Choose one of the forecasting methods and explain the rationale behind using it in real life.

I would choose to use the exponential smoothing forecast method. Exponential smoothing method is an average method that reacts more strongly to recent changes in demand than to more distant past data. Using this data will show how the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place. I would find this information very useful when examining the increased production of a product that appears to be higher in demand in the present than in the past Taylor (2011). For example, annual sales of toys will probably peak in the months of March and April, and perhaps during the summer with a much smaller peak. This pattern is likely to repeat every year, however, the relative amount of increase in sales during March may slowly change from year to year. During the month of march the sales for a particular toy may increase by 1 million dollars every year. We could add to our forecasts for every March the amount of 1 million dollars to account for this seasonal fluctuation.

Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model.

By looking at the data of other companies the fast food chain would be able to put together a forecast to determine if their business venture was viable. They could examine the sales data and determine through a exponential smoothing forecast if it made sense for them to enter into the market. This would show the trends and changes in the data more recently rather than in past time. The fast food industry of China is experiencing phenomenal growth and is one of the fastest growing sectors in the country, with the compounded annual growth rates of the market crossing 25%. Further, on the back of changing and busy lifestyle, fast emerging middle class population and surging...

...
Forecasting
HSM/260
January 17, 2014
Janice Gilstorff
Forecasting
Exercise 9.1
Forecasting is a guess of what the financial future holds (production output or sales). In the scenario in the book exercise 9.1 they want you to forecast what the 20X5 figures would be. It does give you some background information, such as the Human services expenses over the past four years.
20X1 [$5,250,000]
20X2 [$5,500,000]
20X3 [$6,000,000]
20X4 [$6,750,000]
Weighted moving averages and moving averages, just use the data for the past three fiscal years. This would look like this
Moving Averages-
20X2 [$5,500,000]
20X3 [$6,000,000]
20X4 [$6,750,000]
20X5 [$6,083,000]
With just the three we already knew the total of $18,250,000. If you divide the total by three you get, $6,083,000.
Weight averages-
20X2 $5,500,000 1=$5,500,000
20X3 $6,000,000 2=12,000,000
20X4 $6,750,000 3=$20,250,000
20X5 $6,300,000 6=$37,750,000
For 20X5 I divided by 6 (which represents the values 1+2+3=6), which equals $6,291,667 or $6,300,000 as a weighted average. From the information gathered a prediction for the forecast can be made.
Exponentialsmoothing:
The alpha method of 0.95 would work here. The formula would look like this: NF=LF + a (LD- LF)
Last Forecast (LF) = $6,300,000
Last Data (LD) = $6,750,000
a = 0.9
NF = LF + (LD LF)
NF = 6,300,000 +...

...Demand Forecasting Problems
Simple Regression
a) RCB manufacturers black & white television sets for overseas markets. Annual exports in thousands of units are tabulated below for the past 6 years. Given the long term decline in exports, forecast the expected number of units to be exported next year.
|Year |Exports |Year |Exports |
|1 |33 |4 |26 |
|2 |32 |5 |27 |
|3 |29 |6 |24 |
b) A small hospital is planning for future needs in its maternity wing. The data below show the number of births in each of the past eight years.
|Year |Births |Year |Births |
|1 |565 |5 |615 |
|2 |590 |6 |611 |
|3 |583 |7 |610 |
|4 |597 |8 |623 |
Use simple linear regression to forecast the annual number...

...M/W
Jaime Rossiter
12/4/13
FastFood Nation
Let’s be real, the idea of choosing fastfood is an attractive option. The ease of driving to a pick-up window to grab a delicious meal for a few dollars in under a couple of minutes is so hard to resist. I mean, who wants to drive to the grocery store to buy ingredients that cost more than an item on the value menu? Who wants to prepare and spend time cooking when you could just wait a few minutes to have someone make you food? Worst of all, who wants to clean up after the mess you made so you can repeat the cycle over again? I could see why many Americans choose such an appealing option. On the other hand, it appears that there are numerous consequences that people are too blind to notice. The entire experience about eating food among friends and families had been replaced by a rushed bite. It is to the point where people view fastfood as an essential part of their habitual life. This leads to many problems like health and economic issues. America may have evolved into a fastfood nation, or really a fat food nation.
To this day, it seems that fastfood is the “go-to” option because of its convenience, tastiness, and practicality. Unfortunately, the entire experience of eating food has transformed into a “routine…that is now taken...

