C1-5 (Chalmers) What is a forecast? What are the characteristics of a good forecast? “A forecast is a probabilistic estimate or description of a future value or condition,” and should include a “mean, range, and probability estimate of that range.” Since a forecast is a probabilistic estimate, it should be a range of values, as opposed to a single value. For example, sales next week are expected to be $8000, with an 80% probability that sales will be between $6000 and $10,000. (p. 9-10) C1-7 (Chalmers) List and define the common time series patterns described in the book. Also, define typical causes of these time series patterns. Random Patterns: Definition: Random time series have no seasonality or trend, but “are the result of many influences that act independently to yield nonsystematic and non-repeating patterns about some average value. A series that is completely random, will “have a constant mean and no systematic patterns.” Typical causes: As stated in the definition, random patterns “are the result of many influences that act independently.” Random 20 15 Y 10 5 0 1 6 11 16 Tim e Trend Patterns: Definition: “A general increase or decrease in a time series that lasts approximately seven of more periods.” A trend can be linear, logarithmic, exponential, or some other nonlinear function. Typical causes: Population growth, early stages of the product life cycle, continuous economic growth etc. Trend 25 20 15 Y 10 5 0 1 6 11 16 Tim e Seasonal Patterns: Definition: “Results from events that are periodic and recurrent.” Seasonal patterns need not be annual, they could be weekly, daily or even hourly Typical causes: Annual weather changes (seasons), holidays, promotions, pay periods (monthly social security checks) etc. Seasonal 20 15 Y 10 5 0 1 6 11 16 Tim e Cyclical Patterns: Definition: Non-seasonal, and unequal fluctuations in a series. Cyclical patterns are difficult to forecast because the period of the peaks and troughs are unknown. Typical causes: The actual cause of these fluctuations is unknown, but some possible explanations include: population like cycles, product life cycles and long term weather conditions (droughts of floods damaging crops, leading to an economic down term). Autocorrelated Patterns: Definition: “The value of a series in one time period is related to the value of itself in previous periods.” That is, the most recent actual value is the best forecast of the next value. Random walk series are highly autocorrelated. Typical causes: Any series where momentum is significant factor. The stock market is usually a random walk pattern. Causes may include customer preference or brand loyalty, that generate a slow change in the demand for an item. (p. 13-20) C1-8 (Chalmers) What is an outliner and why are they so important? How do outliers relate to planned and unplanned events and interventions? Outlier Defined: Atypical data values. Generally very large or very small values that for some reason, are not typical. Any number of things can cause outliers. For example, power failures, strikes, serious or atypical weather etc. properly identifying and removing outliers is extremely important when forecasting. For example, Home Depot’s chainsaw sales following the big ice storm, would be a significant outlier that should be removed. Planned Events: Promotions are planned events, but if they are not properly documented, may appear to be unexplained outliers. Properly documenting promotions (time and impact of the promotion), not only helps to explain an outlier, but it also helps when determining the impact of a similar promotion in the future. Unplanned Events: Like planned events, unplanned events, such as a competitors promotion, should also be properly documented. Interventions: So, whether the intervention is a planned or unplanned event, it is important to be able to track its impact. (p. 19-20) C1-10 (Chalmers) Briefly explain the three general types of forecasting methods. Make up or relate to examples that are different from those of this chapter. Univariate: Defined: “use the past, internal patterns in data to forecast the future.” Future values are a function of past values. Essentially all of the models we have learned to this point: smoothing, exponential smoothing, decomposition, linear trend and nonlinear trend, Box- Jenkins etc. Univariate methods are generally the most cost effective for short to medium term forecasts. Example: a simple weighted moving average to forecast December’s value. September = 450, October = 600, November = 575 Yt = .5Yt-3 + .3Yt-2 +.2Yt-1 Yt = .5(450) + .3(600) +.2(575) December forecast = 520 Multivariate: Defined: Multivariate, or “causal methods, make projections of the future by modeling the relationship between a series and other series.” That is, the dependent variable is a function of the independent predictor variables. Multivariate methods are generally more costly than univariate methods, and are generally not as accurate for short to medium- term forecasts. Example: Home mortgage rates may be some function of the Fed Funds Rate, and long- term