# Exponential Smoothing with Trend. As we move toward medium-range forecasts, trend becomes more important.

Save this PDF as:

Size: px
Start display at page:

Download "Exponential Smoothing with Trend. As we move toward medium-range forecasts, trend becomes more important."

## Transcription

1 Exponential Smoothing with Trend As we move toward medium-range forecasts, trend becomes more important. Incorporating a trend component into exponentially smoothed forecasts is called double exponential smoothing. The estimate for the average and the estimate for the trend are both smoothed.

2 Exponential Smoothing with Trend Adjustment Forecast including trend (FIT t )OR Adjusted Forecast (AF t ) = exponentially smoothed forecast (F t ) + exponentially smoothed trend (T t ) That is, AF t = F t + T t We need to compute both Ft and Tt

3 or Exponential Smoothing with Trend Adjustment (contd.) F t = Last period s forecast + D (Last period s actual Last period s forecast) F t = F t-1 + D (A t-1 F t-1 ) T t = E(This period s Forecast - last period s Forecast) + (1-E) (Trend estimate last period) or T t = E(F t - F t-1 ) + (1- E) T t-1 for all t F t = exponentially smoothed forecast of the data series in period t T t = exponentially smoothed trend in period t A t = actual demand in period t α = smoothing constant for the average β = smoothing constant for the trend

4 Adjusted Exponential Smoothing Example PERIOD MONTH DEMAND 1 Jan 37 2 Feb 40 3 Mar 41 4 Apr 37 5 May 45 6 Jun 50 7 Jul 43 8 Aug 47 9 Sep Oct Nov Dec 54

5 Adjusted Exponential Smoothing Example Per Month Dem F t+1 T t+1 AF t+1 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan F 3 =F (A 2 -F 2 ) = *3 = 38.5 T 3 = E(F 3 - F 2 ) + (1 - E) T 2 = (0.30)( ) + (0.70)(0) = 0.45 AF 3 = F 3 + T 3 = = T 13 = E(F 13 - F 12 ) + (1 - E) T 12 =(0.30)( ) + (0.70)(1.77) =1.36 AF 13 = F 13 + T 13 = = Forecast (D = 0.50)

6 Adjusted Exponential Smoothing Forecasts Actual Adjusted forecast (D = 0.50; E = 0.30)) Demand Forecast (D = 0.50) Period

7 Seasonal Adjustments Repetitive increase/decrease in demand Use seasonal factor to adjust forecast Seasonal factor = S = D i i D Where D i is the sum of demands of the period i in the time series data 6Dis net sum of demands of the entire period in the time series data

8 Example: Seasonal Adjustment [1] Demand (1000 s per quarter) Year Total Total S i Computed trend line for data y = X [Given to you] 2006 (year 4) forecast = (4) = Forecasted demand after seasonal adjustment for the year 2006 is Details x 0.28 = 16.28; x 0.20 = 11.63; x 0.15 = 8.73; x 0.37 = S D = = = D SF 1 = (S 1 ) (F 5 ) = (0.28)(58.17) = SF 2 = (S 2 ) (F 5 ) = (0.20)(58.17) = SF 3 = (S 3 ) (F 5 ) = (0.15)(58.17) = 8.73 SF 4 = (S 4 ) (F 5 ) = (0.37)(58.17) = 21.53

9 Example: Seasonal Adjustment [2] Quarter Year 1 Year 2 Year 3 Year Total Average

10 Example: Seasonal Adjustment [2] Quarter Year 1 Year 2 Year 3 Year Total Average Seasonal Index = Actual Demand Average Demand

11 Example: Seasonal Adjustment [2] Quarter Year 1 Year 2 Year 3 Year Total Average Seasonal Index = =

12 Example: Seasonal Adjustment [2] Contd. Quarter Year 1 Year 2 Year 3 Year /250 = Total Average Seasonal Index = =

13 Example: Seasonal Adjustment [2] Contd. Quarter Year 1 Year 2 Year 3 Year /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = 0.39

14 Example: Seasonal Adjustment [2] Contd. Quarter Year 1 Year 2 Year 3 Year /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = 0.39 Quarter Average Seasonal Index 1 ( )/4 =

15 Example: Seasonal Adjustment [2] Contd. Quarter Year 1 Year 2 Year 3 Year /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = 0.39 Quarter Average Seasonal Index 1 ( )/4 = ( )/4 = ( )/4 = ( )/4 = 0.50

16 Example: Seasonal Adjustment [2] Contd. Quarter Year 1 Year 2 Year 3 Year /250 = /300 = /450 = /550 = 0.18 Projected Annual Demand = 2600 [Given] 2 335/250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = 2.11 Average Quarterly Demand = 2600/4 = /250 = /300 = /450 = /550 = 0.39 Quarter Average Seasonal Index Forecast 1 ( )/4 = ( )/4 = ( )/4 = ( )/4 = 0.50

17 Example: Seasonal Adjustment [2] Contd. Quarter Year 1 Year 2 Year 3 Year /250 = /300 = /450 = /550 = 0.18 Projected Annual Demand = /250 = /300 = /450 = /550 = /250 Average = 2.08 Quarterly 590/300 = 1.97 Demand 830/450 = = 2600/ /550 = 650= /250 = /300 = /450 = /550 = 0.39 Quarter Average Seasonal Index Forecast 1 ( )/4 = (0.20) = ( )/4 = ( )/4 = ( )/4 = 0.50

18 Example: Seasonal Adjustment [2] Contd. Quarter Year 1 Year 2 Year 3 Year /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = /250 = /300 = /450 = /550 = 0.39 Quarter Average Seasonal Index Forecast 1 ( )/4 = (0.20) = ( )/4 = (1.30) = ( )/4 = (2.00) = ( )/4 = (0.50) = 325

19 Seasonalised Time Series Regression Analysis 1. Select a representative historical data set. 2. Develop a seasonal index for each season. 3. Use the seasonal indexes to De-Seasonalise the data. 4. Perform linear regression analysis on the De-Seasonalised data. 5. Use the regression equation to compute the forecasts. 6. Use the seasonal indexes to reapply the seasonal patterns to the forecasts.

20 Example: Computer Products Corp. Seasonalized Times Series Regression Analysis An analyst at CPC wants to develop next year s quarterly forecasts of sales revenue for CPC s line of Epsilon Computers. She believes that the most recent 8 quarters of sales (shown on the next slide) are representative of next year s sales.

