Time Series - A collection of measurements recorded at specific intervals of time. 1. Short term features Noise: Spike/Outlier: Minor variation about.

Slides:

Advertisements

Similar presentations

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.

Advertisements

©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Lesson 12.

Penguin Parade. Quantitative description [A] – The linear equation is y = x – On average, the number of penguins marching is decreasing.

Time Series Analysis. Time-Series Numerical data obtained at regular time intervals The time intervals can be annually, quarterly, monthly, daily, hourly,

Time-Series Analysis and Forecasting – Part III

Trends and Seasonality Using Multiple Regression with Time Series Data Many time series data have a common tendency of growing over time, and therefore.

Ka-fu Wong © 2003 Chap Dr. Ka-fu Wong ECON1003 Analysis of Economic Data.

Seasonal Variations Why do we have seasons ? (continued…)

1S. Sivananthan. What is time series analysis? A time series is when data is collected at REGULAR time intervals to analyse long term trends and patterns.

Forecasting 5 June Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.

Time Series Forecasting Outline: 1.Measuring forecast error 2.The multiplicative time series model 3.Naïve extrapolation 4.The mean forecast model 5.Moving.

Part II – TIME SERIES ANALYSIS C2 Simple Time Series Methods & Moving Averages © Angel A. Juan & Carles Serrat - UPC 2007/2008.

Time Series Report - Basic 1.Introduction 2.Long term trend 3.Seasonal Component 4.Residual Component 5.Predictions 6.Conclusion (Refer to “Stats enquiry.

Time Series Forecasting Outline: 1.Measuring forecast error 2.The multiplicative time series model 3.Naïve extrapolation 4.The mean forecast model 5.Moving.

SMOOTHING TECHNIQUES TIME SERIES. COMPONENTS OF A TIME SERIES Components of a time series Seasonal effect Long term trend Cyclical effect Irregularity,

Topic 4 Marketing Marketing Planning HL ONLY. Learning Objectives Analyse sales-forecasting methods and evaluate their significance for marketing and.

Statistics and Modelling 3.8 Credits: Internally Assessed.

Chapter Three “Customer Accommodation” Part Four “Forecasting” You will need to manually advance from slide-to-slide on this presentation.

Time Series “The Art of Forecasting”. What Is Forecasting? Process of predicting a future event Underlying basis of all business decisions –Production.

Datta Meghe Institute of Management Studies Quantitative Techniques Unit No.:04 Unit Name: Time Series Analysis and Forecasting 1.

Ex 4f SEASONAL ADJUSTMENT.

Production Planning and Control. 1. Naive approach 2. Moving averages 3. Exponential smoothing 4. Trend projection 5. Linear regression Time-Series Models.

CLASS B.Sc.III PAPER APPLIED STATISTICS. Time Series “The Art of Forecasting”

Market Analysis & Forecasting Trends Businesses attempt to predict the future – need to plan ahead Why?

TIME SERIES by H.V.S. DE SILVA DEPARTMENT OF MATHEMATICS

Time Series Analysis Lecture#29 MGT 601. Time Series Analysis Introduction: A time series consists of numerical data collected, observed or recorded at.

Time-Series Forecasting Learning Objectives 1.Describe What Forecasting Is 2. Forecasting Methods 3.Explain Time Series & Components 4.Smooth a Data.

Analyse time series data to make a forecast.  Forecast will be based on:  estimates of the trend for the smoothed data  estimates of seasonal effects.

Statistics and Modelling 3.1 Credits: 3 Internally Assessed.

INTERNAL ACHIEVEMENT STANDARD 3 CREDITS Time Series 1.

Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.

Line Graph- a graph that uses a line to show the relationship between two sets of data. Line graphs show how data changes over time.

CHAPTER 37 Presentation of Data 2. Time Series A TIME SERIES is a set of readings taken at TIME INTERVALS. A TIME SERIES is often used to monitor progress.

Antarctic Sea Ice. Quantitative description [A] The linear trend has an equation of y = x This means that, on average, the area of Antarctic.

Time Series A collection of measurements recorded at specific time intervals.

© 1999 Prentice-Hall, Inc. Chap Chapter Topics Component Factors of the Time-Series Model Smoothing of Data Series  Moving Averages  Exponential.

Time Series Analysis Predicting future sales from past numbers.

Sunglasses Sales Excellence Discussion. Sunglasses Identify and describe at least one further feature of this time series data with reasons. – Sunglasses.

Economics 173 Business Statistics Lecture 23 © Fall 2001, Professor J. Petry

Learning Objectives Describe what forecasting is Explain time series & its components Smooth a data series –Moving average –Exponential smoothing Forecast.

