# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 6 Discussion and Future Work  
Our proposed framework, as we demonstrated in Section 5, has competitive results to the standard \(k\)-means algorithm for time-series clustering. In our comprehensive evaluation, we highlight the significant advantages of our framework in clustering quality, CPU utilization, and memory requirements, especially for larger datasets and for time series of moderate to large lengths. The bottleneck of our method in terms of CPU utilization is the modeling phase. We underline that this step is completely independent for each time series; hence, it can be parallelized, reducing the total running time of our framework. Moreover, SGPR can be run once as an offline step, with its results stored for any future use. The modeling step allows us to significantly reduce the memory requirements for the clustering step since the time-series representation is logarithmic in the length of the original time series, making the clustering step feasible in huge datasets (with a large number of long time series) for the popular, but computationally demanding, DTW distance. The extensive evaluation of our framework, including additional metrics, has been made accessible on GitHub, providing a comprehensive resource for researchers. This transparency ensures the reproducibility of results and facilitates further exploration and validation. The reported CPU utilization times comprise an important addition to the thus far assessment of time-series clustering methods. Moving forward, there are several promising directions for future work. First, an in-depth exploration of the tuning process is warranted to establish a correlation between the nature of the dataset and the optimal parameter selection of the framework. This understanding could lead to refined configurations, enhancing the effectiveness of the proposed framework across diverse datasets and applications. Additionally, the incorporation of different alignment distances in our framework presents an intriguing direction of research. For instance, Frechet distance [37] and Wasserstein distance [38], which can be used in time-series clustering, come with computational challenges. Therefore, our framework, with the reduction of the time-series length, may allow the efficient application of these distances in time-series clustering to longer univariate time-series. Last but not least, the extension of our framework to handle multivariate time series is a natural direction for future work. The ability to effectively analyze and model complex, multi-dimensional time-series data expands the range of potential applications across diverse domains. Conceptualization, D.F. ; Methodology, D.F., P.P., E.P. and M.X. ; Software, E.P. and M.X. ; Validation, E.P. and M.X. ; Investigation, D.F., P.P. and M.X. ; Data curation, M.X. ; Writing--original draft, P.P., E.P. and M.X. ; Writing--review & editing, D.F. and E.P. ; Visualization, E.P. ; Supervision, D.F. ; Funding acquisition, D.F. All authors have read and agreed to the published version of the manuscript. This work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I) under the \"First Call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment grant\", project BALSAM, HFRI-FM17-1424. A significant part of this work was made while [PERSON] was a student at the National Technical University of Athens. The source code, the datasets, and more results with additional parameter configurations and for clustering quality indicators other than ARI can be found in [[https://github.com/pseleni/ts_clustering](https://github.com/pseleni/ts_clustering)]([https://github.com/pseleni/ts_clustering](https://github.com/pseleni/ts_clustering)) (accessed on 25 January 2024). The authors declare no conflicts of interest.