21 Example: Computer Products Corp. Seasonalised Times Series Regression Analysis Representative Historical Data Set Year Qtr. (\$mil.) Year Qtr. (\$mil.)

22 Example: Computer Products Corp. Compute the Seasonal Indexes Quarterly Sales Year Q1 Q2 Q3 Q4 Total Totals Qtr. Avg Seas.Ind / 9.25

23 Example: Computer Products Corp. Time series data: Quarterly Sales Year Q1 Q2 Q3 Q Seasonal Index De-Seasonalised data for Q1 = { Actual Q1 sales / Seas. Index } De-Seasonalise the Data Quarterly Sales Year Q1 Q2 Q3 Q Y t = T t x S t x C t x R t Assum. There is no C t & R t Y t = T t x S t Y t (dese.)= (Y t /S t )

24 Example: Computer Products Corp. Perform Regression on De-seasonalized Data Yr. Qtr. x y x 2 xy Totals (74.01) 36( ) a = = (204) (36) 8(341.39) 36(74.01) b = = (204) (36) Y = X

25 Example: Computer Products Corp. Compute the De-Seasonalised Forecasts MODEL : Y = X Y 9 = (9) = Y 10 = (10) = Y 11 = (11) = Y 12 = (12) = Note: Average sales are expected to increase by.199 million (about \$200,000) per quarter.

26 Example: Computer Products Corp. Seasonalised the Forecasts Seas. De-seas. Seas. Yr. Qtr. Index Forecast Forecast

27 Time Series Models & Classical Decomposition Decomposition time series models: Multiplicative: Additive: Y = T x C x S x e Y = T + C + S + e T = Trend component C = Cyclical component S = Seasonal component e = Error or random component

28 Time Series Models & Classical Decomposition Classical decomposition is used to isolate trend, seasonal, and other variability components from a time series model

29 Classical Decomposition Explained Basic Steps: 1. Determine seasonal indexes using the ratio to moving average method 2. Deseasonalize the data 3. Develop the trend-cyclical regression equation using deseasonalized data 4. Multiply the forecasted trend values by their seasonal indexes to create a more accurate forecast

30 Start with multiplicative model Y = TCSe Then Se = (Y/TC)

31 Classical Decomposition: Illustration Gem Company s operations department has been asked to deseasonalize and forecast sales for the next four quarters of the coming year The Company has compiled its past sales data in Table 1 An illustration using classical decomposition will follow Table 1: Gem Company s Sales Data Original Forecasted Year Quarter Period Sales Sales t Y TS ? ? ? ?

32 Classical Decomposition Illustration: Step 1 (a) Compute the fourquarter simple moving average Ex: simple MA at end of Qtr 2 and beginning of Qtr 3 ( )/4 = Moving Year Quarter Period Sales Average t Y

33 Classical Decomposition Illustration: Step 1 (b) Compute the twoquarter centered moving average Ex: centered MA at middle of Qtr 3 ( )/2 = Table 2: Four-Quarter Moving Average Simple Centered Moving Moving Year Quarter Period Sales Average Average t Y TC

34 Classical Decomposition Illustration: Step 1 (c) Compute the seasonal-error component (percent MA) Ex: percent MA at Qtr 3 (65/60.500) = Table 2: Four-Quarter Moving Average Simple Centered Percent Moving Moving Moving Year Quarter Period Sales Average Average Average t Y TC Se=Y/(TC)

35 Classical Decomposition Illustration: Step 1 (d) Compute the unadjusted seasonal index using the seasonalerror components from Table 2 Ex (Qtr 1): [(Yr 2, Qtr 1) + (Yr 3, Qtr 1) + (Yr 4, Qtr 1)]/3 = [ ]/3 = Table 3: Seasonal Index Computation Unadjusted Adjusted Seasonal Adjusting Seasonal Quarter Average Index Factor Index 1 ( )/3 = x (4.000/4.004) = ( )/3 = x (4.000/4.004) = ( )/3 = x (4.000/4.004) = ( )/3 = x (4.000/4.004) =

36 Classical Decomposition Illustration: Step 1 (e) Compute the adjusting factor by dividing the number of quarters (4) by the sum of all calculated unadjusted seasonal indexes = 4.000/( ) = (4.000/4.004) Table 3: Seasonal Index Computation Unadjusted Adjusted Seasonal Adjusting Seasonal Quarter Average Index Factor Index 1 ( )/3 = x (4.000/4.004) = ( )/3 = x (4.000/4.004) = ( )/3 = x (4.000/4.004) = ( )/3 = x (4.000/4.004) =

37 Classical Decomposition Illustration: Step 1 (f) Compute the adjusted seasonal index by multiplying the unadjusted seasonal index by the adjusting factor Ex (Qtr 1): x (4.000/4.004) = Table 3: Seasonal Index Computation Unadjusted Adjusted Seasonal Adjusting Seasonal Quarter Average Index Factor Index 1 ( )/3 = x (4.000/4.004) = ( )/3 = x (4.000/4.004) = ( )/3 = x (4.000/4.004) = ( )/3 = x (4.000/4.004) =

38 Classical Decomposition Illustration: Step 2 Compute the deseasonalized sales by dividing original sales by the adjusted seasonal index Ex (Yr 1, Qtr 1): (55 / 0.942) = Table 4: Deseasonalizing Sales Adjusted Original Seasonal Deseasonalized Year Quarter Period Sales Index Sales t Y S TCe

39 Classical Decomposition Illustration: Step 3 Compute the trend-cyclical regression equation using simple linear regression T t = a + bt t-bar = 8.5 T-bar = 69.6 b = a = T t = t Table 5: Regression Equation Values Deseasonalized Year Quarter Period Sales t TCe = (Y/S) t(y/s) t

40 Classical Decomposition Illustration: Step 4 (a) Develop trend sales T t = t Ex (Yr 1, Qtr 1): T 1 = (1) = Table 6: Trend Sales Original Deseasonalized Trend Year Quarter Period Sales Sales Sales t Y TCe = (Y/S) T

41 Classical Decomposition Illustration: Step 4 (b) Forecast sales for each of the four quarters of the coming year Ex (Yr 5, Qtr 1): x = Table 7: Forecasted Sales Seasonal Trend Forecasted Year Quarter Period Index Sales Sales t S T TS

42 Classical Decomposition Illustration: Graphical Look 100 Graph 1: Comparison of Trend, Original, and Deseasonalized Sales 90 Sales (\$) (Y/S) = TCe Deseasonalized T Trend Y Original Quarter

43 The Classical Decomposition- Procedure Smooth the time series to remove random effects and seasonality. Calculate moving averages. Determine period factors to isolate the (seasonal) (error) factor. Determine the unadjusted seasonal factors to eliminate the random component from the period factors Calculate the ratio y t /MA t. Average all the y t /MA t that correspond to the same season.

