Fast Parallel Constrained Viterbi Algorithm for Big Data with Applications to Financial Time Series

Abstract

A new fast parallel constrained Viterbi algorithm for big data is proposed in this paper. We provide a detailed analysis of its performance on big data frameworks. This performance analysis includes the evaluation of execution time, speedup, and prediction accuracy. Additionally, we compare the impact of the proposed approach on the performance of our parallel constrained algorithm with other benchmark versions. We use synthetic data and real-world data in our experiments to describe the behavior of our algorithm for different data sizes and different numbers of nodes. We demonstrate that this algorithm is fast, highly efficient, and scalable when it runs on spark framework and its prediction quality is acceptable since there is no deterioration or reduction observed.