COMPLETE BUSINESS STATISTICS

Marketing “If everything seems to be going well, you have obviously overlooked something”

Time Series and Trend Analysis

Time Series - A collection of measurements recorded at specific intervals of time. 1. Short term features Noise: Spike/Outlier: Minor variation about.

AS91580 Investigate Time-Series Data Internal, 4 credits Investigate Time-Series Data Internal, 4 credits.

Smoothing by moving average

Forecasting Demand. Forecasting Methods Qualitative – Judgmental, Executive Opinion - Internal Opinions - Delphi Method - Surveys Quantitative - Causal,

Economics 173 Business Statistics Lecture 25 © Fall 2001, Professor J. Petry

Time-Series Forecast Models  A time series is a sequence of evenly time-spaced data points, such as daily shipments, weekly sales, or quarterly earnings.

4 - 1 Course Title: Production and Operations Management Course Code: MGT 362 Course Book: Operations Management 10 th Edition. By Jay Heizer & Barry Render.

TIME SERIES ‘Time series’ data is a bivariate data, where the independent variable is time. We use scatterplot to display the relationship between the.

Credit card spending from reserve bank. achieved.

Yandell – Econ 216 Chap 16-1 Chapter 16 Time-Series Forecasting.

Analysis of Time Series

TOPIC 15 Time Series.

Chapter Nineteen McGraw-Hill/Irwin

Techniques for Seasonality

Spring Summer Autumn Winter

Time Series Forecasts Trend - long-term upward or downward movement in data. Seasonality - short-term fairly regular variations in data related to factors.

“The Art of Forecasting”

Telling the story in a graph

Moving Averages OCR Stage 8.

Moving Averages.

Statistics and Modelling 3.8

Statistics Time Series

Time Series Forecasting Accelerator

BUSINESS MATHEMATICS & STATISTICS.

Chapter Nineteen McGraw-Hill/Irwin

Moving Averages.

Presentation transcript:

Time Series - A collection of measurements recorded at specific intervals of time. 1. Short term features Noise: Spike/Outlier: Minor variation about a general trend An obvious difference from the surrounding values e.g.

2. Long term features Trend: Seasonal Variation: Often there is a trend for measurements to remain steady, or show a definite increase or decrease over time. Fairly regular up/down patterns (called cyclical movement if over very long periods) e.g. Long Term Trend: Over long term, sales are increasing overall Seasonal Variation: Sales peak in summer and are lowest in winter. Sales rise again in spring and these are higher than in autumn Ice Cream Sales (in 000’s)

Smoothing Techniques - Used when averaging out random variations to see if there is an overall trend. - Done by averaging all of the data over the period of any natural cycle. The number of values used to form a moving average is called ‘the order of the moving average’ (e.g. we use a 5 point moving mean (order of 5) if the natural cycle is a working week)

e.g. Use 4 point moving means to smooth the data for Elliot’s Fish and Chip shop. Then graph the raw data and the mean of means. SeasonQuarterly sales Moving mean Mean of means Seasonal Difference Sept.9040 Dec.8650 Mar June9250 Sept.9033 Dec.8578 Mar June9407 Sept.9209 Dec.8740 Mar June9504 Sept.9246 Dec.8929 Mar Mean of means are used so there is a 1 to 1 correspondence between the raw and smoothed data.

Seasonal Effects Seasonal Difference =data value – moving mean Seasonal Effect =averaging off all of the seasonal differences Making Predictions - Extend the trend line to find the smoothed data value then add/subtract the average seasonal difference e.g. Using the data and graph of ‘Elliot’s Fish and Chip Shop’ a) Find the seasonal effects for June and December b)Use the long term trend line to predict the turnover in December 1999 and June 2000.

e.g. Use 4 point moving means to smooth the data for Elliot’s Fish and Chip shop. Then graph the raw data and the mean of means. SeasonQuarterly sales Moving mean Mean of means Seasonal Difference Sept.9040 Dec.8650 Mar June9250 Sept.9033 Dec.8578 Mar June9407 Sept.9209 Dec.8740 Mar June9504 Sept.9246 Dec.8929 Mar

e.g. Using the data and graph of ‘Elliot’s Fish and Chip Shop’ a) Find the seasonal effects for June and December b)Use the long term trend line to predict the turnover in December 1999 and June Seasonal Effect for June = = 1350 = $450 3 Seasonal Effect for December = = -547 = -$

Dec ‘99 = 9200 June ‘00 = 9250

e.g. Using the data and graph of ‘Elliot’s Fish and Chip Shop’ a) Find the seasonal effects for June and December b)Use the long term trend line to predict the turnover in December 1999 and June Seasonal Effect for June = = 1350 = $450 3 Seasonal Effect for December = = -547 = -$ Prediction for December 1999 = = $ Prediction for June 2000 = =$9700