44 The Classical Decomposition- Procedure Contd. Determine the adjusted seasonal factors. Determine Deseasonalized data values. Calculate: [Unadjusted seasonal factor] [Average seasonal factor] Calculate: y t [Adjusted seasonal factors] t Determine a deseasonalized trend forecast. Use linear regression on the deseasonalized time series. Determine an adjusted seasonal forecast. Calculate: (y t /Ma t ) [Adjusted seasonal forecast].

45 Monitoring and Controlling Operations Forecasts Reasons for out-of-control forecasts change in trend appearance of cycle politics weather changes promotions

46 Monitoring and Controlling a Forecasting Model Forecasts can be monitored using either Tracking Signal (TS) or Control Charts Why track the forecast? To plan around the error in the future To measure actual demand versus forecasts To improve our forecasting methods

47 Monitoring and Controlling a Forecasting Model Tracking Signal (TS) The TS measures the cumulative forecast error over n periods in terms of MAD TS = n i= 1 (Actual demand - Forecast demand ) If the forecasting model is performing well, the TS should be around zero TS indicates the direction of the forecasting error if the TS is positive -- increase the forecasts, if the TS is negative -- decrease the forecasts. i MAD i

48 Monitoring and Controlling a Forecasting Model Tracking Signal The value of the TS can be used to automatically trigger new parameter values of a model, thereby correcting model performance. If the limits are set too narrow, the parameter values will be changed too often. If the limits are set too wide, the parameter values will not be changed often enough and accuracy will suffer.

49 Tracking Signal Computation Mo Fcst Act Error RSFE Abs Error Cum MAD TS Error

50 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error Error = Actual - Forecast = = -10

51 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error RSFE = 6 Errors = NA + (-10) = -10

52 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error Abs Error = Error = -10 = 10

53 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error Cum Error = 6 Errors = NA + 10 = 10

54 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum Error MAD MAD = 6 Errors /n = 10/1 = 10 TS

55 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error TS = RSFE/MAD = -10/10 = -1

56 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error Error = Actual - Forecast = = -5

57 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error RSFE = 6 Errors = (-10) + (-5) = -15

58 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error Abs Error = Error = -5 = 5

59 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error Cum Error = 6 Errors = = 15

60 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error MAD = 6 Errors /n = 15/2 = 7.5

61 Tracking Signal Computation Mo Forc Act Error RSFE Abs Error Cum MAD TS Error TS = RSFE/MAD = -15/7.5 = -2

62 Plot of a Tracking Signal Signal exceeded limit + Upper control limit Tracking signal MAD 0 Acceptable range - Lower control limit Time

63 Tracking Signals Actual Demand Forecast Actual demand Tracking Signal Time Tracking Singal

64 NOTE on TS ¾ The cumulative forecast error reflects the bias in forecasts, which is the persistent tendency for forecaststo be greater or less than the actual values of a time series. ¾ Tracking signal values are compared to predetermined limits based on judgment and experience. They often range from r3 to r8; for the most part, we shall use limits of ±4, which are roughly comparable to three standard deviation limits. ¾ Values within the limits suggest but do not guarantee that the forecast is performing adequately.

65 Statistical Control Charts The control chart approach involves setting upper and lower limits for individual forecast errors(instead of cumulative errors, as in the case with a tracking signal). The limits are multiples of the square root of MSE (The square root of MSE is used in practice as an estimate of the standard deviation, V, of the distribution of errors). V = (D t - F t ) 2 n This methods assumes (a) Forecast errors are randomly distributed around a mean of zero and (b) The distribution of errors is normal. 9 Using V we can calculate statistical control limits for the forecast error

66 Statistical Control Charts (Contd.) 9 Recall that for a ND, approximately 95% of the values (errors in this case) can be expected to fall within limits of 0 r 2V, and approximately 99.7% of the values can be expected to fall within r 3V of zero. 9 Hence, if the forecast is in control, 99.7% or 95% of the errors should fall within the limits, depending upon whether r 3V or r 2V limits are used. 9 Points that fall outside these limitsshould be regarded as evidence that corrective action is needed [that is the forecast is not performing adequately).

67 Statistical Control Charts Errors Period

68 Statistical Control Charts UCL = +3V 6.12 Errors LCL = -3V Period

69 Ranging Forecasts Forecasts for future periods are only estimates and are subject to error. One way to deal with uncertainty is to develop best-estimate forecasts and the ranges within which the actual data are likely to fall. The ranges of a forecast are defined by the upper and lower limits of a confidence interval.

70 Ranging Forecasts The ranges or limits of a forecast are estimated by: where: Upper limit = Y + t(s yx ) Lower limit = Y - t(s yx ) Y = best-estimate forecast t = number of standard deviations from the mean of the distribution to provide a given probability of exceeding the limits through chance s yx = standard error of the forecast

71 Ranging Forecasts The standard error (deviation) of the forecast is computed as: s = yx 2 y - a y - b xy n - 2

72 Example: Railroad Products Co. Ranging Forecasts Recall that linear regression analysis provided a forecast of annual sales for RPC in year 8 equal to \$20.55 million. Set the limits (ranges) of the forecast so that there is only a 5 percent probability of exceeding the limits by chance.

73 Example: Railroad Products Co. Ranging Forecasts Step 1: Compute the standard error of the forecasts, s yx. s yx (93).0801(15, 440) = = Step 2: Determine the appropriate value for t. n = 7, so degrees of freedom = n 2 = 5. Area in upper tail =.05/2 =.025 Statistical Table shows t =

74 Example: Railroad Products Co. Ranging Forecasts Step 3: Compute upper and lower limits. Upper limit = (.5748) = = Lower limit = (.5748) = = We are 95% confident that the actual sales for year 8 will be between \$ and \$ million.

