SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems

TL;DR

SPINEX-TimeSeries is a novel similarity-based forecasting algorithm that improves accuracy and interpretability by exploring neighbors across multiple time scales, outperforming many existing methods on diverse datasets.

Contribution

The paper introduces SPINEX-TimeSeries, a new algorithm that leverages similarity and higher-order temporal interactions for enhanced forecasting and explainability.

Findings

Ranks among top 5 in forecasting accuracy

Handles complex temporal dynamics effectively

Offers explainability and computational efficiency

Abstract

This paper introduces a new addition to the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family, tailored specifically for time series and forecasting analysis. This new algorithm leverages the concept of similarity and higher-order temporal interactions across multiple time scales to enhance predictive accuracy and interpretability in forecasting. To evaluate the effectiveness of SPINEX, we present comprehensive benchmarking experiments comparing it against 18 algorithms and across 49 synthetic and real datasets characterized by varying trends, seasonality, and noise levels. Our performance assessment focused on forecasting accuracy and computational efficiency. Our findings reveal that SPINEX consistently ranks among the top 5 performers in forecasting precision and has a superior ability to handle complex temporal dynamics compared to commonly adopted algorithms. Moreover, the algorithm's explainability features, Pareto efficiency, and medium complexity (on the order of O(log n)) are demonstrated through detailed visualizations to enhance the prediction and decision-making process. We note that integrating similarity-based concepts opens new avenues for research in predictive analytics, promising more accurate and transparent decision making.