{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T11:42:43Z","timestamp":1781437363972,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T00:00:00Z","timestamp":1753574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>As the demand for more accurate crime prediction and risk assessment grows, researchers have been developing smarter models that blend statistical methods with machine learning. This study compares a hybrid ARIMA-ANN model with traditional classification techniques to see which best forecast monthly crime risk levels in Gala\u021bi County, Romania. The analysis is based on a newly compiled dataset of 132 monthly observations from January 2014 to December 2024, which combines a broad array of social, economic, and environmental data points. The main variable, \u2018Crime risk\u2019, is based on normalized counts of offenses per capita and divided into five balanced levels: very low, low, moderate, high, and very high. The hybrid ARIMA-ANN model merges the strengths of statistical time series analysis with the flexible learning ability of artificial neural networks. Performance is evaluated against multinomial logistic regression, decision trees, random forests, and support vector machines. Overall, the results show that an ARIMA-ANN model consistently outperforms traditional methods, especially in recognizing patterns over time, seasonal trends, and complex nonlinear relationships in crime data. This study not only sets a new benchmark for crime analytics in Romania but also offers a flexible, scalable framework for classifying crime risk levels across different regions.<\/jats:p>","DOI":"10.3390\/a18080470","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T09:53:02Z","timestamp":1753696382000},"page":"470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hybrid ARIMA-ANN for Crime Risk Forecasting: Enhancing Interpretability and Predictive Accuracy Through Socioeconomic and Environmental Indicators"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7539-4159","authenticated-orcid":false,"given":"Paul","family":"Iacobescu","sequence":"first","affiliation":[{"name":"Department of Computers and Information Technology, \u201cDunarea de Jos\u201d University of Galati, 800201 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4795-4699","authenticated-orcid":false,"given":"Ioan","family":"Susnea","sequence":"additional","affiliation":[{"name":"Department of Computers and Information Technology, \u201cDunarea de Jos\u201d University of Galati, 800201 Galati, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/2190-8532-1-2","article-title":"The spatio-temporal modeling for criminal incidents","volume":"1","author":"Wang","year":"2012","journal-title":"Secur. Inf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ratcliffe, J.H. (2016). Intelligence-Led Policing, Routledge.","DOI":"10.4324\/9781315717579"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Susnea, I., Pecheanu, E., Cocu, A., Istrate, A., Anghel, C., and Iacobescu, P. (2025). Non-Intrusive Monitoring and Detection of Mobility Loss in Older Adults Using Binary Sensors. Sensors, 25.","DOI":"10.20944\/preprints202503.1358.v1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1080\/01621459.2015.1077710","article-title":"Randomized Controlled Field Trials of Predictive Policing","volume":"110","author":"Mohler","year":"2015","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_5","unstructured":"Hyndman, R.J., and Athanasopoulos, G. (2021). Forecasting: Principles and Practice, OTexts."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1177\/1043986214525083","article-title":"Hot Spots Policing: What We Know and What We Need to Know","volume":"30","author":"Weisburd","year":"2014","journal-title":"J. Contemp. Crim. Justice"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.dss.2015.04.012","article-title":"A Decision Support System for predictive police patrolling","volume":"75","author":"Liberatore","year":"2015","journal-title":"Decis. Support Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/S0169-2070(03)00089-X","article-title":"Introduction to crime forecasting","volume":"19","author":"Gorr","year":"2003","journal-title":"Int. J. Forecast."},{"key":"ref_9","first-page":"100342","article-title":"Artificial intelligence & crime prediction: A systematic literature review","volume":"6","author":"Dakalbab","year":"2022","journal-title":"Soc. Sci. Humanit. Open"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0925-2312(01)00702-0","article-title":"Time series forecasting using a hybrid ARIMA and neural network model","volume":"50","author":"Zhang","year":"2003","journal-title":"Neurocomputing"},{"key":"ref_11","unstructured":"(2025, May 21). Monthly Statistical Bulletin of Gala\u021bi County. Available online: https:\/\/galati.insse.ro\/produse-si-servicii\/publicatii-statistice\/."},{"key":"ref_12","unstructured":"(2025, May 21). Activity Reports of the Gala\u021bi County Police Inspectorate. Available online: https:\/\/gl.politiaromana.ro\/ro\/informatii-publice\/transparen-institu-ional\/rapoarte-de-activitate."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from Imbalanced Data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez, A., Garc\u00eda, S., Galar, M., Prati, R.C., Krawczyk, B., and Herrera, F. (2018). Learning from Imbalanced Data Sets, Springer International Publishing.","DOI":"10.1007\/978-3-319-98074-4"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1177\/0002716203262548","article-title":"What Can Police Do to Reduce Crime, Disorder, and Fear?","volume":"593","author":"Weisburd","year":"2004","journal-title":"Ann. Am. Acad. Political Soc. Sci."},{"key":"ref_16","unstructured":"Box, G.E.P., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2016). Time Series Analysis: Forecasting and Control, Wiley. [5th ed.]."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.ijpe.2013.01.009","article-title":"Intermittent demand forecasts with neural networks","volume":"143","author":"Kourentzes","year":"2013","journal-title":"Int. J. Prod. Econ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.ijforecast.2011.04.001","article-title":"Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction","volume":"27","author":"Crone","year":"2011","journal-title":"Int. J. Forecast."