# Unsupervised Offline Changepoint Detection Ensembles
## 5 Conclusions  
We proposed a novel, although quite intuitive, way of ensembling unsupervised offline changepoint detection procedures in this work. An ensemble approach for the CPD procedure allows us to avoid the time-consuming process of algorithm selection and decreases the uncertainty of the results. Generally, the robustness of the ensemble algorithms assists in the automatization of the signal segmentation process, which is useful in numerous engineering applications. Our study showed that the proposed CPDE procedure is at least promising. Additionally, we compared various common scaling (_MinMax_, _Z-norm_, _MinAbs_, _Rank_), and aggregation (_Min_, _Sum_, _WeightedSum_, _ThresholdSum_) functions. The comparison results formed the basis for the recommendations on scaling and aggregation functions selection for CPDE. Moreover, the proposed approach can act as a framework allowing putting together many methods, algorithms, and cost functions. It positively affects the existing problem of lack of systematization in the CPDE field of knowledge. Both CPD and CPDE procedures were applied in an unsupervised way without fitting phase but for a predefined number of changepoints. The results were obtained on the technical anomaly detection benchmarks--Tennessee Eastman Process and Skoltech Anomaly Benchmark using the NAB scoring algorithm. We also presented pseudocode of the proposed OptEnsemble, WinEnsemble, and BinSegEnsemble algorithms (search methods) and the link to their Python realization. As for the future work ensemble, the approach may be extended over some other search methods. Altogether with the proposed algorithms, they can form a programming framework of state-of-the-art CPDE algorithms similar to the _ruptures_ library. Additionally, more aggregation and scaling (normalizing) functions may be explored and compared. It can expand the proposed recommendation list for selection normalizing and aggregation functions in the technical domain or regardless of the domain at all. Presented algorithms can also be applied to various application domains using the proposed pseudocode and python code from the repository. Applying the algorithms to more datasets and benchmarks, including synthetic ones, may help to generalize results and avoid overfitting to the particular dataset. One more interesting direction is automizing window width selection for Win and WinEnsemble procedures, which can be based on the presented algorithms and code. Generally, the proposed solution forms the framework for changepoint detection ensembles. It can be the base for various search methods, scaling, aggregation functions, and cost functions (models) applicable in offline CPD in numerous applications--from computer networks intrusion analysis and power plant failures diagnostics to ecological changes analysis and various medical applications. Our proposed solution fits the best in unsupervised offline or retrospective analysis applications where the most accurate changepoint detection is required, and models from which the analyzed signals being generated are unknown. Conceptualization and methodology, I.K., V.K., and V.L. ; software, I.K. and V.K. ; validation, V.L. and I.M. ; formal analysis, I.K. and V.K. ; investigation, V.K. ; resources, I.M. ; data curation, I.K. ; writing--original draft preparation, I.K. ; writing--review and editing, V.L. and I.M. ; visualization, V.K. ; supervision, V.L. ; project administration, I.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study. The first dataset (TEP) can be found here: [[https://github.com/[PERSON]/CPDE/tree/master/TEP_data](https://github.com/[PERSON]/CPDE/tree/master/TEP_data)]([https://github.com/[PERSON]/CPDE/tree/master/TEP_data](https://github.com/[PERSON]/CPDE/tree/master/TEP_data)) (accessed on 8 May 2021).