p301 chapter 3 forecasting

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p301 chapter 3 forecasting
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2015-02-11 12:15:49
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  1. components of a time series
    • trends- when data is going up or down
    • Cyclical- the effect on a time series cause by economic cycles
    • Seasonality- short-term regular variations in data caused by calendar related events
    • Random or Irregular variations- unpredictable and unexplainable functions in a time series caused by unusual circumstance
  2. Naive Forecast
    • Uses a single previous value of a time series as the basis for a forecast
    • EX. the weather yesterday is going to be the same as tomorrow
    • this is the number 1 method that business use
  3. Averaging
    • these techniques work best when a series tends to vary an average
    •   - Averaging techniques smoothing variations in the data
    •    
    •     -
  4. Moving Average
    • (only looks at randomness)
    • (the more periods we use the less randomness)
    • techniques that averages a number of recent actual values, updates as new values become available
  5. Features of Forecasting
    • - the past is a prediction of the future
    • -they are not perfect
    • -Forecasting for a group is better the a item
    • -we can predict to days that are closer then ones that are farther away
  6. forecast responsiveness
    • is how quickly the forecast changes in actual demand
    • responsiveness and stability are different when it comes to forecasting 
    • when you use fewer moving averages its makes it more responsive
    • when you use more moving averages it makes it more stable
  7. weighted moving average
    • the most recent values in a time series are given more weight in computing a forecast
    •   - the choice of weights ,w, is somewhat arbitrary and involves trail and error
  8. simple Exponential Smoothing
    • (its a form of weighting moving average moving them exponentially )
    • Premise- the most recent observations might have the highest predictive value
    • There for we should give more weight to the more recent time periods when forecasting

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