SOMs for time series

Jul 24, 2012

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SOMs for time series

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1. 25.07.2012 Louvain la Neuve 1 SOMs for time series

2. 25.07.2012 Louvain la Neuve 2 Time series ...

3. 25.07.2012 Louvain la Neuve 3

$Time Series$

Time Series

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

1.14k views • 56 slides

Time Series 2 Time Series 1

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

375 views • 6 slides

Time Series Methods for Epidemiology

Overview. Background on public healthIssues in epidemiologyParticular issues for time seriesExample analysis. Public Health vs. Clinical Medicine. PH focuses on populations or communities rather than individualsPH tries to understand and promote behaviors and conditions that make for healthy communitiesFocus on primary prevention, rather than treatment of ailments.

951 views • 36 slides

Time series

Time series. Characteristics. Non-independent observations (correlations structure) Systematic variation within a year (seasonal effects) Long-term increasing or decreasing level (trend) Irregular variation of small magnitude (noise). Where can time series be found?.

845 views • 51 slides

Time Series

Time Series. Learning Objectives. Cross-sectional vs. Longitudinal Describe what is forecasting Explain time series & its components Smooth a data series Moving average Exponential smoothing Forecast using trend models Simple Linear Regression

380 views • 20 slides

Time Series

Time Series . Instructor Dr. L. D. Behera Department of Electrical Engineering Indian institute of Technology Kanpur. Presented by Vikas Kumar vidyarthi Ph.D Scholar (10203069),CE. Contents:-. Correlation and Regression What is Time Series? Field of its Applications Methods:

560 views • 30 slides

Time series

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:

607 views • 21 slides

Time Series

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

501 views • 17 slides

MODELS FOR STATIONARY TIME SERIES

MODELS FOR STATIONARY TIME SERIES. Eni Sumarminingsih , SSi , MM. General Linear Processes. Moving Average Processes.

532 views • 26 slides

Time Series

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. .

627 views • 28 slides

Time Series

Time Series. From regression to time series. Review of Regression Analysis. Regression: Y = f(X), where X is some independent variable, and f( ) is linear. The function that relates the actual data points is… . Y = β 0 + β 1 X + ε. Y.

273 views • 12 slides

MODELS FOR NONSTATIONARY TIME SERIES

MODELS FOR NONSTATIONARY TIME SERIES. By Eni Sumarminingsih , SSi , MM. Stationarity Through Differencing. Consider again the AR(1) model Consider in particular the equation. Iterating into the past as we have done before yields.

371 views • 21 slides

Time series

Time series. Previous class. Time series. - This class.

258 views • 5 slides

Time Series

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.

386 views • 17 slides

Time Series

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.

720 views • 32 slides

Time Series

Time Series. A time series is a sequence of measurements of a variable that is a function of time. Components of a time series. Trend Seasonal Cyclical Random or irregular. trend. the long term movement in a time series. seasonal variation.

1.38k views • 48 slides

Time series

Time series. Model assessment. Tourist arrivals to NZ. Period is quarterly. Draw graph of raw data. Write down the components of the time series in context. Identify and discuss any trend(s) in your series. The number of tourists arriving in NZ increases with time.

939 views • 71 slides

Time Series

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

1.06k views • 17 slides

TIME SERIES

TIME SERIES. ‘Time series’ data is a bivariate data, where the independent variable is time. We use scatterplot to display the relationship between the dependent variable and time of ‘time series’. The time (years, weeks, months) is always converted to equivalent digits starting from 1.

1.01k views • 21 slides

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