75 Criteria/factor to be considered for Selecting a Forecasting Method Cost Accuracy Data available Time span Nature of products and services Impulse response and noise dampening

76 Criteria for Selecting a Forecasting Method Cost and Accuracy There is a trade-off between cost and accuracy; generally, more forecast accuracy can be obtained at a cost. High-accuracy approaches have disadvantages: Use more data Data are ordinarily more difficult to obtain The models are more costly to design, implement, and operate Take longer to use - Low/Moderate-Cost Approaches statistical models, historical analogies, executive-committee consensus - High-Cost Approaches complex econometric models, Delphi, and market research

77 Criteria for Selecting a Forecasting Method Availability of historical data Is the necessary data available or can it be economically obtained? If the need is to forecast sales of a new product, then a customer survey may not be practical; instead, historical analogy or market research may have to be used.

78 Criteria for Selecting a Forecasting Method Time Span What operations resource is being forecast and for what purpose? Short-term staffing needs might best be forecast with moving average or exponential smoothing models. Long-term factory capacity needs might best be predicted with regression or executivecommittee consensus methods.

79 Criteria for Selecting a Forecasting Method Nature of Products and Services Is the product/service high cost or high volume? Where is the product/service in its life cycle? Does the product/service have seasonal demand fluctuations?

80 Criteria for Selecting a Forecasting Method Impulse Response and Noise Dampening An appropriate balance must be achieved between: How responsive we want the forecasting model to be to changes in the actual demand data Our desire to suppress undesirable chance variation or noise in the demand data

81 Reasons for Ineffective Forecasting Not involving a broad cross section of people Not recognizing that forecasting is integral to business planning Not forecasting the right things Not selecting an appropriate forecasting method Not tracking the accuracy of the forecasting models Not recognizing that forecasts will always be wrong

82 Forecasting in Small Businesses and Start-Up Ventures Forecasting for these businesses can be difficult for the following reasons: Not enough personnel with the time to forecast Personnel lack the necessary skills to develop good forecasts These businesses are not data-rich environments Forecasting for new products/services is always difficult, even for the experienced forecaster

83 Sources of Forecasting Data and Help Government agencies at the local, regional, state, and federal levels Industry associations Consulting companies

84 Some Specific Forecasting Data Consumer Confidence Index Consumer Price Index (CPI) Gross Domestic Product (GDP) Index of Leading Economic Indicators Personal Income and Consumption Producer Price Index (PPI) Purchasing Manager s Index Retail Sales

85 NOTE The wise decision maker does not limit forecasting decisions to a single technique but combines the subjective and objective methods. Furthermore, the approximate way of defining forecast could be Forecast = Projection r Judgment Good Forecasting has to be determined with the tool : DSS

### Forecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs

PRODUCTION PLANNING AND CONTROL CHAPTER 2: FORECASTING Forecasting the first step in planning. Estimating the future demand for products and services and the necessary resources to produce these outputs

### Median and Average Sales Prices of New Homes Sold in United States

Jan 1963 \$17,200 (NA) Feb 1963 \$17,700 (NA) Mar 1963 \$18,200 (NA) Apr 1963 \$18,200 (NA) May 1963 \$17,500 (NA) Jun 1963 \$18,000 (NA) Jul 1963 \$18,400 (NA) Aug 1963 \$17,800 (NA) Sep 1963 \$17,900 (NA) Oct

### HOSPIRA (HSP US) HISTORICAL COMMON STOCK PRICE INFORMATION

30-Apr-2004 28.35 29.00 28.20 28.46 28.55 03-May-2004 28.50 28.70 26.80 27.04 27.21 04-May-2004 26.90 26.99 26.00 26.00 26.38 05-May-2004 26.05 26.69 26.00 26.35 26.34 06-May-2004 26.31 26.35 26.05 26.26

### MGT 267 PROJECT. Forecasting the United States Retail Sales of the Pharmacies and Drug Stores. Done by: Shunwei Wang & Mohammad Zainal

MGT 267 PROJECT Forecasting the United States Retail Sales of the Pharmacies and Drug Stores Done by: Shunwei Wang & Mohammad Zainal Dec. 2002 The retail sale (Million) ABSTRACT The present study aims

### Module 6: Introduction to Time Series Forecasting

Using Statistical Data to Make Decisions Module 6: Introduction to Time Series Forecasting Titus Awokuse and Tom Ilvento, University of Delaware, College of Agriculture and Natural Resources, Food and

### COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) CHARTERED BANK ADMINISTERED INTEREST RATES - PRIME BUSINESS*

COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) 2 Fixed Rates Variable Rates FIXED RATES OF THE PAST 25 YEARS AVERAGE RESIDENTIAL MORTGAGE LENDING RATE - 5 YEAR* (Per cent) Year Jan Feb Mar Apr May Jun

### COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) CHARTERED BANK ADMINISTERED INTEREST RATES - PRIME BUSINESS*

COMPARISON OF FIXED & VARIABLE RATES (25 YEARS) 2 Fixed Rates Variable Rates FIXED RATES OF THE PAST 25 YEARS AVERAGE RESIDENTIAL MORTGAGE LENDING RATE - 5 YEAR* (Per cent) Year Jan Feb Mar Apr May Jun

### Production Planning. Chapter 4 Forecasting. Overview. Overview. Chapter 04 Forecasting 1. 7 Steps to a Forecast. What is forecasting?

