Ch. 11 Forecasting Flashcards
- A process for predicting the future
- Forecasts become less accurate over time.
- Forecasts for aggregates are more accurate than forecasts for individual items.
- Forecasts are often used as a motivator or strategic objective.
- Forecasts should be developed using several different methods.
- A good forecast should consist of both a point as well as a range.
Forecasting Principles
What are the three types of Forecasting approaching?
- Judgmental
- Time Series
- Regression
A set of data points recorded over successive time periods
Projects past trends into the future
Chronologically ordered sequence of data values for one variable
Time Series
Examples of Time Series
- month unemployment rate for the period 2010 to 2019
- reports of the closing value Dow Jones Industrial average
technique that analyzes the past behavior of a time series variable to predict the future
extrapolation models
time series data set that contains no trend and the variance is constant.
stationary data
time series data set that contains a trend.
non stationary data
The degree of time lagged correlation in the time series
Autocorrelation
Four Common Time Series Accuracy Measures?
1 Mean Absolute Deviation (MAD)
2 Mean square error (MSE) (most common)
3 Mean absolute percentage error (MAPE)
4 Root Mean Square Error (RMSE)
A method for measuring the accuracy of a forecast by summing the absolute value of the differences between the actual and forecast values and dividing by the number of observations.
It is a standard error measurement that is used in time series analysis
Mean Absolute Deviation (MAD)
A method for measuring the accuracy of a forecast by summing the squared differences dived by the number of observations
It penalizes the larger errors by squaring them
Mean square error (MSE) (most common)
A method for measuring the accuracy of a forecast by summing the absolute percentage error
It is the average of absolute errors divided by actual observation values
It should not be used if there are zeros or near zeros in the actual data
Mean absolute percentage error (MAPE)
What is the simple moving average known as?
The smoothing method
A forecasting method based on averaging two or more consecutive time series data points
Simple moving average
A forecasting method based on placing a larger weight on the more recent of two or more consecutive time series observations
Weighted moving average
another averaging technique that is a forecasting method based on weighting the previous time series value using a smoothing coefficient
Exponential smoothing
a technique that involves taking the average of the averages.
Double moving average
- effective forecasting tool for time series data that exhibits a linear trend.
- it computes an estimate of the base, or expected, level of the time series, and the expected rate of increase or decrease per period
Double exponential smoothing (Holt’s Method)
The process of separating a times series into its basic components. You can use this to communicate time series to management
Decomposition
is the forecast value when the smooting coefficient for the simple exponential model is equal to one. (Also called the Naïve Model)
The forecast value when the smoothing value is one is the actual value for the previous period.
Previous actual value
when your data does not have a trend or a seasonal component what forecast methods should you use?
moving average and single expo smoothing
when your data has a seasonal component (with or without trend)
Decomposition or the Holt Winters method
when your data has a trend but does not have a seasonal component
Trend analysis or double expo smoothing
What are the four Time Series components?
Trend component
Seasonality component
Cyclic component
Irregular Component