Order ID |
53563633773 |
Type |
Essay |
Writer Level |
Masters |
Style |
APA |
Sources/References |
4 |
Perfect Number of Pages to Order |
5-10 Pages |
Description/Paper Instructions
ANLY620 Analysis of Exponential Smoothing Paper
ANLY620, Analysis, Exponential, Smoothing, Paper
150 words to each question
Q1. Exponential Smoothing
Exponential smoothing is a time series forecasting method. There are three main types, simple, double, and triple exponential smoothing (Brownlee, 2020). In exponential smoothing models are weighted sums of past observations where older data weights less than newer one.
Figure 1. Exponential Smoothing. (Vandeput, 2019).
In Figure 1 we can observer the blue, jagged line, representing demand over a period. An untrained eye can pick the upward trend, however, if we were to shrink the time interval this could be harder to notice.
Additionally, there are data sets which true patterns are harder to understand. With exponential smoothing, represented by the orange line in Figure 1, an analyst can see the underlying trend. In figure 1 this trend is upwards, signifying that the demand has increased over time.
As mentioned before, exponential smoothing can account for seasonality. According to Brownlee (2020), to account for this seasonality trends analysts must employee the following variables: alpha, beta, trend type, demand type, and phi. Alpha is the smoothing factor for the level. Beta is the smoothing factor for the trend. Trend type can be additive or multiplicative. Dampen type can be either additive or multiplicative. Finally, phi, the damping coefficient.
According to Brownlee (2020), other variables can also say the analyst produce a more accurate model. These include gamma and the period. Gamma is a smoothing factor for the seasonality. The period is time steps in the season.
Understanding these variables is crucial in the planning and creating of an exponential smoothing model. Although the arithmetic calculations can be rigorous, data analysts rely on the use of statistical software’s perform later.
Bibliography
Brownlee, J. (2020, April 12). A Gentle Introduction to Exponential Smoothing for Time Series Forecasting in Python. Retrieved from Machine Learning Mastery: https://machinelearningmastery.com/exponential-smoothing-for-time-series-forecasting-in-python/
Vandeput, N. (2019, Novemeber 12). Simple Exponential Smoothing for Time Series Forecasting. Retrieved from Towards Data Science: https://towardsdatascience.com/simple-exponential-smoothing-749fc5631bed
Q2.K-mean (k-mean) clustering is a technique of plotting specific items that show correlation within groups or clusters and measuring the distance from a specific center point of the cluster. The center point is referred to as a centroid(s). Clustering is an unsupervised technique to group items or objects that have similar attributes (EMC Education Services, 2015, p. 153).
These clusters generally identify hidden attributes to data that may not be normally viewed during data manipulation activities. While developing k-means, the first step is to estimate the centroids, in an attempt to locate the center or best fit (this may be hard to locate and may not be perfect in the end). The clusters with centroids are calculated for best fit, and recalculated to make sure.
One of the best examples about k-means clustering is that of grocery store design. From information from shoppers and the habits in buying like items (linked) clusters in data can be visualized.
Once visualized a grocery store has two decisions to make cluster like items together so that consumers buy items in clusters or they may try to design a store that links many clusters together so that consumers buy items from different clusters as well as items they may not need but they associate them together with the item that was needed.
Example one: A store that lumps the milk isle, cereal aisle, and cookie isle all together because these three items may be frequently purchased together.
Example two: A store that alternates clusters: milk isle, canned soup isle, cereal aisle, bread isle, cookie isle, baking isle. So that different cluster are hit on each isle, but alternating clusters so more product is bought.
These examples come from two different stores locally in Fairbanks, AK.
The information collected from shopper habits has been instrument in the development of grocery stores and how they are laid out. These strategies also allow for room for season items normally found at the main entrance or exit. Where again “Fall” items are clustered together to promote multiple item purchases.
References
EMC Education Services. (2015). Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data. Indianapolis, IN: John Wiley & Sons, Inc.
RUBRIC
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30 points out of 50: The essay illustrates a rudimentary understanding of the relevant material by mentioning but not full explaining the relevant content; identifying some of the key concepts/ideas though failing to fully or accurately explain many of them; using terminology, though sometimes inaccurately or inappropriately; and/or incorporating some key claims/points but failing to explain the reasoning behind them or doing so inaccurately. Elements of the required response may also be lacking. |
40 points out of 50: The essay illustrates solid understanding of the relevant material by correctly addressing most of the relevant content; identifying and explaining most of the key concepts/ideas; using correct terminology; explaining the reasoning behind most of the key points/claims; and/or where necessary or useful, substantiating some points with accurate examples. The answer is complete. |
50 points: The essay illustrates exemplary understanding of the relevant material by thoroughly and correctly addressing the relevant content; identifying and explaining all of the key concepts/ideas; using correct terminology explaining the reasoning behind key points/claims and substantiating, as necessary/useful, points with several accurate and illuminating examples. No aspects of the required answer are missing. |
Use of Sources (worth a maximum of 20% of the total points). |
Zero points: Student failed to include citations and/or references. Or the student failed to submit a final paper. |
5 out 20 points: Sources are seldom cited to support statements and/or format of citations are not recognizable as APA 6th Edition format. There are major errors in the formation of the references and citations. And/or there is a major reliance on highly questionable. The Student fails to provide an adequate synthesis of research collected for the paper. |
10 out 20 points: References to scholarly sources are occasionally given; many statements seem unsubstantiated. Frequent errors in APA 6th Edition format, leaving the reader confused about the source of the information. There are significant errors of the formation in the references and citations. And/or there is a significant use of highly questionable sources. |
15 out 20 points: Credible Scholarly sources are used effectively support claims and are, for the most part, clear and fairly represented. APA 6th Edition is used with only a few minor errors. There are minor errors in reference and/or citations. And/or there is some use of questionable sources. |
20 points: Credible scholarly sources are used to give compelling evidence to support claims and are clearly and fairly represented. APA 6th Edition format is used accurately and consistently. The student uses above the maximum required references in the development of the assignment. |
Grammar (worth maximum of 20% of total points) |
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20 points: The paper is clear, concise, and a pleasure to read as a result of appropriate and precise use of terminology; total coherence of thoughts and presentation and logical organization; and the essay is error free. |
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7 points out of 10: Research paper presents an above-average use of formatting skills. The paper has slight errors within the paper. This can include small errors or omissions with the cover page, abstract, page number, and headers. There could be also slight formatting issues with the document spacing or the font Additionally the paper might slightly exceed or undershoot the specific number of required written pages for the assignment. |
10 points: Student provides a high-caliber, formatted paper. This includes an APA 6th edition cover page, abstract, page number, headers and is double spaced in 12’ Times Roman Font. Additionally, the paper conforms to the specific number of required written pages and neither goes over or under the specified length of the paper. |
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