to identify risk prototypes and assist in credit evaluation.
• Healthcare: Monitor ICU physiological signals, predict patient deterioration, and explain the basis.
• Energy demand: Integrate electricity consumption data and text events to improve prediction accuracy and enhance trust.

Advantages

• Transparency and trust: The prediction process is transparent, suitable for deployment in regulated industries.
• Knowledge transfer: Prototypes can be transferred across datasets, reducing adaptation costs for new scenarios.
• Error diagnosis: Locate the root cause of prediction errors through prototype matching.

Limitations

• Prototype number selection: Needs to be pre-set; too few leads to incomplete coverage, too many increases computational overhead.
• Complex pattern expression: The ability to represent highly complex non-local patterns may be weaker than purely parametric models.
• High-dimensional data challenge: The curse of dimensionality may lead to unreliable distance metrics.

ProSeNet achieves a balance between prediction accuracy and transparency through the prototype learning mechanism, representing an important direction in interpretable AI (designing inherently interpretable architectures rather than post-hoc explaining black boxes). Future directions include: automatically determining the optimal number of prototypes, supporting more modalities (images, graph structures), and combining causal reasoning to provide deeper explanations.