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- 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.

- Assumes causal system: path => future
- 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?
- No: Return to step 3
- Yes: Publish forecast

- Measure accuracy, are results acceptable?

- Types of forecasts
- Judgmental
- Use subjective inputs
- Consumer surveys, opinions of managers and staff: ie Delphi method

- Use subjective inputs
- 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.

- Judgmental
- 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

- Forecast: a statement about the future
- 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!

- Stable time-series data
- 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

- Pros

- Naive forecasts
- 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)

- Simple moving average
- 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

- Weighted moving average

- Introduction to Forecasting (5:59)
- 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

- for data without trends or seasonality, we could use
- 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

- How to select a forecast