{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T10:21:29Z","timestamp":1783160489923,"version":"3.54.6"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030299101","type":"print"},{"value":"9783030299118","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-29911-8_3","type":"book-chapter","created":{"date-parts":[[2019,8,23]],"date-time":"2019-08-23T01:03:32Z","timestamp":1566522212000},"page":"24-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Transfer Learning for Financial Time Series Forecasting"],"prefix":"10.1007","author":[{"given":"Qi-Qiao","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick Cheong-Iao","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yain-Whar","family":"Si","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,8,23]]},"reference":[{"key":"3_CR1","unstructured":"Keras (2015). https:\/\/keras.io\/"},{"key":"3_CR2","unstructured":"Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: $$12^{th}$$ USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), pp. 265\u2013283 (2016)"},{"key":"3_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/978-3-319-11758-4_32","volume-title":"Image Analysis and Recognition","author":"T Amaral","year":"2014","unstructured":"Amaral, T., Silva, L.M., Alexandre, L.A., Kandaswamy, C., de S\u00e1, J.M., Santos, J.M.: Transfer learning using rotated image data to improve deep neural network performance. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014. LNCS, vol. 8814, pp. 290\u2013300. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11758-4_32"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: Advances in Neural Information Processing Systems, pp. 153\u2013160 (2007)","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"3_CR5","unstructured":"Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, vol. 10, no. 16, pp. 359\u2013370 (1994)"},{"key":"3_CR6","first-page":"197","volume":"2\u20133","author":"L Deng","year":"2013","unstructured":"Deng, L., Yu, D.: Deep learning for signal and information processing. Found. Trends Signal Process. 2\u20133, 197\u2013387 (2013)","journal-title":"Found. Trends Signal Process."},{"key":"3_CR7","unstructured":"Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 2327\u20132333. AAAI Press (2015)"},{"key":"3_CR8","unstructured":"Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A.: Transfer learning for time series classification. In: 2018 IEEE International Conference on Big Data (Big Data). pp. 1367\u20131376. IEEE (2018)"},{"issue":"1","key":"3_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/for.3980040103","volume":"4","author":"ES Gardner Jr","year":"1985","unstructured":"Gardner Jr., E.S.: Exponential smoothing: the state of the art. Int. J. Forecast. 4(1), 1\u201328 (1985)","journal-title":"Int. J. Forecast."},{"issue":"4","key":"3_CR10","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/j.ijforecast.2006.03.005","volume":"22","author":"ES Gardner Jr","year":"2006","unstructured":"Gardner Jr., E.S.: Exponential smoothing: the state of the art\u2013part ii. Int. J. Forecast. 22(4), 637\u2013666 (2006)","journal-title":"Int. J. Forecast."},{"key":"3_CR11","unstructured":"Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 513\u2013520 (2011)"},{"issue":"4","key":"3_CR12","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1109\/TPWRS.2014.2299801","volume":"29","author":"AU Haque","year":"2014","unstructured":"Haque, A.U., Nehrir, M.H., Mandal, P.: A hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting. IEEE Trans. Power Syst. 29(4), 1663\u20131672 (2014)","journal-title":"IEEE Trans. Power Syst."},{"issue":"5786","key":"3_CR13","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504\u2013507 (2006)","journal-title":"Science"},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.renene.2015.06.034","volume":"85","author":"Q Hu","year":"2016","unstructured":"Hu, Q., Zhang, R., Zhou, Y.: Transfer learning for short-term wind speed prediction with deep neural networks. Renew. Energy 85, 83\u201395 (2016)","journal-title":"Renew. Energy"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Huang, J.T., Li, J., Yu, D., Deng, L., Gong, Y.: Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7304\u20137308. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6639081"},{"issue":"3","key":"3_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v027.i03","volume":"27","author":"R Hyndman","year":"2008","unstructured":"Hyndman, R., Khandakar, Y.: Automatic time series forecasting: the forecast package for R. J. Stat. Softw. 