{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T22:40:26Z","timestamp":1770072026676,"version":"3.49.0"},"publisher-location":"Berlin, Heidelberg","reference-count":36,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"value":"9783662680131","type":"print"},{"value":"9783662680148","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-662-68014-8_2","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T19:01:34Z","timestamp":1695322894000},"page":"41-55","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["TSPredIT: Integrated Tuning of\u00a0Data Preprocessing and\u00a0Time Series Prediction Models"],"prefix":"10.1007","author":[{"given":"Rebecca","family":"Salles","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esther","family":"Pacitti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo","family":"Bezerra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Celso","family":"Marques","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carla","family":"Pacheco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carla","family":"Oliveira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabio","family":"Porto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo","family":"Ogasawara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"2_CR1","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"2_CR2","unstructured":"Bischl, B., et al.: mlr: machine learning in R. J. Mach. Learn. Res. 17(170), 1\u20135 (2016)"},{"key":"2_CR3","unstructured":"Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)"},{"issue":"10","key":"2_CR4","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1080\/0740817X.2014.999180","volume":"47","author":"C Cheng","year":"2015","unstructured":"Cheng, C., et al.: Time series forecasting for nonlinear and non-stationary processes: a review and comparative study. IIE Trans. (Ins. Ind. Eng.) 47(10), 1053\u20131071 (2015). https:\/\/doi.org\/10.1080\/0740817X.2014.999180","journal-title":"IIE Trans. (Ins. Ind. Eng.)"},{"issue":"3","key":"2_CR5","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.ijforecast.2012.09.002","volume":"29","author":"A Davydenko","year":"2013","unstructured":"Davydenko, A., Fildes, R.: Measuring forecasting accuracy: the case of judgmental adjustments To SKU-level demand forecasts. Int. J. Forecast. 29(3), 510\u2013522 (2013). https:\/\/doi.org\/10.1016\/j.ijforecast.2012.09.002","journal-title":"Int. J. Forecast."},{"key":"2_CR6","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/S0169-7161(96)14010-4","volume":"14","author":"F Diebold","year":"1996","unstructured":"Diebold, F., Lopez, J.: 8 Forecast evaluation and combination. Handb. Stat. 14, 241\u2013268 (1996). https:\/\/doi.org\/10.1016\/S0169-7161(96)14010-4","journal-title":"Handb. Stat."},{"issue":"1","key":"2_CR7","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1198\/073500102753410444","volume":"20","author":"F Diebold","year":"2002","unstructured":"Diebold, F., Mariano, R.: Comparing predictive accuracy. J. Bus. Econ. Stat. 20(1), 134\u2013144 (2002). https:\/\/doi.org\/10.1198\/073500102753410444","journal-title":"J. Bus. Econ. Stat."},{"key":"2_CR8","first-page":"299","volume-title":"Compstat 2008","author":"MJA Eugster","year":"2008","unstructured":"Eugster, M.J.A., Leisch, F.: Bench plot and mixed effects models: first steps toward a comprehensive benchmark analysis toolbox. In: Brito, P. (ed.) Compstat 2008, pp. 299\u2013306. Physica Verlag, Heidelberg, Germany (2008)"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Garcia, S., Luengo, J., Herrera, F.: Data Preprocessing in Data Mining. Springer (aug 2014). https:\/\/doi.org\/10.1007\/978-3-319-10247-4","DOI":"10.1007\/978-3-319-10247-4"},{"key":"2_CR10","unstructured":"Gujarati, D.N.: Essentials of Econometrics. SAGE (sep 2021)"},{"issue":"3","key":"2_CR11","doi-asserted-by":"publisher","first-page":"348","DOI":"10.3102\/1076998619832248","volume":"44","author":"J Hao","year":"2019","unstructured":"Hao, J., Ho, T.: Machine learning made easy: a review of Scikit-learn package in python programming language. J. Educ. Behav. Stat. 44(3), 348\u2013361 (2019). https:\/\/doi.org\/10.3102\/1076998619832248","journal-title":"J. Educ. Behav. Stat."