Chapter 4 Forecasting Production Planning MRP Purchasing Sales Forecast Aggregate Planning Master Production Schedule Production Scheduling Production What is forecasting? Types of forecasts 7 steps of

### Demand Forecasting When a product is produced for a market, the demand occurs in the future. The production planning cannot be accomplished unless

Demand Forecasting When a product is produced for a market, the demand occurs in the future. The production planning cannot be accomplished unless the volume of the demand known. The success of the business

### 2) The three categories of forecasting models are time series, quantitative, and qualitative. 2)

Exam Name TRUE/FALSE. Write 'T' if the statement is true and 'F' if the statement is false. 1) Regression is always a superior forecasting method to exponential smoothing, so regression should be used

### CALL VOLUME FORECASTING FOR SERVICE DESKS

CALL VOLUME FORECASTING FOR SERVICE DESKS Krishna Murthy Dasari Satyam Computer Services Ltd. This paper discusses the practical role of forecasting for Service Desk call volumes. Although there are many

### Industry Environment and Concepts for Forecasting 1

Table of Contents Industry Environment and Concepts for Forecasting 1 Forecasting Methods Overview...2 Multilevel Forecasting...3 Demand Forecasting...4 Integrating Information...5 Simplifying the Forecast...6

### Demand forecasting & Aggregate planning in a Supply chain. Session Speaker Prof.P.S.Satish

Demand forecasting & Aggregate planning in a Supply chain Session Speaker Prof.P.S.Satish 1 Introduction PEMP-EMM2506 Forecasting provides an estimate of future demand Factors that influence demand and

### Section A. Index. Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1. Page 1 of 11. EduPristine CMA - Part I

Index Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting techniques... 1 EduPristine CMA - Part I Page 1 of 11 Section A. Planning, Budgeting and Forecasting Section A.2 Forecasting

### THE UNIVERSITY OF BOLTON

JANUARY Jan 1 6.44 8.24 12.23 2.17 4.06 5.46 Jan 2 6.44 8.24 12.24 2.20 4.07 5.47 Jan 3 6.44 8.24 12.24 2.21 4.08 5.48 Jan 4 6.44 8.24 12.25 2.22 4.09 5.49 Jan 5 6.43 8.23 12.25 2.24 4.10 5.50 Jan 6 6.43

### Ch.3 Demand Forecasting.

Part 3 : Acquisition & Production Support. Ch.3 Demand Forecasting. Edited by Dr. Seung Hyun Lee (Ph.D., CPL) IEMS Research Center, E-mail : lkangsan@iems.co.kr Demand Forecasting. Definition. An estimate

### Smoothing methods. Marzena Narodzonek-Karpowska. Prof. Dr. W. Toporowski Institut für Marketing & Handel Abteilung Handel

Smoothing methods Marzena Narodzonek-Karpowska Prof. Dr. W. Toporowski Institut für Marketing & Handel Abteilung Handel What Is Forecasting? Process of predicting a future event Underlying basis of all

### Forecasting DISCUSSION QUESTIONS

4 C H A P T E R Forecasting DISCUSSION QUESTIONS 1. Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. When

### Analyzing price seasonality

Analyzing price seasonality Asfaw Negassa and Shahidur Rashid Presented at the COMESA policy seminar Food price variability: Causes, consequences, and policy options" on 25-26 January 2010 in Maputo, Mozambique

### NAV HISTORY OF DBH FIRST MUTUAL FUND (DBH1STMF)

NAV HISTORY OF DBH FIRST MUTUAL FUND () Date NAV 11-Aug-16 10.68 8.66 0.38% -0.07% 0.45% 3.81% 04-Aug-16 10.64 8.66-0.19% 0.87% -1.05% 3.76% 28-Jul-16 10.66 8.59 0.00% -0.34% 0.34% 3.89% 21-Jul-16 10.66

### Slides Prepared by JOHN S. LOUCKS St. Edward s University

s Prepared by JOHN S. LOUCKS St. Edward s University 2002 South-Western/Thomson Learning 1 Chapter 18 Forecasting Time Series and Time Series Methods Components of a Time Series Smoothing Methods Trend

### AT&T Global Network Client for Windows Product Support Matrix January 29, 2015

AT&T Global Network Client for Windows Product Support Matrix January 29, 2015 Product Support Matrix Following is the Product Support Matrix for the AT&T Global Network Client. See the AT&T Global Network

### S&P Year Rolling Period Total Returns

S&P 500 10 Year Rolling Period Total Returns Summary: 1926 June 2013 700% 600% 500% 400% 300% 200% 100% 0% 100% Scatter chart of all 931 ten year periods. There were 931 ten year rolling periods from January

### FORECASTING. Operations Management

2013 FORECASTING Brad Fink CIT 492 Operations Management Executive Summary Woodlawn hospital needs to forecast type A blood so there is no shortage for the week of 12 October, to correctly forecast, a

### Using INZight for Time series analysis. A step-by-step guide.

Using INZight for Time series analysis. A step-by-step guide. inzight can be downloaded from http://www.stat.auckland.ac.nz/~wild/inzight/index.html Step 1 Click on START_iNZightVIT.bat. Step 2 Click on

### Demand Forecasting LEARNING OBJECTIVES IEEM 517. 1. Understand commonly used forecasting techniques. 2. Learn to evaluate forecasts

IEEM 57 Demand Forecasting LEARNING OBJECTIVES. Understand commonly used forecasting techniques. Learn to evaluate forecasts 3. Learn to choose appropriate forecasting techniques CONTENTS Motivation Forecast

### Time series Forecasting using Holt-Winters Exponential Smoothing

Time series Forecasting using Holt-Winters Exponential Smoothing Prajakta S. Kalekar(04329008) Kanwal Rekhi School of Information Technology Under the guidance of Prof. Bernard December 6, 2004 Abstract

### How to Construct a Seasonal Index

How to Construct a Seasonal Index Methods of Constructing a Seasonal Index There are several ways to construct a seasonal index. The simplest is to produce a graph with the factor being studied (i.e.,

### Forecasting Methods. What is forecasting? Why is forecasting important? How can we evaluate a future demand? How do we make mistakes?

Forecasting Methods What is forecasting? Why is forecasting important? How can we evaluate a future demand? How do we make mistakes? Prod - Forecasting Methods Contents. FRAMEWORK OF PLANNING DECISIONS....

