Time series.

Slides:



Advertisements
Similar presentations
Time Series Analysis ( AS 3.8)
Advertisements

A.S. 3.8 INTERNAL 4 CREDITS Time Series. Time Series Overview Investigate Time Series Data A.S. 3.8 AS91580 Achieve Students need to tell the story of.
DSCI 5340: Predictive Modeling and Business Forecasting Spring 2013 – Dr. Nick Evangelopoulos Exam 1 review: Quizzes 1-6.
Forecasting Models With Linear Trend. Linear Trend Model If a modeled is hypothesized that has only linear trend and random effects, it will be of the.
Penguin Parade. Quantitative description [A] – The linear equation is y = x – On average, the number of penguins marching is decreasing.
Ka-fu Wong © 2003 Chap Dr. Ka-fu Wong ECON1003 Analysis of Economic Data.
Forecasting 5 June Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.
Data Sources The most sophisticated forecasting model will fail if it is applied to unreliable data Data should be reliable and accurate Data should be.
Part II – TIME SERIES ANALYSIS C2 Simple Time Series Methods & Moving Averages © Angel A. Juan & Carles Serrat - UPC 2007/2008.
Chapter 19 Time-Series Analysis and Forecasting
CHAPTER 18 Models for Time Series and Forecasting
Box Jenkins or Arima Forecasting. H:\My Documents\classes\eco346\Lectures\chap ter 7\Autoregressive Models.docH:\My Documents\classes\eco346\Lectures\chap.
Winter’s Exponential smoothing
Time-Series Analysis and Forecasting – Part V To read at home.
Describing and Exploring Data Initial Data Analysis.
Chapter 5 Demand Forecasting.
Chapter 16: Time-Series Analysis
Holt’s exponential smoothing
Chapter 17 Time Series Analysis and Forecasting ©.
Forecasting Models Decomposition and Exponential Smoothing.
Copyright © 2014, 2011 Pearson Education, Inc. 1 Chapter 27 Time Series.
© 2000 Prentice-Hall, Inc. Chap The Least Squares Linear Trend Model Year Coded X Sales
Time series Model assessment. Tourist arrivals to NZ Period is quarterly.
Forecasting Demand for Services. 2 Learning Objectives Recommend the appropriate forecasting model for a given situation. Recommend the appropriate forecasting.
Chapter 10 Re-expressing Data: Get It Straight!. Slide Straight to the Point We cannot use a linear model unless the relationship between the two.
Lecture 6 Re-expressing Data: It’s Easier Than You Think.
Antarctic Sea Ice. Quantitative description [A] The linear trend has an equation of y = x This means that, on average, the area of Antarctic.
Copyright © 2011 Pearson Education, Inc. Time Series Chapter 27.
Ch 5-1 © 2004 Pearson Education, Inc. Pearson Prentice Hall, Pearson Education, Upper Saddle River, NJ Ostwald and McLaren / Cost Analysis and Estimating.
Jon Curwin and Roger Slater, QUANTITATIVE METHODS: A SHORT COURSE ISBN © Thomson Learning 2004 Jon Curwin and Roger Slater, QUANTITATIVE.
Sunglasses Sales Excellence Discussion. Sunglasses Identify and describe at least one further feature of this time series data with reasons. – Sunglasses.
Forecasting Demand. Forecasting Methods Qualitative – Judgmental, Executive Opinion - Internal Opinions - Delphi Method - Surveys Quantitative - Causal,
Forecasting is the art and science of predicting future events.
Forecasting Demand. Problems with Forecasts Forecasts are Usually Wrong. Every Forecast Should Include an Estimate of Error. Forecasts are More Accurate.
Forecasts and Projections “A trend is a trend is a trend, But the question is, will it bend? Will it alter its course Through some unforeseen force And.
Statistics 10 Re-Expressing Data Get it Straight.
Yandell – Econ 216 Chap 16-1 Chapter 16 Time-Series Forecasting.
Analysis of Time Series
Financial Analysis, Planning and Forecasting Theory and Application
Analysis of Time Series
Demand Estimation and Forecasting
Chapter Nineteen McGraw-Hill/Irwin
Forecasting Methods Dr. T. T. Kachwala.
Let’s Get It Straight! Re-expressing Data Curvilinear Regression
Time Series for Teaching, Learning and Assessment
Demand Forecasting Production and Operations Management
Carsten Boldsen Hansen Economic Statistics Section, UNECE
Re-expressing the Data: Get It Straight!
“The Art of Forecasting”
Five steps in a forecasting task
Re-expressing Data: Get it Straight!
Re-expressing the Data: Get It Straight!
Re-expressing the Data: Get It Straight!
So how do we know what type of re-expression to use?
Regression.
Forecasting Elements of good forecast Accurate Timely Reliable
Are we moving towards more integration of the NEW FEATURES OF TS INto JDEMETRA+ ?
Forecasting Qualitative Analysis Quantitative Analysis.
Exponential Smoothing
Lecture 6 Re-expressing Data: It’s Easier Than You Think
Regression Forecasting and Model Building
Problematic time series and how to treat them
BUSINESS MATHEMATICS & STATISTICS.
Algebra Review The equation of a straight line y = mx + b
BEC 30325: MANAGERIAL ECONOMICS
Forecasting - Introduction
OUTLINE Questions? Quiz Go over homework Next homework Forecasting.
Re-expressing Data: Get it Straight!
Are we moving towards more integration of the NEW FEATURES OF TS INto JDEMETRA+ ?
Exponential Smoothing
Presentation transcript:

Time series

Comments of a trivial or wildly speculative nature are not acceptable. More than generic, learned responses are required.

Relevance of forecast Relate the results to the context, eg reference made to whom it is useful for - economic planners or individual business. Could include reference to changes in conditions.

Relevance of forecast There could be a problem in extrapolating too far especially where non linear models have been applied.

Relevance of forecast Comment on conditions remaining constant over the period of the extrapolation related to the context can be of significance.

Features of the data Discussion of curvature, seasonal effects, linearity, outliers, ramps etc where appropriate.

Features of the data Adjacent periods may be compared once the data has been seasonally adjusted. Longitudinal trends over consecutive periods of time is what is important.

Appropriateness of the model. The decision should be primarily visual. Basing a decision on the appropriateness of the model on the R2 value alone is not appropriate.

Appropriateness of the model. Consideration of the model could include reference to additive, multiplicative, steps, ramps and giving reasons why – this needs to be a comment related to the smoothed data.

Appropriateness of the model. Over plot of data discussed.

Improvements to the model (not data collection method.) A polynomial, log or exponential model for a trend is rarely appropriate when related to a long run or unlimited time (over a limited period).

Improvements to the model (not data collection method.) Weighting towards the end of period in which the data is collected could improve the model.

Seasonally adjusted data Discussion involving interpretation of seasonally adjusted values should be related to seasonal adjustment enabling trend over consecutive time periods to be seen

Seasonally adjusted data You must consider that you do not need to comment on all aspects. Don’t try and end up giving contradictions

Comment You must consider that you do not need to comment on all aspects. Don’t try and end up giving contradictions.