untitled FORECASTING PROJECT 1 Use ARIMA methods to forecast a time series data set of your choice, subject to the requirements below. The data set should have at least 50 observations. It would be a...

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untitled FORECASTING PROJECT 1 Use ARIMA methods to forecast a time series data set of your choice, subject to the requirements below. The data set should have at least 50 observations. It would be a good idea to show me a plot before you start the project to avoid potential data difficulties. Please provide a source for the data (web link, but not the course website). The last observation should be recent, and must be the most up-to-date observation available from the source. Start by plotting the data. Briefly describe any patterns you see. Decide whether you need to take logs. If the data are clearly seasonal, e.g., monthly sales of beer, or if the plot seems to indicate a strong seasonal pattern, then it would be best to remove this component. (It can be put back in at the end). The easiest way to remove a seasonal component is to subtract out the seasonal averages. For example, if your data is monthly, subtract from each data value the average for the corresponding month. Use the ACF, PACF and AICC to select an ARIMA model, and to decide whether a constant term should be included in the model. Present the parameter estimate printout for the selected model. Write the complete form of the fitted model. (You SHOULD NOT give the printouts for the other models you tried. A table of the AICC values for all models will be sufficient. Furthermore, I do not need to see a printout of the data set. A plot is much more informative). Note that in Minitab, the MA parameters are -1 times the MA parameters as defined in class. For example, in an MA(1) model in Minitab, if the MA 1 parameter is -.9, the model is xt = εt + .9εt −1. For the selected model, comment on the Ljung-Box statistics, plot the residuals and the ACF and PACF of the residuals. Comment briefly on any problems revealed by this diagnostic checking. Plot the data, together with the forecasts (at lead times 1-50 or further, if possible), and the 95% forecast intervals. If you can’t get the computer to put the intervals on the plot, then just sketch them in (roughly) by hand. Comment briefly on whether the forecasts seem reasonable, and on whether the fore- cast intervals seem excessively wide. Length of Project: Please be brief. Just tell me what you did and why. Long explanations are - 2 - not needed. I think you should be able to do this project quite adequately with 5 pages of text, not including figures and tables. Please don’t submit a huge number of figures and tables. I would hope that the whole project, including figures and tables will be at most 10 pages. This is not an absolute limit, but there is really no advantage to be gained by exceeding it. If you need help: I will be happy to talk to you about your project, after class or in my office.
Answered Same DayMar 19, 2021

Answer To: untitled FORECASTING PROJECT 1 Use ARIMA methods to forecast a time series data set of your choice,...

Pooja answered on Mar 28 2021
129 Votes
1

Time Series Analysis for Quantity sales vs time :

Monthly sales data has been recorded for year 2016 to 2020.
Firstly, we plot a graph (Sequence chart) between quantity sales vs time period using
SPSS tool to check any type of
trend, seasonality or cyclic variation in the data.
Figure 1


Figure 1
In Figure 1 we found some trend in the data. As there is no constant mean in the data.
Mean is continuously changing with the time period i.e series is not stationary.
To make non stationary series to stationary we can do it through difference
transformation. we will take first order difference (Integrated component “d” in ARIMA
model) between the data values then again plot the graph between quantity sales vs
time period. (figure 2)
2
Figure 2


Figure 2
Now the series is having almost constant series. To make it more stationary we can
take the second order difference also. To detect seasonality, we will plot the
autocorrelation function (ACF) by calculating the residuals (observed minus mean for
each data point) and graphs. The graph of the residuals against a specified time
interval is called a lagged autocorrelation function. The null hypothesis for the ACF is
that the time series observations are not correlated to one another, i.e.; that any
pattern in the data is by chance only.
The alternative hypothesis is there is no correlation between the time series
observation.
3
ACF :
Table 1
Autocorrelations
Series: quantitysale
Lag
Autocorrelat
ion
Std.
Errora
Box-Ljung Statistic
Value Df Sig.b
1 -.661 .139 22.749 1 .000
2 .260 .137 26.353 2 .000
3 .011 .136 26.359 3 .000
4 -.194 .134 28.446 4 .000
5 .194 .133 30.591 5 .000
6 -.207 .131 33.085 6 .000
7 .201 .130 35.484 7 .000
8 -.165 .128 37.153 8 .000
9 .100 .127 37.775 9 .000
10 .054 .125 37.961 10 .000
11 -.054 .123 38.151 11 .000
12 -.083 .122 38.617 12 .000
13 .044 .120 38.751 13 .000
14 .041 .118 38.868 14 .000
15 -.086 .117 39.415 15 .001
16 .123 .115 40.554 16 .001

a. The underlying process assumed is independence (white
noise).
b. Based on the asymptotic chi-square approximation.
Figure 3
4



Partial Autocorrelations : The partial autocorrelation function (PACF) is also used to
detect trends and...
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