Time Series Data, Correlation, Time Series Analysis, Forecasting Strategies, Demanding forecasting, Basic Stochastic Models, Characteristics of Time Series, Definition of Prediction, Prediction Fields and Applications, Categories of Predictive Paths, Predictive Performance Measures, Basic statistical concepts, Statistical Methods in the Frequency Domain, Basic Statistical Analysis and prediction models, Statistical measures of accuracy in Predictions, Graphical Data Representation, Parameter Estimation, Growth Rate, Normalization Terms, classical Decomposition methods, Stationary Models, Non-stationary Models, Introduction to Spectral Analysis and Filtering, State Space Models, Multivariate Models, Confidence Space, Business Forecast Process, Mobile Intermediate Terms for Exposure, Methods of Exposure Smoothing , Seasonal Smoothing), Selection of smoothing model, Introduction to ARIMA Timeline Forecasting Models (Prediction Limits). Time Series Regression and Exploratory Data Analysis (simple linear and multiple regression), Binary Categorization (such as Support Vector Machines and Multiple Layer Perceptron) and Machine Learning applications as well as Clustering techniques (such as Neural Networks, k-Nearest Neighbours, Expectation Maximization). Detection of embarassing and malicious behavioral patterns (description of SAX technique) in online dialogues (questions of predator to a candidate minor victim).

The aim of the course is to enable students to understand the basic principles of time series analysis, strategies prediction, basic Statistical Analysis and Performance measures in forecasting, Time Series Regression and Exploratory Data Analysis (simple linear and multiple regression), Binary Categorization (such as Support Vector Machines and Multiple Layer Perceptron) and Machine Learning applications as well as Clustering techniques (such as Neural Networks, k-Nearest Neighbours, Expectation Maximization). By concluding the course, students are able to:

• analyze and adapt data in original form
• estimate the parameters and compute the mobile average of data based on basic Statistic methodology
• distinguish the quality of characteristics in time series data
• apply forecasting methods analyzing and designing data required for prediction
• develop deep knowledge in Time Series Regression and Exploratory Data Analysis
• understand the content / role of forecasting based on basic prediction models
• identify, describe and distinguish the main methods and prediction techniques in Binary Classification as well as clustering.
• have comprehensive knowledge in methodology and application of forecasting techniques

Not required.

1. Elmasri R. and Navathe S.B.: "Θεμελιώδεις Αρχές Συστημάτων Βάσεων Δεδομένων", Τόμος Α', 5η Έκδοση, 2007. Μετάφραση από τις Εκδόσεις Δίαυλος, 2008.
2. Ramakrishnan R. and Gehrke J.: "Συστήματα Διαχείρισης Βάσεων Δεδομένων" Τόμος Α', 2η έκδοση, McGraw Hill, 2000. Μετάφραση από τις Εκδόσεις Τζιόλα, 2002.

1. Toby J. Teorey: "Database Modeling & Design”, ISBN 1558605002, Morgan Kaufmann
2. Terry Halpin: “Information Modeling and Relational Databases: From Conceptual Analysis to Logical Design”, ISBN 1558606726

Lectures, resolving exercises, Laboratory Exercises.

Personal assignments and pair or group assignments, lab practice, regular short assessments in the form of a quiz test, final examination.

Face-to-face.