DS 786 – Week 2

• Watch Videos
• Introduction to Forecasting (5:59)
• Forecast: a statement about the future
• To be of value, the forecast is about a future event that we don’t have complete certainty for?
• Given what we know now, how much do we expect to sell next month?
• Forecast Time Horizons
• Short-range: immediate future to three months
• Medium-range: several months to a year
• Long-range: more than one year
• Common Aspects of Forecasts
• Assumes causal system: path => future
• Do observations of past behavior have relevance in predicting future behavior?
• Forecasts are never perfect (we just need them to be good enough)
• Forecasts are more accurate for groups vs individuals
• (ie sales of the category greek yogurt not just single-serving non-fat strawberry)
• Forecast accuracy decreases with time
• Predicting next week’s sales has less uncertainty than predicting sales 50 weeks out.
• Steps in the forecasting process
• Step 1 Determine the purpose of the forecast
• Step 2 Establish time horizon
• Step 3 Select forecasting technique
• Step 4 Gather and analyze data in detail
• State any assumptions
• Validate data: may need to cleanse of filter for past events
• Step 5 Calculate forecast
• Step 6 Analyze/monitor the forecast –
• Measure accuracy, are results acceptable?
• Yes: Publish forecast
• Types of forecasts
• Judgmental
• Use subjective inputs
• Consumer surveys, opinions of managers and staff: ie Delphi method
• Time Series
• Use historical data and assuming the future will be similar to the past
• Associative forecasts
• Use explanatory variables other than time to predict the future.
• Time series foreacasting
• Plot your data and look for patterns
• Trend: Long-term movement in data
• Linear: steady increase or decrease over time
• Nonlinear: demand may be exponential, may both increase and decrease over product life cycle
• Seasonality: short-term regular variations in data
• What season does walgreens sell the most allergy meds
• Not just limited to fall/winter/spring/summer variations
• Which days are busiest for fancy restaurants?
• Daily cycle at starbucks: when do they sell the most coffee drinks?
• Irregular variations: caused by unexpected events
• Infrequent spikes or troughs like toilet paper in 2020
• Random variations: caused by change
• Learn how to do a Naive Forecast (4:33)
• Naive forecasts
• Stable time-series data
• eg. sold 50 tires last week, so expect to sell 50 this week
• Seasonal variations
• sold 50k heart shaped candy boxes last February so expect to sell 50k this February.
• Data with trends
• sold 50 lattes yesterday but 45 the day before so expect to sell 55 today!
• Naive forecasting: pros and cons
• Pros
• You only need one data point
• Can start soon
• It’s easy
• Easy to explain simply
• Cons
• Not very accurate
• Naive forecasts are useful as a benchmark or lower bound for accuracy
• Working with the Moving Average (4:54)
• Simple moving average
• The average of N most recent observations
• The larger the N, the smoother the forecast, but the greater the lag (ability to respond to “real” changes)
• How to Work with Weighted Moving Averages and Exponential Smoothing (6:38)
• Weighted moving average
• Premise: the most recent observations might have the best predictive value. Yet for simple moving averages, older data points have same importance as most recent
• Solution: Assign weights to give more importance to more recent observations. (Weights must be between 0 and 1 and all weights must sum to 1 or you get bias)
• Advantages: avoids over-smoothing and lag time is decreased
• Weights are often found through trial and error, no one set of weights work best for all situations.
• Exponential smoothing
• Exponential smoothing is a type of weighted moving average that considers all time periods that we have data for, but gives them exponentially less weight the further back in time they fall
• Measuring Forecast Accuracy for Stable Time Series Data (5:23)
• How to select a forecast
• for data without trends or seasonality, we could use
• a simple naive forecast
• a moving average
• a weighted moving average
• an exponential smoother
• We will get different answers with each technique
• How do we pick the one most likely to be close to what will happen
• We calculate past forecast accuracy
• Measuring forecast accuracy
• Many different measures exist, such as MSE (mean squared error) and MAPE but in this class we will only calculate mean absolute deviation (MAD)
• First define an error as the difference between the actual value and predicted value for that time period
• The take the absolute value of this error
• Sum these absolute values and take the average