bond rates. Mortgage rates = f(Fed Funds Rate, long-term bond rate) Qualitative: Defined: “Are based on the judgment and opinions of others concerning future trends, tastes, and technological change.” Better for very long term forecasts, where univariate and multivariate ineffective, or when there is insufficient data. Qualitative methods are often used to estimate demand for new products (for which there is no historical data). Example: Qualitative forecasting is used to predict what the Fed is likely to do with interest rates. Several economists are polled, and predictions can be made based on their opinions. Fed’s next interest rate move = ∑(economists projections)/n Forecast rate move = ¼ + 0 + ¼ + ½ + ¼ + 0 + ¼ + ¼ + ½ + ¼ / 10 = ¼ (p. 21-23) C1-11 (Chalmers) Describe the scientific method and how it relates to forecasting. Generally, the scientific method has four steps: 1. Observation and description of a phenomenon or group of phenomena. 2. Formulation of a hypothesis to explain the phenomena. 3. Use of the hypothesis to predict the existence of other phenomena, or to predict quantitatively the results of new observations. 4. Performance of experimental tests of the predictions by several independent experimenters and properly performed experiments. (http://teacher.nsrl.rochester.edu/phy_labs/AppendixE/AppendixE.html) “The scientific method is the process by which scientists, collectively and over time, endeavor to construct an accurate (that is, reliable, consistent and non-arbitrary) representation of the world. The scientific method attempts to minimize the influence of bias or prejudice in the experimenter when testing a hypothesis or a theory.” The seven steps in scientific method of forecasting are an extension of the four general steps. C1-11 (Chalmers) Describe the seven steps of the forecasting process discussed in this chapter. Since the forecasting process is a systematic, step-by-step process, and to avoid deterioration of the meaning through paraphrasing, it seemed logical to directly quote the steps from the book (page 26-27). I. Problem definition--There is a need to solve a problem or explain some phenomenon; that is, there is a need to plan or forecast some future event, for example, a product's demand. II. Information search--This is the process of collecting information about the behavior of the system in which the problem or phenomenon resides (i.e., what influences the time series). For example, to understand the behavior of the series we need past data about sales of the product, out-of-stock conditions, prices, sales of competitors' products, advertising expenditures, out-of- stock conditions of competitors, and the number of customers. III. Hypothesis/theory/model formulation--On the basis of the information and observations realized in step II, a hypothesis or hypothetical model is formulated to describe the important factors that influence the problem or phenomenon. For example, it may be hypothesized that demand is seasonal and trending or that demand is a function of price, advertising, number of competitors, and their prices. IV. Experimental design--Using facts gathered in steps I, II, and II/ and statistical/mathematical tools, experiments are designed to test the hypotheses and theories (e.g., fit a model to all data except last year's, then see how well the model does in forecasting last year through this year). This might be as simple as selecting a model from several models or designing an experiment where data is collected and divided into two groups. The first group (called in-sample data) is used in constructing the model; the second group (called out-of-sample data) is used to validate the model in a simulated forecasting environment. Using out-of-sample data is an effective way to judge the effectiveness of a model or theory; most would argue that this is an essential step when sufficient data exists. V. Execute the experiment--The experiment is designed and executed, then the results are measured and collected (e.g., fit the model to the data before last year and see how well it forecast last year through this year). Use the model fitted to in-sample data to forecast the out-of sample data. VI. Results analysis--The results of the experiment are analyzed in order to accept or reject the hypothesis or model (e.g., calculate appropriate error measures and perform statistical significance tests). Statistical diagnostic measures are used to judge the validity of the parts of the model and its forecasts. The model is accepted, modified, or rejected. Depending on the results achieved, several iterations may be made before converging on a best model. Finally, because most forecasts support ongoing processes of planning, the seventh step addresses ongoing use of the theory or model. VII. Ongoing maintenance and verifications - This is the process of ensuring that the model or theory is still valid and effective (e.g., diagnostic tools called tracking signals and other statistics can be monitored to ensure model validity).
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