Lecture series: Data analysis. Thomas Kreuz , ISC, CNR thomas.kreuz@cnr.it http://www.fi.isc.cnr.it/users/thomas.kreuz /. Lectures: Each Tuesday at 16:00 (First lecture: May 21, last lecture: June 25 ). (Very preliminary) Schedule.

ByLecture series: Data analysis. Thomas Kreuz , ISC, CNR thomas.kreuz@cnr.it http://www.fi.isc.cnr.it/users/thomas.kreuz /. Lectures: Each Tuesday at 16:00 (First lecture: May 21, last lecture: June 25 ). Other lecture series. Stefano Luccioli : Neuronal models (February/March 2013)

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Math 419/592 Winter 2009 Prof. Andrew Ross Eastern Michigan University. Time Series. Overview of Stochastic Models. Take Math 560 (Optimization) this fall! Sign up soon or it will disappear. But first, a word from our sponsor. Outline. Look at the data! Common Models

Time Series . Smoothing techniques. Components of a time series. Components of a time series Seasonal effect Long term trend Cyclical effect Irregularity , random variation or random error. Seasonal effect. Seasonal effect. Seasonal variation

AS 90641 (internal) 3 credits. Time Series. Time Series. A time series is a set of values of a variable measured at regular intervals of time E.g. temperature measured daily, monthly sales figures Analysis looks for patterns of change over time Try to predict future movements.

Time series. When today has impact on what happens tomorrow. Time series analysis. Statistical time series are data in time, where what happens at one point in time is dependent on what happens at other points in time. T he past and the present give information about the future. Examples:

Time Series. Internal Achievement Standard 3 Credits. I Can Do…. Unit Overview. Introduction Plotting Time Series Analysing Time Series Cycles and Trends Forecasting Smoothing Adjusting for Seasonal Effects Index Numbers Linear and Non-linear Trends. Week One: Just in Time.

Time Series. Techniques and an Example from Roger Simon’s The City Building Process. What is a Time Series?. Numerical data ordered by intervals of time in chronological order The analysis of a time series involves decomposing the series into its components, e.g., Trends

Time Series. Internal AS credits. Applications : . The usage of time series models is twofold: Obtain an understanding of the underlying forces and structure that produced the observed data Forecasting , monitoring or even feedback and feed-forward control. .

Time Series 2 Time Series 1. TS2=5*cos(2*t) TS1=cos(2*t). R 2 = 1 Perfectly correlated. Time Series 2 Time Series 1. *. R 2 = 0 No LINEAR correlation. TS2=5*cos(2*t) TS1=cos(t). Time Series 1 Time Series 2. TS1=sin(t) TS2=cos(t). R 2 = 0 No correlation. Time Series 2

Time Series. César Emmanuel Richard Bruno. XML et Data Mining – 2005-2006 Université de Versailles Saint-Quentin en Yvelines. Sommaire. Présentation des Séries Temporelles Définitions & Explications But de l’Analyse Modèles Mathématiques Les Algorithmes Présentation générale