Damped trend method
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Damped trend method
According to the article, there are a number of methods used in making forecasts
decisions (Hyndman, 2014). The article delves on damped trend methods and compares it with
other methods used to generate forecasts such as Holts’ linear method. One noticeable feature or
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difference between the damped trend methods and the Holt’s linear method is that Holt’s method
as it displays constant changes that is either increasing or decreasing indefinitely into the future
(Hyndman, 2014). On the other hand, the dampened trend methods usually dampen the fault line
in the future which in one of the differences between the methods compared to others. This
dampening has proven successful hence many people like the method especially when they want
automatic forecasts for many series (Kauczor, Norman, Christiansen & Coriani, 2013).
Other forecasts methods the article highlights include Exponential, SES, additive and
multiplicative. These methods have same characteristics as Holt’s linear method because they
lead to extreme results either increase or decreases in their future (Phojanamongkolkij, Kato,
Wielicki, Taylor & Mlynczak, 2014).
These methods have their advantages and disadvantages hence, used in various situations
as deemed appropriate. For instance, Holt’s linear trend method provides best fit to the data
presented then followed by additive linear trend method. On the other hand, simple exponential
smoothing takes the lead in generating largest within sample one-step errors (Hyndman, 2014).
The article provides in-depth analysis of these methods. They are compared to provide
insights on the most accurate method in forecasts which enhances understanding of their
suitability. Therefore, through the articles, in-depth knowledge and understanding of these
methods is provided.
The article is clear and precise. Citation and organization is up to standard hence
enhancing understanding. The information and arguments raised are well supported with
credible references making it more credible and persuasive. The article as well has graphs and
tables that illustrate the theoretical information hence menacing understanding.
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References
Hyndman, R. (2014). Forecasting: Principles and practice.
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Kauczor, J., Norman, P., Christiansen, O., & Coriani, S. (2013). Communication: A reduced-
space algorithm for the solution of the complex linear response equations used
in coupled cluster damped response theory. Journal of Chemical Physics,
139(21):211102-211102-4.
Phojanamongkolkij, N., Kato, S., Wielicki, B.,Taylor, P., & Mlynczak, M. (2014). A
Comparison of Climate Signal Trend Detection Uncertainty Analysis Methods.
Journal of Climate, 27( 9):3363-3376.
