Moving Averages.

Slides:

Advertisements

Similar presentations

Data collected over a period of time Example Data seems pretty random!!

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.

Moving Averages OCR Stage 8. Why use them? Moving Averages, when graphed, allow us to see any trends in data that are cyclical By calculating the average.

Time-Series Analysis and Forecasting – Part III

Chapter 12.2 Business Cycles Four Phases of the Business Cycle Expansion /Recovery Peak - Contraction /Recession Trough - What is the long term trend in.

OCT 2012 UPDATE Cement Sales for the Second Quarter of 2012 Dr Johan Snyman STELLENBOSCH 3 October 2012.

A variation on mean (or average) is called a Moving Average.  This is used when more data is added at a later date and another average needs to be calculated.

1 Time Series Analysis Thanks to Kay Smith for making these slides available to me!

T T18-06 Seasonal Relatives Purpose Allows the analyst to create and analyze the "Seasonal Relatives" for a time series. A graphical display of.

Moving Averages Example The table below represents test results for two students from last year’s 11M2. SurnameForenameTutorP1P2P3P4P5P6P7P8P9P10 ALIMubarikOB

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

Ex 4f SEASONAL ADJUSTMENT.

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

Chapter 5 Demand Forecasting. Qualitative Forecasts Survey Techniques Planned Plant and Equipment Spending Expected Sales and Inventory Changes Consumers’

ADJUSTED EXPONENTIAL SMOOTHING FORECASTING METHOD Prepared by Dan Milewski November 29, 2005.

Eco 6351 Economics for Managers Chapter 10a. The Business Cycle Prof. Vera Adamchik.

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

Definition of Time Series: An ordered sequence of values of a variable at equally spaced time intervals. The variable shall be time dependent.

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.

Chapter 6 Business and Economic Forecasting Root-mean-squared Forecast Error zUsed to determine how reliable a forecasting technique is. zE = (Y i -

6.5 Applying Averages Obj: To calculate average and solve problems with average Find Average = Sum # of items.

1 Given the following data, calculate forecasts for months April through June using a three- month moving average and an exponential smoothing forecast.

Simple Exponential Smoothing The forecast value is a weighted average of all the available previous values The weights decline geometrically Gives more.

Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.

Quantitative Decision Making 7 th ed By Lapin and Whisler.

Chapter 5 The Decomposition Method Components of a Time Series Trend - Tr t Seasonal - Sn t Cyclical - Cl t Irregular -  t.

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.

MEDIAN SMOOTHING.  Smoothing involves replacing the original time series with another one where most of the variation has been removed, in order to see.

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

5.3 – Solving Multi-Step Inequalities. *Just like solving equations*

Time Series Analysis Predicting future sales from past numbers.

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

Economics 173 Business Statistics Lecture 26 © Fall 2001, Professor J. Petry

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

Time Series and Trend Analysis

+ How Accountants use Income Statements p. 266 exercises: 1, 2,5 We will explore different ways to extract meaningful information from our income statement.

 Average – mean, median and mode are three “kinds” of average (commonly used to describe the mean).  Mean temperature – average temperature of the air.

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

Analysis of Time Series

Financial Analysis, Planning and Forecasting Theory and Application

Business Cycles and Unemployment

Moving average method A quantitative method of forecasting or smoothing a time series by averaging each successive group (no. of observations) of data.

TOPIC 15 Time Series.

Look at the following graph?

BUDGETING Saturday, July 22, 2017

CALCULATING THE ACCOUNTS RECEIVABLE TURNOVER RATIO

Exponential Functions

Moving Averages OCR Stage 8.

Chapter 11 Reading Graphics.

MISLEADING DIAGRAMS.

Chapter 13 Improved forecasting methods

A graphing calculator is required for some problems or parts of problems 2000.

GCSE Statistics Time Series.

Sales Tax Calculator Choose 1 item that you would like to buy. (at least $500) Research the.

Time series graphs……. SMOOTHING.

The periodic and cyclical ups and downs of the economy

Unit 3 Review (Calculator)

Time Series Forecasting Accelerator

Sunspot Number (SSN) Basic Information and trends

Time Series Analysis (Demand Forecasting)

BUSINESS MATHEMATICS & STATISTICS.

Statistics Cyclical Time Series

Calculating: Discount Prices

Today we are going back in time to help us predict the future!!!

Calculate 9 x 81 = x 3 3 x 3 x 3 x 3 3 x 3 x 3 x 3 x 3 x 3 x =

ECONOMICS: March 27 Warm-up What is the relationship between unemployment and poverty? Learning Target #1 In order to understand the causes and consequences.

Business Analysis.

Moving Averages.

Understanding Main Effects and Interactions

Presentation transcript:

Moving Averages

Why use them? Moving Averages, when graphed, allow us to see any trends in data that are cyclical By calculating the average of 2 or more items in the data, any peaks and troughs are smoothed out.

Year 1996 1997 1998 Quarter 1 2 3 4 Sales 189 244 365 262 190 266 359 250 201 259 401 265 265 269.25 265.25 270.75 4 Period Moving Average

Year 1996 1997 1998 Quarter 1 2 3 4 Sales 189 244 365 262 190 266 359 250 201 259 401 265 4 period Moving Average Quarters 1-4 2-5 3-6 4-7 5-8 6-9 7-10 8-11 9-12 Moving Average 265 265.25 270.75 269.25 266.25 269 267.25 277.75 281.5

Year 1996 1997 1998 Quarter 1 2 3 4 Sales 189 244 365 262 190 266 359 250 201 259 401 265 Quarters 1-4 2-5 3-6 4-7 5-8 6-9 7-10 8-11 9-12 Moving Average 265 265.25 270.75 269.25 266.25 269 267.25 277.75 281.5 500 400 x x x 300 x x x x x x x x x x x x x x x 200 x x x 100 1 2 3 4 1 2 3 4 1 2 3 4 1996 1997 1998