{"id":4261,"date":"2026-01-16T10:58:42","date_gmt":"2026-01-16T10:58:42","guid":{"rendered":"https:\/\/devserver.admin.uoc.gr\/damsl\/?page_id=4261"},"modified":"2026-01-16T11:01:56","modified_gmt":"2026-01-16T11:01:56","slug":"damsl-211-time-series-analysis","status":"publish","type":"page","link":"https:\/\/mscs.uoc.gr\/damsl\/damsl-211-time-series-analysis\/","title":{"rendered":"DAMSL-211 Time series analysis"},"content":{"rendered":"\t\t
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Type<\/strong><\/p>

Elective<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Course Code<\/strong><\/p>

DAMSL-211<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Teaching Semester<\/strong><\/p>

B Semester<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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ECTS Credits<\/strong><\/p>

10<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Student Performance Evaluation<\/h6>

Homework and\/or Lab assignments, Final Exam and\/or project<\/p>

Prerequisite Courses<\/h6>

Calculus I, Linear Algebra I, Probabilities<\/p><\/div><\/div><\/div>

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Syllabus<\/span><\/h6>

The course presents predictive models for time series. The first part\u00a0f the course presents Time Series Regression whereas the second part focuses on ARIMA
models. R software (the lingua franca of Statistics) is utilized throughout the course, in a\u00a0wide variety of examples, based on real and simulated data.<\/p>

  • Linear Regression with One Predictor; Inference in Regression and Correlation<\/li>
  • Diagnostics and Remedial Measures; Simultaneous Inferences<\/li>
  • Matrix Approach to Linear Regression; Multiple Regression\u00a0<\/li>
  • Models for Quantitative and Qualitative Predictors; Model Selection and Validation; Diagnostics<\/li>
  • Weighted Least Squares; Serial Correlation<\/li>
  • Exploratory Analysis of Time Series Data; Time Series Regression\u00a0<\/li>
  • Stationarity<\/li>
  • ARMA models<\/li>
  • ARMA model Identification and Maximum Likelihood Estimation<\/li>
  • Forecasting with ARMA models<\/li>
  • ARIMA and seasonal models<\/li>
  • Multivariate Time Series<\/li>
  • Forecasting Experiments; Rolling-Window Estimation and Evaluation<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Learning Outcomes<\/span><\/h6>
    • Upon successful completion of this course students will develop an in-depth understanding of linear regression and ARIMA models.<\/li>
    • Students will have learned alternative ARIMA and time series regression model-building strategies, depending on the number of available predictors.<\/li>
    • Students will gain hands-on experience in classic (e.g. exploratory analyses, diagnostic checks) and modern topics (e.g. cross-validation, quantile regression, penalized estimation) by analyzing real (or simulated) datasets using statistical software.<\/li><\/ul><\/div><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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      <\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

      Type Elective Course Code DAMSL-211 Teaching Semester B Semester ECTS Credits 10 Student Performance Evaluation Homework and\/or Lab assignments, Final Exam and\/or project Prerequisite Courses Calculus I, Linear Algebra I, Probabilities \u00a0 Syllabus The course presents predictive models for time series. The first part\u00a0f the course presents Time Series Regression whereas the second part focuses on ARIMAmodels. R software (the lingua franca of Statistics) is utilized throughout the course, in a\u00a0wide variety of examples, based on real and simulated data. Linear Regression with One Predictor; Inference in Regression and Correlation Diagnostics and Remedial Measures; Simultaneous Inferences Matrix Approach to Linear Regression; Multiple Regression\u00a0 Models for Quantitative and Qualitative Predictors; Model Selection and Validation; Diagnostics Weighted Least Squares; Serial Correlation Exploratory Analysis of Time Series Data; Time Series Regression\u00a0 Stationarity ARMA models ARMA model Identification and Maximum Likelihood Estimation Forecasting with ARMA models ARIMA and seasonal models Multivariate Time Series Forecasting Experiments; Rolling-Window Estimation and Evaluation Learning Outcomes Upon successful completion of this course students will develop an in-depth understanding of linear regression and ARIMA models. Students will have learned alternative ARIMA and time series regression model-building strategies, depending on the number of available predictors. Students will gain hands-on experience in classic (e.g. exploratory analyses, diagnostic checks) and modern topics (e.g. cross-validation, quantile regression, penalized estimation) by analyzing real (or simulated) datasets using statistical software.<\/p>\n","protected":false},"author":194,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-4261","page","type-page","status-publish","hentry","post-no-thumbnail"],"acf":[],"_links":{"self":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4261","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/users\/194"}],"replies":[{"embeddable":true,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/comments?post=4261"}],"version-history":[{"count":4,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4261\/revisions"}],"predecessor-version":[{"id":4265,"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/pages\/4261\/revisions\/4265"}],"wp:attachment":[{"href":"https:\/\/mscs.uoc.gr\/damsl\/wp-json\/wp\/v2\/media?parent=4261"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}