...Forecasting
In order for a business to be successful it must come up with the most accurate forecast possible so they can plan for the demands. There are forecasting tools that assist with making calculations to receive the best outcome by your company’s needs. The tools are moving average, weighted moving average and exponentialsmoothing.
The moving average takes the total of actual demand for previous months then divides by the number of months added. The number of months that is used can be predefined such as using the previous three months. This is the simplest and easiest calculation but often is not accurate since it can have a lag in spotting trends (Murphy).
The weighted moving average is similar to the moving average but it places weights on each period usually with more recent periods weighing more. For example if you are averaging the past three months with the most recent month being the most valuable you would multiply the last month by .3 and then month before that at .2 and then the first month by .1 then by adding them together you would get the average with more of an emphasis on the month with the most weight (Career Education Corporation, 2010).
Lastly there is the exponentialsmoothing tool. The exponentialsmoothing tool is a moving average which is ideal for forecasting smaller items. It uses the most recent...

...Businesses use forecasting to predict future, trends, patterns, and business with data to develop a forecast. This data is used to predict future sales. In forecasting we use testing and reasonableness to predict future events. Companies use this method to compare their sales with other companies. Forecasting has many benefits to include; what is the popular product customers are purchasing, and it enhances cash flow, and identifies patterns and trends inside a corporation. Using this method is popular and is quite achieving when done effectively.
Forecasting can result in decrease in product cost, increase company efficiency, and increase revenue. This method has to be administered at it entirely to reap the best benefits. Forecasting also requires a company to keep record of inventory, sales, and customer satisfaction. Many items are needed such as; financial statements, accounting records. In order to be successful you have to know what the customers want and why they want it.
Something’s that can affect the benefits of forecasting is weather, consumer income for example a recession, changes in population, and product changes. I have notice with some businesses, for example Chik Fila years ago changed their chicken. Something like this could cause changes in forecasting and profits.
Eight Steps to Forecasting
Determine the use of the forecast-...

...with
weights of0.50 for the immediate preceding year and 0.3, 0.15, and 0.05 for the
three years before that?
F2013 = 0.50A2012 + 0.3A2011 + 0.15A2010 + 0.05A2009
=0.50(83000) + 0.30(67000) + 0.15(64000) + 0.05(48000)
= 41,500 + 20,100 + 9,600 + 2,400
= $73,600
$73,600 is the forecast for 2013
Q2. Using exponentialsmoothing with a weight of 0.6 on actual values:
a) If sales are $45,000 and $50,000 for 2010 and 2011, what would you forecast for 2012?
(The first forecast is equal to the actual value of the preceding year.)
Actual values are
2010: $45,000
2011: $50,000
α = 0.6
F2012 = 0.60A2011 + 0.40A2010
= 0.60(50000) + 0.40(45000)
=48000
Forecast for 2012 is $48,000
b) Given this forecast and actual 2012 sales of $53,000, what would you then forecast for2009?
Actual value of 2012 = $53,000
F2009 =
Q3. In question 4-1, taking actual 2009 sales of $48,000 as the forecast for 2010, what sales
would you forecast for 2011, 2012, and 2013, using exponentialsmoothing and a weight
a on actual values of (a) 0.4 and (b) 0.8?
a) α = 0.4
Actual values of 2009 = $48,000 and it is forecasted for 2010
We have an Actual value for 2010 = $64,000
F2011 = 0.4(64,000) + 0.6(48,000)
F2011= $54,400
Now we have both actual and forecasted values for 2011
Actual value for 2011= $67,000
F2012 = 0.4(67,000) +...

...Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgemental methods. Usage can differ between areas of application: for example, in hydrology, the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period.
Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible.[1]
Although quantitative analysis can be very precise, it is not always appropriate. Some experts in the field of forecasting have advised against the use of mean square error to compare forecasting methods.[2]
-------------------------------------------------
Categories of forecasting methods
[edit]Qualitative vs. quantitative methods
Qualitative forecasting...

...ABSTRACT
The purpose of the project is to determine the most suitable technique to generate the forecast of cocoa production. The models understudied are based on Univariate Modelling Techniques i.e. Naïve with Trend Model, Average Change Model, Average Percent Change Model, Single ExponentialSmoothing, Double ExponentialSmoothing and ARESS method. These models are normally used to determine the short-term forecasts (one month ahead) by analyzing the pattern such as monthly cocoa production. The performances of the models are validated by retaining a portion of the monthly observations as holdout samples. The selection of the most suitable model was indicated by the smallest value of mean square error (MSE) and mean absolute percentage error (MAPE). Based on the analysis, ARRES Method Model is the most suitable model for forecasting monthly cocoa production.
Keywords: Univariate Modelling Techniques; Forecast Model; Mean Square
Error, Mean Absolute Percentage Error
INTRODUCTION
We refer very frequently to future events in our daily lives, we look forward, we have the foresight to do something, we are able to foretell, we foresee an event and we say that something is forthcoming. Forecasting can be defined as the science and the art to predict a future event with some degree of accuracy. There are two types of forecast which are event forecast and time series forecast. The future...

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