### INCREASING FORECASTING ACCURACY OF TREND DEMAND BY NON-LINEAR OPTIMIZATION OF THE SMOOTHING CONSTANT

58 INCREASING FORECASTING ACCURACY OF TREND DEMAND BY NON-LINEAR OPTIMIZATION OF THE SMOOTHING CONSTANT Sudipa Sarker 1 * and Mahbub Hossain 2 1 Department of Industrial and Production Engineering Bangladesh

### CHAPTER 11 FORECASTING AND DEMAND PLANNING

OM CHAPTER 11 FORECASTING AND DEMAND PLANNING DAVID A. COLLIER AND JAMES R. EVANS 1 Chapter 11 Learning Outcomes l e a r n i n g o u t c o m e s LO1 Describe the importance of forecasting to the value

### Enhanced Vessel Traffic Management System Booking Slots Available and Vessels Booked per Day From 12-JAN-2016 To 30-JUN-2017

From -JAN- To -JUN- -JAN- VIRP Page Period Period Period -JAN- 8 -JAN- 8 9 -JAN- 8 8 -JAN- -JAN- -JAN- 8-JAN- 9-JAN- -JAN- -JAN- -JAN- -JAN- -JAN- -JAN- -JAN- -JAN- 8-JAN- 9-JAN- -JAN- -JAN- -FEB- : days

### 2. What is the general linear model to be used to model linear trend? (Write out the model) = + + + or

Simple and Multiple Regression Analysis Example: Explore the relationships among Month, Adv.\$ and Sales \$: 1. Prepare a scatter plot of these data. The scatter plots for Adv.\$ versus Sales, and Month versus

### COE BIDDING RESULTS 2009 Category B Cars >1600 cc

Quota System A COE BIDDING RESULTS 2009 B Jan-2009 Quota 1,839 1,839 1,100 1,099 274 268 409 411 767 758 Successful bids 1,784 1,832 1,100 1,097 274 260 401 386 763 748 Bids received 2,541 2,109 1,332

### Consumer prices and the money supply

Consumer prices and the money supply Annual rise. Per cent. -year moving average Money supply Consumer prices - - 9 9 9 96 98 Sources: Statistics Norway and Norges Bank JB Terra Kapitalmarkedsdager, Gardermoen.

### Analysis One Code Desc. Transaction Amount. Fiscal Period

Analysis One Code Desc Transaction Amount Fiscal Period 57.63 Oct-12 12.13 Oct-12-38.90 Oct-12-773.00 Oct-12-800.00 Oct-12-187.00 Oct-12-82.00 Oct-12-82.00 Oct-12-110.00 Oct-12-1115.25 Oct-12-71.00 Oct-12-41.00

Nebraska Monthly Economic Indicators: May 20, 2016 Prepared by the UNL College of Business Administration, Department of Economics Authors: Dr. Eric Thompson, Dr. William Walstad Leading Economic Indicator...1

### Case 2:08-cv-02463-ABC-E Document 1-4 Filed 04/15/2008 Page 1 of 138. Exhibit 8

Case 2:08-cv-02463-ABC-E Document 1-4 Filed 04/15/2008 Page 1 of 138 Exhibit 8 Case 2:08-cv-02463-ABC-E Document 1-4 Filed 04/15/2008 Page 2 of 138 Domain Name: CELLULARVERISON.COM Updated Date: 12-dec-2007

### IT S ALL ABOUT THE CUSTOMER FORECASTING 101

IT S ALL ABOUT THE CUSTOMER FORECASTING 101 Ed White CPIM, CIRM, CSCP, CPF, LSSBB Chief Value Officer Jade Trillium Consulting April 01, 2015 Biography Ed White CPIM CIRM CSCP CPF LSSBB is the founder

### 2.2 Elimination of Trend and Seasonality

26 CHAPTER 2. TREND AND SEASONAL COMPONENTS 2.2 Elimination of Trend and Seasonality Here we assume that the TS model is additive and there exist both trend and seasonal components, that is X t = m t +

### Week TSX Index 1 8480 2 8470 3 8475 4 8510 5 8500 6 8480

1) The S & P/TSX Composite Index is based on common stock prices of a group of Canadian stocks. The weekly close level of the TSX for 6 weeks are shown: Week TSX Index 1 8480 2 8470 3 8475 4 8510 5 8500

### 16 : Demand Forecasting

16 : Demand Forecasting 1 Session Outline Demand Forecasting Subjective methods can be used only when past data is not available. When past data is available, it is advisable that firms should use statistical

### TIME SERIES ANALYSIS. A time series is essentially composed of the following four components:

TIME SERIES ANALYSIS A time series is a sequence of data indexed by time, often comprising uniformly spaced observations. It is formed by collecting data over a long range of time at a regular time interval

### 2015-16 BCOE Payroll Calendar. Monday Tuesday Wednesday Thursday Friday Jun 29 30 Jul 1 2 3. Full Force Calc

July 2015 CM Period 1501075 July 2015 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 August 2015 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

### 4. Simple regression. QBUS6840 Predictive Analytics. https://www.otexts.org/fpp/4

4. Simple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/4 Outline The simple linear model Least squares estimation Forecasting with regression Non-linear functional forms Regression

### Methodology For Illinois Electric Customers and Sales Forecasts: 2016-2025

Methodology For Illinois Electric Customers and Sales Forecasts: 2016-2025 In December 2014, an electric rate case was finalized in MEC s Illinois service territory. As a result of the implementation of

### ANNEXURE 1 STATUS OF 518 DEMAT REQUESTS PENDING WITH NSDL

ANNEXURE 1 STATUS OF 518 DEMAT REQUESTS PENDING WITH NSDL Sr. No. Demat Request No.(DRN) DP ID Client ID Date of Demat Request Received Quantity Requested Date of Demat Request Processed No. of days of

### USING TIME SERIES CHARTS TO ANALYZE FINANCIAL DATA (Presented at 2002 Annual Quality Conference)

USING TIME SERIES CHARTS TO ANALYZE FINANCIAL DATA (Presented at 2002 Annual Quality Conference) William McNeese Walt Wilson Business Process Improvement Mayer Electric Company, Inc. 77429 Birmingham,

### Architectural Services Data Summary March 2011

Firms Typically Small in Size According to the latest U.S. Census Survey of Business Owners, majority of the firms under the description Architectural Services are less than 500 in staff size (99.78%).

