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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p style='mso-margin-top-alt:0cm;margin-right:0cm;margin-bottom:7.5pt;margin-left:0cm;background:white'><span class=si-deadline><span style='font-size:9.0pt;font-family:"Arial",sans-serif;color:#222222'><<< Deadline for manuscript submissions: <b>15 October 2022</b> >>></span></span><span style='font-size:9.0pt;font-family:"Arial",sans-serif;color:#222222'><o:p></o:p></span></p><p class=MsoNormal style='margin-bottom:7.5pt;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif;color:#222222'>Dear Colleagues, <br><br>Time series data represent the change and evolution of phenomena encountered in several domains ranging from finance, transportation, energy and manufacturing up to healthcare, medicine and retail. Nowadays, the abundance of big time series data has developed a great interest in the development of effective and efficient methods for various tasks’ time series analyses, including anomaly detection in time series, time series decomposition, time series segmentation, trend analysis, time series classification and clustering, time series storage, time series visualization and time series forecasting. Of particular interest is the use of the current state-of-the-art machine learning and deep learning approaches for effectively addressing these tasks. The aim of this Special Issue is to bring together academics and practitioners to exchange and discuss the latest innovations and applications of machine learning and deep learning methods on time series analysis and forecasting tasks. Papers addressing, but not limited to the following topics, will be considered for publication:<o:p></o:p></span></p><ul style='margin-top:0cm' type=disc><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Anomaly/outlier detection on time series<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Correlation and causation among time series<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Time series decomposition methods<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Trend analysis in time series<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Time series segmentation<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Time series classification<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Time series clustering<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Time series storage<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Time series visualization<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Automatic feature extraction from time series data<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Univariate time series forecasting<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Multivariate time series forecasting<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Deployment of time series forecasting models in production<o:p></o:p></span></li><li class=MsoNormal style='color:#222222;margin-bottom:7.5pt;mso-list:l0 level1 lfo1;background:white'><span style='font-size:9.0pt;font-family:"Arial",sans-serif'>Efficiency issues on time series forecasting models<o:p></o:p></span></li></ul><p class=MsoNormal>For more details and Manuscript Submission Information please visit<o:p></o:p></p><p class=MsoNormal><a href="https://www.mdpi.com/journal/electronics/special_issues/machine_learning_forecasting">https://www.mdpi.com/journal/electronics/special_issues/machine_learning_forecasting</a> <o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal>Kind regards,<o:p></o:p></p><p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><b><span lang=EN-GB style='font-size:8.0pt;font-family:"Verdana",sans-serif;color:#333399'>Dionysios Kehagias</span></b><span lang=EN-GB style='font-size:8.0pt;font-family:"Verdana",sans-serif;color:#333399'>, PhD<br></span><i><span style='font-size:8.0pt;font-family:"Verdana",sans-serif;color:#333399'>Principal</span></i><i><span lang=EN-GB style='font-size:8.0pt;font-family:"Verdana",sans-serif;color:#333399'> Researcher</span></i><span lang=EN-GB style='font-size:8.0pt;font-family:"Verdana",sans-serif;color:#333399'><br>Centre for Research and Technology Hellas <br>Information Technologies Institute (CERTH/ITI) <br>6th Km. Charilaou-Thermi Rd., 57001, Thessaloniki, Greece. <br>t: <a href="tel:%2B30-2311257716" target="_blank"><span style='color:blue'>+30-2311257716</span></a>, m:<a href="tel:%2B30-6944477886" target="_blank"><span style='color:blue'>+30-6944477886</span></a>, f:<a href="tel:%2B302310474128" target="_blank"><span style='color:blue'>+30-2310474128</span></a></span><span lang=IT style='font-size:8.0pt;font-family:"Verdana",sans-serif;color:#333399'><br><a href="http://www.iti.gr/" target="_blank"><span lang=EN-GB style='color:blue'>http://www.iti.gr</span></a></span><span style='font-size:8.0pt;color:#1F497D'><o:p></o:p></span></p><p class=MsoNormal><span style='font-size:7.0pt;color:#A6A6A6'>CONFIDENTIALITY NOTICE: The information in this email and any attachments thereto are confidential, except where the e-mail specifically states that its contents can be disclosed, and is intended solely for the use of its intended recipient(s). It may also be subject to legal privilege or otherwise protected from disclosure.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:7.0pt;color:#A6A6A6'>If you are not the intended recipient of this communication, you are hereby notified that any disclosure, copying, distribution, or use of the information contained in or attached to this e-mail is strictly prohibited. Please refrain from disclosing its contents to any third party, but notify the sender immediately and make sure to delete and permanently destroy the e-mail (including any attachments thereto) from your filing systems. Internet communications are not secure and therefore sender/CERTH does not accept any liability for the contents of this message and for any damage whatsoever that may be caused by viruses.<o:p></o:p></span></p></div></body></html>