Forecasting. BUS 735: Business Decision Making and Research. exercises. Assess what we have learned
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1 Forecasting BUS 735: Business Decision Making and Research Goals and Agenda Goals and Agenda Learning Objective Learn how to identify regularities in time series data Learn popular univariate time series forecasting methods Learn how to use regression analysis for forecasting Practice what we learn. More practice. Assess what we have learned Active Learning Activity Lecture / Excel Example. Lecture / Excel Example. Lecture / Excel Example. In-class exercise. Read Chapter 15, Homework exercises. Quiz?? 2 Time Series Analysis 2.1 Example Data Working with Example Data Download data on the total number of Mining, Logging, and Construction employees (in thousands) in the La Crosse area from the Bureau of Labor Statistics website, To plot the data, we need to convert it to a single column: 1. First generate observation numbers 1 through Figure out what row the observation is in: =int((obs-1)/12)+1 3. Figure out what column the observation is in: =mod((obs-1),12)+1 4. Pick out the right observation: =offset([top corner],row,col) Create dates: through
2 Graphing Example Data In Excel: Insert, Line, Line with markers. Right click on data, select Select Data. Remove all the nonsense there. Select Add. Type Employment in Series Name. Select data for Series Values. Click Edit under Horizontal Axis Values. Select dates. 2.2 Time Series Characteristics Time Series Characteristics Trend: gradual, long-term movement of the data in a positive or negative direction. Cycle: repetitive up-and-down movement of the data, each cycle need not be the same length or magnitude. Seasonal pattern: up-and-down movement of the data that can be predicted quite accurately by the time of the year. Random variations: movements in the data that are otherwise unpredictable. 2.3 Forecasting Time Series Time Series Analysis Time series analysis: use of statistical methods to uncover trend, cyclical patterns, and seasonal patterns; and using this information to forecast future outcomes for a variable. Univariate time series: using many observations of only the variable of interest to forecast that variable. Multivariate time series: using one or more related variables to help forecast variable of interest. New housing sales may also help predict construction employment. National price measures for costs of building materials may also help predict construction employment. BUS 735: Focus on univariate time series analysis. 2
3 3 Time Series Methods 3.1 Smoothing Methods Moving Average Naïve forecast: Forecast for tomorrow is what happened today. Often used to measure usefulness of other time series forecasts. Moving average: uses several recent values to forecast the next period s outcome. MA t,q = 1 q x t i q i=1 x t denotes the value of the variable at time t, MA t,q denotes the Moving Average forecast for time t, using the most recent q periods. Moving Average Properties Moving average lag length: Longer lag lengths cause forecast to react more quickly/slowly to recent changes in the variable. Longer lag lengths cause forecast to be more smooth/volatile. Performs (forecasting accuracy) best with data that has No pronounced cyclical or seasonal variation. No long-term trend. Weighted Moving Average Weighted moving average: like a moving average, but larger weights are assigned to more recent observations. W MA t,q = q w i x t i i=1 w i is the weight given to the observation that occurred i periods ago. q i=1 w i = 1 Typically, w i > w i+1. More recent observations are viewed as more informative. 3
4 Exponential Smoothing Exponential smoothing: Averaging method using all previous data, but puts weights larger weight on the more recent observations. F t is the forecast for period t. F t = αx t 1 + (1 α)f t 1 x t 1 is the value of the variable in the previous time period, t 1. Useful for capturing information on recent seasonal or cyclical patterns (but with a lag). α [0, 1] is the smoothing parameter. When α is larger, more weight is given to most recent observations. Adjusted Exponential Smoothing Adjusted exponential smoothing: exponential smoothing that is adjusted to incorporate information on a long-term trend. AF t = F t + T t AF t is the adjusted exponential smoothing forecast. F t is the regular exponential smoothing forecast. T t is the latest estimate of the trend. Trend is computed by, T t = β(f t F t 1 ) + (1 β)t t 1 β [0, 1] is a trend weighting parameter. Trend formula allows for changing trend throughout the data. 3.2 Regression Methods Regression Regression line: equation of the line that describes the linear relationship between variable x and variable y. Need to assume that one variable causes another. x: independent or explanatory variable. y: dependent or outcome variable. Variable x can influence the value for variable y, but not vice versa. 4
5 Regression Model Example How does housing demand affects construction employment? x i : housing demand (independent variable, aka explanatory variable). y i : construction employment (dependent variable, aka outcome variable). Seasonal adjustment: how does winter season affect construction employment? Dummy variable: x i = 1 if winter, x i = 0 otherwise). y i : construction employment. 3.3 Seasonal Adjustment Seasonal Adjustment Previous methods capture information in recent movements, but not past seasonal fluctuations. Example, we may realize that construction employment is always lowest in Jan, Feb, and March. Seasonal factor: percentage of a total year that occurs in a specific season: S k = D k Dk D k is the sum of all values occurring in season k, for all years considered. Use your favorite forecasting method, forecast years only. For each season, multiply annual forecast by the seasonal factor. Regression Seasonal Adjustment Run a regression. Use trend as an explanatory variable. Use seasonal dummies as explanatory variables. Note: avoid multicolinearity, choose 1 fewer dummy variables than total seasons. Coefficient on seasonal dummies: impact of the season over and above excluded season dummy. 5
6 4 Forecast Accuracy 4.1 Absolute Deviations Forecast Accuracy Useful to compare forecasts from multiple techniques. Mean absolute deviation (MAD): average distance between the forecast value and the actual value. MAD = 1 T x t F t Mean absolute percentage deviation: measures the distance between the forecast and actual values as a percentage of the total values. MAP D = x t F t x t 4.2 Squared Deviations and Bias Forecast Accuracy and Bias Mean Squared Error (MSE): instead of taking absolute value of differences, square them: MSE = 1 T (x t F t ) 2 Variance of forecasts (population formula): V AR = 1 T (F t F t ) 2 Bias: when a forecast is persistently wrong, either in the positive direction or negative direction. Bias = MSE - VAR Root Mean Squared Error (RMSE) = MSE. 6
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