### Objectives of Chapters 7,8

Objectives of Chapters 7,8 Planning Demand and Supply in a SC: (Ch7, 8, 9) Ch7 Describes methodologies that can be used to forecast future demand based on historical data. Ch8 Describes the aggregate planning

### TIME SERIES ANALYSIS & FORECASTING

CHAPTER 19 TIME SERIES ANALYSIS & FORECASTING Basic Concepts 1. Time Series Analysis BASIC CONCEPTS AND FORMULA The term Time Series means a set of observations concurring any activity against different

### Forecasting in supply chains

1 Forecasting in supply chains Role of demand forecasting Effective transportation system or supply chain design is predicated on the availability of accurate inputs to the modeling process. One of the

### Jinadasa Gamage, Professor of Mathematics, Illinois State University, Normal, IL, e- mail: jina@ilstu.edu

Submission for ARCH, October 31, 2006 Jinadasa Gamage, Professor of Mathematics, Illinois State University, Normal, IL, e- mail: jina@ilstu.edu Jed L. Linfield, FSA, MAAA, Health Actuary, Kaiser Permanente,

### TIME SERIES ANALYSIS

TIME SERIES ANALYSIS L.M. BHAR AND V.K.SHARMA Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-0 02 lmb@iasri.res.in. Introduction Time series (TS) data refers to observations

### Time Series AS90641. This Is How You Do. Kim Freeman

AS90641 This Is How You Do Time Series Kim Freeman This book covers NZQA, Level 3 Mathematics Statistics and Modelling 3.1 Determine the Trend for Time Series Level: 3, Credits: 3, Assessment: Internal

### LeSueur, Jeff. Marketing Automation: Practical Steps to More Effective Direct Marketing. Copyright 2007, SAS Institute Inc., Cary, North Carolina,

Preface. Overview. PART 1: Marketing Financials. Chapter 1 Profit and Loss Fundamentals. Chapter 2 Profit and Loss Component Details. Chapter 3 Managing the P&L. Chapter 4 Measuring Marketing Effectiveness.

### Pricing and Strategy for Muni BMA Swaps

J.P. Morgan Management Municipal Strategy Note BMA Basis Swaps: Can be used to trade the relative value of Libor against short maturity tax exempt bonds. Imply future tax rates and can be used to take

### Comparative Study of Demand Forecast Accuracy for Healthcare Products Using Linear and Non Linear Regression

International Journal of Business and Management Invention ISSN (Online): 2319 8028, ISSN (Print): 2319 801X Volume 3 Issue 5ǁ May. 2014 ǁ PP.01-10 Comparative Study of Demand Forecast Accuracy for Healthcare

### Outline: Demand Forecasting

Outline: Demand Forecasting Given the limited background from the surveys and that Chapter 7 in the book is complex, we will cover less material. The role of forecasting in the chain Characteristics of

### The introduction of new methods for price observations in the Consumer Price Index (CPI) New methods for airline tickets and package holidays

Statistics Netherlands Economics, Enterprises and NA Government Finance and Consumer Prices P.O.Box 24500 2490 HA Den Haag The Netherlands The introduction of new methods for price observations in the

### OBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS

OBJECTIVE ASSESSMENT OF FORECASTING ASSIGNMENTS USING SOME FUNCTION OF PREDICTION ERRORS CLARKE, Stephen R. Swinburne University of Technology Australia One way of examining forecasting methods via assignments

### A Primer on Forecasting Business Performance

A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.

### 8. Time Series and Prediction

8. Time Series and Prediction Definition: A time series is given by a sequence of the values of a variable observed at sequential points in time. e.g. daily maximum temperature, end of day share prices,

### Revenue Forecasting: Tips and Techniques for All Sources

Municipal Finance Institute December 4-5 Revenue Forecasting: Tips and Techniques for All Sources by Daniel Jordan, Ph.D., CGFM Director of Finance City of La Cañada Flintridge Adjunct Assistant Professor

### NATIONAL CREDIT UNION SHARE INSURANCE FUND

NATIONAL CREDIT UNION SHARE INSURANCE FUND PRELIMINARY & UNAUDITED FINANCIAL HIGHLIGHTS RENDELL L. JONES CHIEF FINANCIAL OFFICER MANAGEMENT OVERVIEW Balance Sheet Other - Insurance and Guarantee Program

### Regression and Time Series Analysis of Petroleum Product Sales in Masters. Energy oil and Gas

Regression and Time Series Analysis of Petroleum Product Sales in Masters Energy oil and Gas 1 Ezeliora Chukwuemeka Daniel 1 Department of Industrial and Production Engineering, Nnamdi Azikiwe University

### LSUF 24 th January 2006

Quality Control ISO 17025:2005 5.9 Assuring the quality of test and calibration results 5.9.1 The laboratory shall have quality control procedures for monitoring the validity of tests and calibrations

### Forecasting methods applied to engineering management

Forecasting methods applied to engineering management Áron Szász-Gábor Abstract. This paper presents arguments for the usefulness of a simple forecasting application package for sustaining operational

### An Aggregate Reserve Methodology for Health Claims

An Aggregate Reserve Methodology for Health Claims R = P M M Unpaid Paid F F Upaid Paid Where: R = The estimated claim reserve P = Observed paid claims M Paid, M Unpaid = Portion of exposure basis that

### Volatility in the Overnight Money-Market Rate

5 Volatility in the Overnight Money-Market Rate Allan Bødskov Andersen, Economics INTRODUCTION AND SUMMARY This article analyses the day-to-day fluctuations in the Danish overnight money-market rate during

### Outline. Role of Forecasting. Characteristics of Forecasts. Logistics and Supply Chain Management. Demand Forecasting

Logistics and Supply Chain Management Demand Forecasting 1 Outline The role of forecasting in a supply chain Characteristics ti of forecasts Components of forecasts and forecasting methods Basic approach

### Chapter 10 Capital Markets and the Pricing of Risk

Chapter 10 Capital Markets and the Pricing of Risk 10-1. The figure below shows the one-year return distribution for RCS stock. Calculate a. The expected return. b. The standard deviation of the return.

### P/T 2B: 2 nd Half of Term (8 weeks) Start: 25-AUG-2014 End: 19-OCT-2014 Start: 20-OCT-2014 End: 14-DEC-2014

2014-2015 SPECIAL TERM ACADEMIC CALENDAR FOR SCRANTON EDUCATION ONLINE (SEOL), MBA ONLINE, HUMAN RESOURCES ONLINE, NURSE ANESTHESIA and ERP PROGRAMS SPECIAL FALL 2014 TERM Key: P/T = Part of Term P/T Description

### P/T 2B: 2 nd Half of Term (8 weeks) Start: 26-AUG-2013 End: 20-OCT-2013 Start: 21-OCT-2013 End: 15-DEC-2013

2013-2014 SPECIAL TERM ACADEMIC CALENDAR FOR SCRANTON EDUCATION ONLINE (SEOL), MBA ONLINE, HUMAN RESOURCES ONLINE, NURSE ANESTHESIA and ERP PROGRAMS SPECIAL FALL 2013 TERM Key: P/T = Part of Term P/T Description