},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Hyndman, R., Khandakar, Y.: Automatic time series forecasting: The forecast package for R. J. Stat. Softw. 27(3), 1\u201322 (2008). https:\/\/doi.org\/10.18637\/jss.v027.i03","DOI":"10.18637\/jss.v027.i03"},{"issue":"3","key":"2_CR13","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/S0169-2070(01)00110-8","volume":"18","author":"R Hyndman","year":"2002","unstructured":"Hyndman, R., Koehler, A., Snyder, R., Grose, S.: A state space framework for automatic forecasting using exponential smoothing methods. Int. J. Forecast. 18(3), 439\u2013454 (2002). https:\/\/doi.org\/10.1016\/S0169-2070(01)00110-8","journal-title":"Int. J. Forecast."},{"key":"2_CR14","unstructured":"Hyndman, R.J., Athanasopoulos, G.: Forecasting: principles and practice. OTexts (may 2018)"},{"key":"2_CR15","doi-asserted-by":"publisher","unstructured":"Iza\u00fa, L., et al.: Towards robust cluster-based hyperparameter optimization. In: Anais do Simp\u00f3sio Brasileiro de Banco de Dados (SBBD), pp. 439\u2013444. SBC (sep 2022). https:\/\/doi.org\/10.5753\/sbbd.2022.224330","DOI":"10.5753\/sbbd.2022.224330"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Khalid, R., Javaid, N.: A survey on hyperparameters optimization algorithms of forecasting models in smart grid. Sustain. Cities Soc. 61, 102275 (2020). https:\/\/doi.org\/10.1016\/j.scs.2020.102275","DOI":"10.1016\/j.scs.2020.102275"},{"issue":"4","key":"2_CR17","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1145\/2935694.2935698","volume":"44","author":"A Kumar","year":"2016","unstructured":"Kumar, A., McCann, R., Naughton, J., Patel, J.M.: Model selection management systems: the next frontier of advanced analytics. ACM SIGMOD Rec. 44(4), 17\u201322 (2016). https:\/\/doi.org\/10.1145\/2935694.2935698","journal-title":"ACM SIGMOD Rec."},{"issue":"4","key":"2_CR18","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TSE.2008.35","volume":"34","author":"S Lessmann","year":"2008","unstructured":"Lessmann, S., Baesens, B., Mues, C., Pietsch, S.: Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Trans. Softw. Eng. 34(4), 485\u2013496 (2008). https:\/\/doi.org\/10.1109\/TSE.2008.35","journal-title":"IEEE Trans. Softw. Eng."},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Lim, B., Zohren, S.: Time-series forecasting with deep learning: a survey. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 379(2194), 20200209 (2021). https:\/\/doi.org\/10.1098\/rsta.2020.0209","DOI":"10.1098\/rsta.2020.0209"},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Lindemann, B., M\u00fcller, T., Vietz, H., Jazdi, N., Weyrich, M.: A survey on long short-term memory networks for time series prediction. In: Procedia CIRP. vol. 99, pp. 650\u2013655 (2021). https:\/\/doi.org\/10.1016\/j.procir.2021.03.088","DOI":"10.1016\/j.procir.2021.03.088"},{"issue":"12","key":"2_CR21","doi-asserted-by":"publisher","first-page":"2346","DOI":"10.1109\/TKDE.2018.2876857","volume":"31","author":"J Lu","year":"2019","unstructured":"Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 31(12), 2346\u20132363 (2019). https:\/\/doi.org\/10.1109\/TKDE.2018.2876857","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2_CR22","unstructured":"Moreno, A.V., Rivas, A.J.R., Godoy, M.D.P.: predtoolsTS: Time Series Prediction Tools. Tech. rep.,https:\/\/cran.r-project.org\/package=predtoolsTS (2018)"},{"key":"2_CR23","doi-asserted-by":"publisher","unstructured":"Mumuni, A., Mumuni, F.: Data augmentation: a comprehensive survey of modern approaches. Array 16, 100258 (2022). https:\/\/doi.org\/10.1016\/j.array.2022.100258","DOI":"10.1016\/j.array.2022.100258"},{"key":"2_CR24","doi-asserted-by":"publisher","unstructured":"Ogasawara, E., Martinez, L., De Oliveira, D., Zimbr\u00e3o, G., Pappa, G., Mattoso, M.: Adaptive normalization: a novel data normalization approach for non-stationary time series. In: Proceedings of the International Joint Conference on Neural Networks (2010). https:\/\/doi.org\/10.1109\/IJCNN.2010.5596746","DOI":"10.1109\/IJCNN.2010.5596746"},{"key":"2_CR25","doi-asserted-by":"publisher","unstructured":"Pacheco, C., et al.: Exploring data preprocessing and machine learning methods for forecasting worldwide fertilizers consumption. In: Proceedings of the International Joint Conference on Neural Networks. vol. 2022-July (2022). https:\/\/doi.org\/10.1109\/IJCNN55064.2022.9892325","DOI":"10.1109\/IJCNN55064.2022.9892325"},{"key":"2_CR26","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"2_CR27","unstructured":"Ramey, J.A.: sorting hat: sorting hat. Tech. rep., https:\/\/cran.r-project.org\/web\/packages\/sortinghat\/index.html (2013)"},{"issue":"5","key":"2_CR28","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1162\/NECOa00947","volume":"29","author":"ZY Ran","year":"2017","unstructured":"Ran, Z.Y., Hu, B.G.: Parameter identifiability in statistical machine learning: a review. Neural Comput. 29(5), 1151\u20131203 (2017). https:\/\/doi.org\/10.1162\/NECOa00947","journal-title":"Neural Comput."},{"key":"2_CR29","doi-asserted-by":"publisher","unstructured":"Salles, R., Assis, L., Guedes, G., Bezerra, E., Porto, F., Ogasawara, E.: A framework for benchmarking machine learning methods using linear models for univariate time series prediction. In: Proceedings of the International Joint Conference on Neural Networks. vol. 2017-May, pp. 2338\u20132345 (2017). https:\/\/doi.org\/10.1109\/IJCNN.2017.7966139","DOI":"10.1109\/IJCNN.2017.7966139"},{"key":"2_CR30","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.knosys.2018.10.041","volume":"164","author":"R Salles","year":"2019","unstructured":"Salles, R., Belloze, K., Porto, F., Gonzalez, P., Ogasawara, E.: Nonstationary time series transformation methods: an experimental review. Knowl.-Based Syst. 164, 274\u2013291 (2019). https:\/\/doi.org\/10.1016\/j.knosys.2018.10.041","journal-title":"Knowl.-Based Syst."},{"key":"2_CR31","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.neucom.2021.09.067","volume":"467","author":"R Salles","year":"2022","unstructured":"Salles, R., Pacitti, E., Bezerra, E., Porto, F., Ogasawara, E.: TSPred: a framework for nonstationary time series prediction. Neurocomputing 467, 197\u2013202 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.09.067","journal-title":"Neurocomputing"},{"key":"2_CR32","doi-asserted-by":"publisher","unstructured":"Sarwar Murshed, M., Murphy, C., Hou, D., Khan, N., Ananthanarayanan, G., Hussain, F.: Machine learning at the network edge: a survey. ACM Comput. Surv. 54(8), 1\u201337 (2022). https:\/\/doi.org\/10.1145\/3469029","DOI":"10.1145\/3469029"},{"key":"2_CR33","doi-asserted-by":"publisher","unstructured":"Talavera, E., Iglesias, G., Gonz\u00e1lez-Prieto, A., Mozo, A., G\u00f3mez-Canaval, S.: Data Augmentation techniques in time series domain: A survey and taxonomy (jun 2022). https:\/\/doi.org\/10.48550\/arXiv.2206.13508,http:\/\/arxiv.org\/abs\/2206.13508","DOI":"10.48550\/arXiv.2206.13508"},{"issue":"4","key":"2_CR34","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U Von Luxburg","year":"2007","unstructured":"Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395\u2013416 (2007). https:\/\/doi.org\/10.1007\/s11222-007-9033-z","journal-title":"Stat. Comput."},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Wen, Q., et al.: Time series data augmentation for deep learning: a survey. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 4653\u20134660 (2021)","DOI":"10.24963\/ijcai.2021\/631"},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Wickham, H.: Advanced R. CRC Press, second edn. (may 2019)","DOI":"10.1201\/9781351201315"}],"container-title":["Lecture Notes in Computer Science","Transactions on Large-Scale Data- and Knowledge-Centered Systems LIV"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-662-68014-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T19:01:54Z","timestamp":1695322914000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-662-68014-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783662680131","9783662680148"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-662-68014-8_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}