BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis
Abstract
:1. Introduction
2. Data and Methods
2.1. The BFAST Lite Algorithm
2.2. Testing the Performance of BFAST Lite
2.2.1. Compared Algorithms
2.2.2. Satellite Data
2.2.3. Reference Data and Validation
3. Results
3.1. Accuracy Comparison
3.2. Run Time Benchmark
4. Discussion and Outlook
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AIC | Akaike’s Information Criterion. 3, 11 |
BFAST | Breaks For Additive Season and Trend. 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14 |
BIC | Bayesian Information Criterion. 3, 5, 9, 11 |
CCDC | Continuous Change Detection and Classification. 1, 2 |
CGLS-LC100 | Copernicus Global Land Services Land Cover 100 m. 3, 6, 13 |
EWMACD | Exponentially Weighted Moving Average Change Detection. 1 |
IIASA | International Institute for Applied Systems Analysis. 6, 13 |
JUST | Jumps Upon Spectrum and Trend. 1 |
LWZ | information criterion of Liu,Wu and Zidek. 3, 5, 8, 9, 11, 12, 13 |
OLS-MOSUM | ordinary least squares residual moving sum. 3, 4, 6, 8, 10 |
RSS | residual sum of squares. 3, 11 |
STL | Seasonal decomposition of Time series by Loess. 3, 11 |
TSCCD | Time-Series Classification approach based on Change Detection. 1 |
Appendix A
Parameter | BFAST | BFAST Lite |
---|---|---|
Minimum segment size h | 15% by default | 15% by default |
Trend component | Always included | Included by default, can be disabled |
Seasonality component | Either harmonics or seasonal dummies | Harmonics, seasonal dummies, both or external regressor |
Maximum harmonic order | Preset to 3 | Customisable, defaults to 3 |
Number of seasonal dummies | Preset to equal to observations per year | Customisable, defaults to the number of observations per year but can be fewer |
Maximum number of iterations | 10 by default | 1 |
Structural change test type | OLS-MOSUM by default | None, from version 1.7: optional with none by default |
Structural change test significance threshold | 0.05 by default | None, from version 1.7: optional with 0.05 by default |
Decomposition algorithm | STL by default or stlplus | None by default, STL or stlplus on any of the components |
Break number selection criterion | Preset to BIC | LWZ by default, BIC, RSS |
Autoregressive components | None (not supported) | None by default, seasonal lag, trend lag, or both |
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Algorithm | Sensitivity | Specificity | Precision | F1 score | OA | Beta |
---|---|---|---|---|---|---|
BFAST Lite (BIC) | 0.737 | 0.881 | 0.607 | 0.666 | 0.852 | 0.130 |
BFAST Lite (LWZ) | 0.569 | 0.943 | 0.715 | 0.633 | 0.868 | 0.146 |
BFAST (linear int.) | 0.880 | 0.807 | 0.518 | 0.653 | 0.821 | 0.362 |
BFAST (stlplus) | 0.811 | 0.832 | 0.540 | 0.648 | 0.827 | 0.271 |
BFAST Monitor | 0.852 | 0.851 | 0.587 | 0.695 | 0.851 | 0.265 |
Algorithm | Advantages | Disadvantages |
---|---|---|
BFAST [2] | Detects breaks in trends and seasonality separately | Slow, limited number of parameters, overestimates change |
BFAST Monitor [3] | Designed to detect breaks at the end of time series (near real-time), fastest, many tunable parameters | By default not designed for multiple breakpoints, overestimates change |
BFAST Lite (this study) | Faster than BFAST, designed for multiple breakpoints, many tunable parameters, lowest bias between sensitivity and precision, highest OA | Needs parameter tuning to optimise performance, does not differentiate between breaks in seasonality and trend |
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Masiliūnas, D.; Tsendbazar, N.-E.; Herold, M.; Verbesselt, J. BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis. Remote Sens. 2021, 13, 3308. https://doi.org/10.3390/rs13163308
Masiliūnas D, Tsendbazar N-E, Herold M, Verbesselt J. BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis. Remote Sensing. 2021; 13(16):3308. https://doi.org/10.3390/rs13163308
Chicago/Turabian StyleMasiliūnas, Dainius, Nandin-Erdene Tsendbazar, Martin Herold, and Jan Verbesselt. 2021. "BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis" Remote Sensing 13, no. 16: 3308. https://doi.org/10.3390/rs13163308