### P/T 2B: 2 nd Half of Term (8 weeks) Start: 24-AUG-2015 End: 18-OCT-2015 Start: 19-OCT-2015 End: 13-DEC-2015

2015-2016 SPECIAL TERM ACADEMIC CALENDAR For Scranton Education Online (SEOL), Masters of Business Administration Online, Masters of Accountancy Online, Health Administration Online, Health Informatics

### CTL.SC1x -Supply Chain & Logistics Fundamentals. Time Series Analysis. MIT Center for Transportation & Logistics

CTL.SC1x -Supply Chain & Logistics Fundamentals Time Series Analysis MIT Center for Transportation & Logistics Demand Sales By Month What do you notice? 2 Demand Sales by Week 3 Demand Sales by Day 4 Demand

### Agenda. Managing Uncertainty in the Supply Chain. The Economic Order Quantity. Classic inventory theory

Agenda Managing Uncertainty in the Supply Chain TIØ485 Produkjons- og nettverksøkonomi Lecture 3 Classic Inventory models Economic Order Quantity (aka Economic Lot Size) The (s,s) Inventory Policy Managing

### NTC Project: S01-PH10 (formerly I01-P10) 1 Forecasting Women s Apparel Sales Using Mathematical Modeling

1 Forecasting Women s Apparel Sales Using Mathematical Modeling Celia Frank* 1, Balaji Vemulapalli 1, Les M. Sztandera 2, Amar Raheja 3 1 School of Textiles and Materials Technology 2 Computer Information

### PROJECT REPORT FORECASTING ANALYTICS. Submitted By: Arka Sarkar ( ) Kushal Paliwal ( ) Malvika Gaur ( )

FORECASTING ANALYTICS PROJECT REPORT Submitted By: Arka Sarkar (613161) Kushal Paliwal (613128) Malvika Gaur (6131456) Shwaitang Singh (6131261) Executive Summary: Problem Description: We aim to forecast

### Simple Methods and Procedures Used in Forecasting

Simple Methods and Procedures Used in Forecasting The project prepared by : Sven Gingelmaier Michael Richter Under direction of the Maria Jadamus-Hacura What Is Forecasting? Prediction of future events

### Multivariate Time Series Analysis for Optimum Production Forecast: A Case Study of 7up Soft Drink Company in Nigeria (Pp )

An International Multi-Disciplinary Journal, Ethiopia Vol. 4 (3a) July, 2010 ISSN 1994-9057 (Print) ISSN 2070-0083 (Online) Multivariate Time Series Analysis for Optimum Production Forecast: A Case Study

### Energy Savings from Business Energy Feedback

Energy Savings from Business Energy Feedback Behavior, Energy, and Climate Change Conference 2015 October 21, 2015 Jim Stewart, Ph.D. INTRODUCTION 2 Study Background Xcel Energy runs the Business Energy

### Comparing share-price performance of a stock

Comparing share-price performance of a stock A How-to write-up by Pamela Peterson Drake Analysis of relative stock performance is challenging because stocks trade at different prices, indices are calculated

### Use of Statistical Forecasting Methods to Improve Demand Planning

Use of Statistical Forecasting Methods to Improve Demand Planning Talk given at the Swiss Days of Statistics 2004 Aarau, November 18th, 2004 Marcel Baumgartner marcel.baumgartner@nestle.com Nestec 1800

### Grain Stocks Estimates: Can Anything Explain the Market Surprises of Recent Years? Scott H. Irwin

Grain Stocks Estimates: Can Anything Explain the Market Surprises of Recent Years? Scott H. Irwin http://nationalhogfarmer.com/weekly-preview/1004-corn-controversies-hog-market http://online.wsj.com/news/articles/sb10001424052970203752604576641561657796544

### US Inflation Rate Consumer Price Index

1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 US Inflation Rate Consumer Price Index 14.0% 13.0% 12.0% 11.0%

### Improving Demand Forecasting

Improving Demand Forecasting 2 nd July 2013 John Tansley - CACI Overview The ideal forecasting process: Efficiency, transparency, accuracy Managing and understanding uncertainty: Limits to forecast accuracy,

### An alternative methodology for measuring financial services sector output in Ireland

An alternative methodology for measuring financial services sector output in Ireland 29 th April 214 Central Bank of Ireland Conference: Macro to Micro- A New Era in Financial Statistics Jenny Osborne-Kinch,

### Introduction to time series analysis

Introduction to time series analysis Margherita Gerolimetto November 3, 2010 1 What is a time series? A time series is a collection of observations ordered following a parameter that for us is time. Examples

### Diversifying with Negatively Correlated Investments. Monterosso Investment Management Company, LLC Q1 2011

Diversifying with Negatively Correlated Investments Monterosso Investment Management Company, LLC Q1 2011 Presentation Outline I. Five Things You Should Know About Managed Futures II. Diversification and

### Managing Staffing in High Demand Variability Environments

Managing Staffing in High Demand Variability Environments By: Dennis J. Monroe, Six Sigma Master Black Belt, Lean Master, and Vice President, Juran Institute, Inc. Many functions within a variety of businesses

### Forecasting in STATA: Tools and Tricks

Forecasting in STATA: Tools and Tricks Introduction This manual is intended to be a reference guide for time series forecasting in STATA. It will be updated periodically during the semester, and will be

### Project Cost & Schedule Monitoring Process Using MS Excel & MS Project

Project Cost & Schedule Monitoring Process Using MS Excel & MS Project Presented by: Rajesh Jujare About Us Solutions is founded with objectives a. To share its expertise and experiences to overcome the

### Statistical release P6242.1

Statistical release Retail trade sales (Preliminary) May 2014 Embargoed until: 16 July 2014 13:00 Enquiries: Forthcoming issue: Expected release date: User Information Services June 2014 13 August 2014

### Time Series and Forecasting

Chapter 22 Page 1 Time Series and Forecasting A time series is a sequence of observations of a random variable. Hence, it is a stochastic process. Examples include the monthly demand for a product, the

### Ashley Institute of Training Schedule of VET Tuition Fees 2015

Ashley Institute of Training Schedule of VET Fees Year of Study Group ID:DECE15G1 Total Course Fees \$ 12,000 29-Aug- 17-Oct- 50 14-Sep- 0.167 blended various \$2,000 CHC02 Best practice 24-Oct- 12-Dec-