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rastogi, A., Sridhar, S., and Gupta, R. (2020, January 24). Comparison of Different Spatial Interpolation Techniques to Thematic Mapping of Socio-Economic Causes of Crime Against Women. Proceedings of the 2020 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA.","DOI":"10.1109\/SIEDS49339.2020.9106690"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8","DOI":"10.32628\/IJSRSET241134","article-title":"Crime Prediction Using Machine Learning and Deep Learning","volume":"11","author":"Karthik","year":"2024","journal-title":"Int. J. Sci. Res. Sci. Eng. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3233\/IDA-2010-0409","article-title":"Evolutionary data analysis for the class imbalance problem","volume":"14","author":"Khoshgoftaar","year":"2010","journal-title":"Intell. Data Anal."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.trc.2014.01.005","article-title":"Short-term traffic forecasting: Where we are and where we\u2019re going","volume":"43","author":"Vlahogianni","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.eswa.2009.05.044","article-title":"An artificial neural network (p,d,q) model for timeseries forecasting","volume":"37","author":"Khashei","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Atesongun, A., and Gulsen, M. (2024). A Hybrid Forecasting Structure Based on Arima and Artificial Neural Network Models. Appl. Sci., 14.","DOI":"10.20944\/preprints202407.0808.v1"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e0224365","DOI":"10.1371\/journal.pone.0224365","article-title":"Machine learning algorithm validation with a limited sample size","volume":"14","author":"Vabalas","year":"2019","journal-title":"PLoS ONE"},{"key":"ref_30","unstructured":"Lundberg, S.M. (2017). Su-In Lee A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems 30, NeurIPS. Available online: https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/8a20a8621978632d76c43dfd28b67767-Abstract.html."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1080\/01900692.2019.1575664","article-title":"Predictive Policing: Review of Benefits and Drawbacks","volume":"42","author":"Meijer","year":"2019","journal-title":"Int. J. Public Adm."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"60153","DOI":"10.1109\/ACCESS.2023.3286344","article-title":"Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions","volume":"11","author":"Mandalapu","year":"2023","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3236009","article-title":"A Survey of Methods for Explaining Black Box Models","volume":"51","author":"Guidotti","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Bennetot","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1177\/1745691616658637","article-title":"Increasing Transparency Through a Multiverse Analysis","volume":"11","author":"Steegen","year":"2016","journal-title":"Perspect. Psychol. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021). An Introduction to Statistical Learning: With Applications in R, Springer. Springer Texts in Statistics.","DOI":"10.1007\/978-1-0716-1418-1"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"Haixiang","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_39","first-page":"20150202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. Math. Phys. Eng. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"219","DOI":"10.7763\/IJMLC.2013.V3.306","article-title":"Data Extract: Mining Context from the Web for Dataset Extraction","volume":"3","author":"Singhal","year":"2013","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e0118432","DOI":"10.1371\/journal.pone.0118432","article-title":"The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets","volume":"10","author":"Saito","year":"2015","journal-title":"PLoS ONE"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e1003285","DOI":"10.1371\/journal.pcbi.1003285","article-title":"Ten Simple Rules for Reproducible Computational Research","volume":"9","author":"Sandve","year":"2013","journal-title":"PLoS Comput. Biol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v059.i10","article-title":"Tidy Data","volume":"59","author":"Wickham","year":"2014","journal-title":"J. Stat. Soft."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.ins.2011.12.028","article-title":"On the use of cross-validation for time series predictor evaluation","volume":"191","author":"Bergmeir","year":"2012","journal-title":"Inf. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/1471-2105-7-91","article-title":"Bias in error estimation when using cross-validation for model selection","volume":"7","author":"Varma","year":"2006","journal-title":"BMC Bioinform."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"319","DOI":"10.2307\/2946681","article-title":"The Effect of Prison Population Size on Crime Rates: Evidence from Prison Overcrowding Litigation","volume":"111","author":"Levitt","year":"1996","journal-title":"Q. J. Econ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102306","DOI":"10.1016\/j.jcrimjus.2024.102306","article-title":"Economic correlates of crime: An empirical test in Houston","volume":"95","author":"Henderson","year":"2024","journal-title":"J. Crim. Justice"},{"key":"ref_49","first-page":"13","article-title":"Exploring the correlation between temperature and crime: A case-crossover study of eight cities in America","volume":"5","author":"Hu","year":"2024","journal-title":"J. Saf. Sci. Resil."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1007730.1007733","article-title":"Editorial: Special issue on learning from imbalanced data sets","volume":"6","author":"Chawla","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.neucom.2019.05.099","article-title":"Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition","volume":"361","author":"Ertekin","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.ins.2013.07.007","article-title":"An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics","volume":"250","author":"Palade","year":"2013","journal-title":"Inf. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1111\/j.1745-9125.2000.tb01413.x","article-title":"Inequality, welfare state, and homicide: Further support for the institutional anomie theory","volume":"38","author":"Savolainen","year":"2000","journal-title":"Criminology"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/8\/470\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:17:01Z","timestamp":1760033821000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/8\/470"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,27]]},"references-count":53,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["a18080470"],"URL":"https:\/\/doi.org\/10.3390\/a18080470","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,27]]}}}