Câu 3. Nêu các ph??ng pháp làm tr? chu?i th?i gian. Hãy làm tr?n chu?i s? li?u sau b?ng trung bình tr??t công th?c: V? 2 ?? th? so sánh Y và \( Y^{*} \) ? \begin{tabular}{|l|l|} \hline Quý & Y \\ \hline 1996Q1 4.47 \\ \hline 1996Q2 4.43 \\ \hline 1996Q3 4.44 \\ \hline 1996Q4 4.52 \\ \hline 1997Q1 4.63 \\ \hline 1997Q2 4.73 \\ \hline 1997Q3 4.82 \\ \hline 1997Q4 4.93 \\ \hline 1998Q1 4.99 \\ \hline 1998Q2 5.09 \\ \hline 1998Q3 5.03 \\ \hline 1998Q4 4.83 \\ \hline 1999Q1 4.66 \\ \hline 1999Q2 4.68 \\ \hline 1999Q3 4.67 \\ \hline 1999Q4 4.62 \\ \hline 2000Q1 4.55 \\ \hline 2000Q2 4.53 \\ \hline 2000Q3 4.67 \\ \hline 2000Q4 4.87 \\ \hline 2001Q1 4.92 \\ \hline 2001Q2 4.87 \\ \hline 2001Q3 4.85 \\ \hline 2001Q4 4.9 \\ \hline \end{tabular}
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Step 1: **Identify Smoothing Methods** - Common methods for smoothing time series include: - Moving Average - Exponential Smoothing - Weighted Moving Average Show more…
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Calculate the moving averages for each observation in the data based on periods 1, 2, 3, and 4. It should be centered on period t = 2. Use the function of =AVERAGE (D2:D5) in cell E3 and copy the formula down to cell E20. Show the moving averages plotted on the passenger series y(t). Do not forget to label x and y axes and adjust the values on the plot using the Figure1. Show your plot space provided below 2. Even though the moving averages help highlight the long-run trend of a time series, the moving-average model is not designed for making forecasts in the presence of trends. Explain the reason why we should not rely on moving averages for predicting future observations of a trending series. Year Quarter t y(t) 2009 1 1 110569 2 2 113433 3 3 118183 4 4 114932 2010 1 5 112337 2 6 117224 3 7 118863 4 8 116554 2011 1 9 116287 2 10 124077 3 11 126540 4 12 123559 2012 1 13 122607.4 2 14 129549.7 3 15 128105.9 4 16 126818.9 2013 1 17 123020.3 2 18 130158.9 3 19 133584.3 4 20 132147.9 2014 1 21 126932.4
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