27(3), 1\u201322 (2008)","journal-title":"J. Stat. Softw."},{"issue":"4","key":"3_CR17","first-page":"111","volume":"1","author":"B Karlik","year":"2011","unstructured":"Karlik, B., Olgac, A.V.: Performance analysis of various activation functions in generalized mlp architectures of neural networks. Int. J. Artif. Intell. Expert Syst. 1(4), 111\u2013122 (2011)","journal-title":"Int. J. Artif. Intell. Expert Syst."},{"issue":"3","key":"3_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0194889","volume":"13","author":"S Makridakis","year":"2018","unstructured":"Makridakis, S., Spiliotis, E., Assimakopoulos, V.: Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS One 13(3), 1\u201326 (2018)","journal-title":"PLOS One"},{"issue":"2","key":"3_CR19","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1002\/we.411","volume":"14","author":"EG Ortiz-Garc\u00eda","year":"2011","unstructured":"Ortiz-Garc\u00eda, E.G., Salcedo-Sanz, S., P\u00e9rez-Bellido, \u00c1.M., Gasc\u00f3n-Moreno, J., Portilla-Figueras, J.A., Prieto, L.: Short-term wind speed prediction in wind farms based on banks of support vector machines. Wind Energy 14(2), 193\u2013207 (2011)","journal-title":"Wind Energy"},{"issue":"10","key":"3_CR20","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2009)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Ramachandran, P., Liu, P., Le, Q.: Unsupervised pretraining for sequence to sequence learning. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 383\u2013391. Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/D17-1039"},{"key":"3_CR22","unstructured":"Rosenstein, M.T., Marx, Z., Kaelbling, L.P., Dietterich, T.G.: To transfer or not to transfer. In: NIPS 2005 Workshop on Transfer Learning, vol. 898, pp. 1\u20134 (2005)"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Vu, N.T., Imseng, D., Povey, D., Motlicek, P., Schultz, T., Bourlard, H.: Multilingual deep neural network based acoustic modeling for rapid language adaptation. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7639\u20137643. IEEE (2014)","DOI":"10.1109\/ICASSP.2014.6855086"},{"key":"3_CR24","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.neucom.2011.12.013","volume":"83","author":"B Wang","year":"2012","unstructured":"Wang, B., Huang, H., Wang, X.: A novel text mining approach to financial time series forecasting. Neurocomputing 83, 136\u2013145 (2012)","journal-title":"Neurocomputing"},{"issue":"1","key":"3_CR25","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 9 (2016). https:\/\/doi.org\/10.1186\/s40537-016-0043-6","journal-title":"J. Big Data"},{"key":"3_CR26","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1057\/9781137466297_5","volume-title":"Enterprise Risk Management in Finance","author":"DD Wu","year":"2015","unstructured":"Wu, D.D., Olson, D.L.: Financial risk forecast using machine learning and sentiment analysis. In: Wu, D.D., Olson, D.L. (eds.) Enterprise Risk Management in Finance, pp. 32\u201348. Springer, London (2015). https:\/\/doi.org\/10.1057\/9781137466297_5"},{"key":"3_CR27","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.knosys.2018.05.021","volume":"156","author":"R Ye","year":"2018","unstructured":"Ye, R., Dai, Q.: A novel transfer learning framework for time series forecasting. Knowl. Based Syst. 156, 74\u201399 (2018)","journal-title":"Knowl. Based Syst."},{"key":"3_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1007\/978-3-319-13560-1_60","volume-title":"PRICAI 2014: Trends in Artificial Intelligence","author":"A Yoshihara","year":"2014","unstructured":"Yoshihara, A., Fujikawa, K., Seki, K., Uehara, K.: Predicting stock market trends by recurrent deep neural networks. In: Pham, D.-N., Park, S.-B. (eds.) PRICAI 2014. LNCS (LNAI), vol. 8862, pp. 759\u2013769. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-13560-1_60"},{"key":"3_CR29","unstructured":"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320\u20133328 (2014)"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2019: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29911-8_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T11:02:59Z","timestamp":1695121379000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-29911-8_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030299101","9783030299118"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29911-8_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cuvu, Yanuka Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fiji","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}