p301 chapter 3 forecasting
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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 shortterm 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

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

Averaging
 these techniques work best when a series tends to vary an average
  Averaging techniques smoothing variations in the data

 

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

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

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

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

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