Tham khảo tài liệu 'process or product monitoring and control_6', kỹ thuật - công nghệ, điện - điện tử phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | . What are Moving Average or Smoothing Techniques Table showing squared error for the mean for sample data Next we will examine the mean to see how well it predicts net income over time. The next table between 1985 Year gives the income and 1994. millions before taxes of a PC manufacturer Mean Error Squared Error 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 The MSE . The mean is not a good estimator when there are trends The question arises can we use the mean to forecast income if we suspect a trend A look at the graph below shows clearly that we should not do this. http div898 handbook pmc section4 3 of 4 5 1 2006 10 35 07 AM . What are Moving Average or Smoothing Techniques Average weighs all past observations equally In summary we state that 1. The simple average or mean of all past observations is only a useful estimate for forecasting when there are no trends. If there are trends use different estimates that take the trend into account. 2. The average weighs all past observations equally. For example the average of the values 3 4 5 is 4. We know of course that an average is computed by adding all the values and dividing the sum by the number of values. Another way of computing the average is by adding each value divided by the number of values or 3 3 4 3 5 3 1 4. The multiplier 1 3 is called the weight. In general NIST SEMATECH are the weights and of course they sum to 1. The HOME TOOLS RAIDS SEARCH BACK NEXT http div898 handbook pmc section4 4 of 4 5 1 2006 10 35 07 AM . Single Moving Average TOOLS 4 aids Iẹẽarch back Nixf 6. Process or Product Monitoring and Control . Introduction to Time Series .
