ADM 3301 Lecture Notes - Lecture 3: Time Series, Exponential Smoothing, Forecast Error

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1 Aug 2018

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> in a time series, each data point is associated with a specific point in time. > time-series data is often displayed with a scatter plot, with the horizontal axis = time. > a time series could contain one or more of the following components: > this is the pattern of demand fluctuations that occurs every year above or below the average demand. > random variations = blips (ups and downs) in the data caused by chance and unusual situations. For this reason we can not use this component to forecast future values. > a time series can be broken down into its individual components. > if the overall average of a time series remains the same, the time series is said to be stationary; otherwise, it is non stationary. > if it has no trend, it is stationary (e. g. , seasonality, cycles, random variations) > if a time series has an upward or downward trend, it is non stationary.

Related Questions

1. You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality.

Quarter Seasonal Index Seasonally Adjusted Data

Q1 .80 295

Q2 .85 299

Q3 1.15 270

Q4 --- 271

2. One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing?

A. Alpha and Delta

B. Delta and Gamma

C. Alpha and Gamma

D. Standard Deviation and Mean

3. When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient.

A. Ho: r = .05 p < .5

B. Ho: r = 0 p >.05

C. Ho: r ? 0 p?.05

D. Ho: r = 0 p?.05

In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable?

A. The estimated value is 80% of the average monthly seasonal estimate.
B. The estimate is .80 of the forecasted Y trend value.
C. The estimated value is .80 of the historical average CMA values.
D. The estimated value has 20% more variation than the average historical Y data values.

5. A Wendy's franchise owner notes that the sales per store has fallen below the stated national Wendy's outlet average of $1,368,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied?

A. H1: u ? $1,368,000 A one-tailed t-test to the left.
B. H1: u = $1,368,000 A two-tailed t-test.
C. H1: u < $1,368,000 A one-tailed t-test to the left.
D. H1: p < $1,368,000 A one-tailed test to the right

6. As the sample size from a population increases, for a given level of significance what happens to the null hypothesis rejection region and size of the t-table value?

A. The rejection region and the t-table value generally gets smaller for sample size below 31.
B. The rejection region gets larger and the t-table value generally gets smaller for sample sizes below 31.
C. The rejection region remains unchanged while the t-table value gets smaller for all sample sizes.
D. The zero mean hypothesis region gets larger and the t-table value gets larger as well for sample sizes below 31.

7. You obtained autocorrelation LBQ value of 18.58 for the 12^th lag from a data sample. Are the data significantly correlated at the lag examined or not? You want to be 95% confident in your answer.

A. Yes. The data are significantly correlated through the 12th lag.
B. No. The data are not significantly correlated through the 12^th lag.

C. No. Only the 12 lag period is not correlated.

D. You cannot tell since the number of sample observations is not provided.

E. The p-value is above .05 so the data is correlated.

8. Sometimes forecasters get lazy or forgetful and do not check the significance of XY data correlations and use the X variable to forecast Y. What is the result of this?

A. Type 2 error
B. Autocorrelation error
C. Type 3 error
D. Type 1 error

9. Do error measures indicate the statistical significance of forecast model variables?

A. Yes. They move in the same direction as statistical significance.
B. Yes. As error measures decrease the variable significance increases.
C. No They indicate only the magnitude of estimate error.
D. No. They indicate only the statistical significance of a forecast or fitted values.

10. In exponential smoothing what is the weight of the alpha coefficient for a time series data observation from the 3^rd previous period if the original alpha value is set at .8?

A. The weight cannot be calculated since the data observation is not given.
B. The weight is zero since the alpha value is set relatively high.
C. .548
D. .0064

11.Given the data series below for variables Y (Monthly Inventory Balance) and X (Monthly Sales) are they significantly correlated at the 95% confidence level and how can you tell?

Ending Inv. Bal. Y		Monthly Sales X
1544		5053
1913		5052
2028		7507
1178		2887
1554		3880
1910		4454
1208		3855
2467		8824
2101		5716

A. Yes. The correlation coefficient is .873 that is greater than .05.
B. Yes. The correlation p-value is .002 which is less than .05.
C. No. The correlation coefficient is above the p-value.
D.No. The correlation p-value is greater than the 95% confidence level.

12. You have forecast the sales for your company for the last 12 months and the forecast residuals are shown below. Are these residuals to be considered random?
Residuals

-24

-348

-892

-62

-378

-489

-342

490

578

198

A. Yes, since the residuals randomly vary in magnitude.
B. Yes since the residuals are positive and negative and vary in magnitude.
C. No, since the residuals are stationary and vary in magnitude.
D. No, since the residuals indicate positive slope.

13. Given the residuals in problem 12 above what is the RMSE for the forecast?

A. -101.0
B. 411.8
C. 169603
D. 220.1

14. Which form of exponential smoothing can result in a na

Please write a 250 word response to the below discussion. Please provide 2 scholarly sources.

The efficient market hypothesis states that share prices reflect all relevant information, and that it is impossible to beat the market or achieve above-average returns on a sustainable basis. There are many critics of this theory, such as behavioral economists, who believe in inherent market inefficiencies. The Efficient Market Hypothesis (EMH) states that securities are very efficient in reflecting the truth about a stock. In other words, when new information is available about a stock, it is incorporated into the price adjustments of the stock quickly enough to reflect the actual state of the market (Malkiel, 2003). EHM debunks the idea that financial analysis and forecasting are useful tools to shop for stocks. In other words, because securities accurately reflect the status of that stock, it is not possible to use financial analysis to find an undervalued stock or predict trends in the market in order to buy low with the future insight to sell high. There are three levels, or degrees, of the efficient market hypothesis: weak, semi-strong, and strong. The weak form assumes that current stock prices reflect all available information, and that past price performance has no relationship with the future. In other words, this form of the hypothesis says that using technical analysis to achieve exceptional returns is impossible. The semi-strong form says that stock prices have factored in all available public information. Because of this, it's impossible to use fundamental analysis to choose stocks that will beat the market's returns. Finally, the strong form of the efficient market hypothesis says that all information, public as well as private, is incorporated into current stock prices. This form of the efficient market hypothesis essentially assumes a perfect market, and isn't plausible when there are insider trading restrictions (VanBergen, 2011). Yes, I believe that financial statement analysis can provide a significant advantage to an investor. The key is that empirical analysis, specifically statistical models aimed at forecasting future trends based on historical data, are simply another tool to be taken in to account. They are not a single source of the definitive answer. But, through the use of high power computing and mathematically rigorous modeling tools, forecasting is a powerful tool to provide additional insight to decision makers to include the financial industry. In support of my position, (Malkiel 2003) reports the findings of several empirical studies which demonstrate positive statistical correlations in short-run return analysis and the successful prediction of up to 40% of variation in long-term return analysis through time-series statistical models, which are able to take into account seasonal fluctuations (Malkiel 2003). One experimental study demonstrated with statistical significance that the Ohlson model provides predictive ability for future stock price movements (Lee, Chen and Tsai, 2014).

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ADM 3301 Lecture Notes - Lecture 3: Time Series, Exponential Smoothing, Forecast Error

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