×

### Let's log you in.

or

Don't have a StudySoup account? Create one here!

×

or

## Forecasting Models

by: tophomework Notetaker

14

0

3

# Forecasting Models

Marketplace > Forecasting Models
tophomework Notetaker
AU
GPA 3.8

Get a free preview of these Notes, just enter your email below.

×
Unlock Preview

Forecasting Models
COURSE
PROF.
No professor available
TYPE
Study Guide
PAGES
3
WORDS
KARMA
50 ?

## Popular in Department

This 3 page Study Guide was uploaded by tophomework Notetaker on Sunday November 15, 2015. The Study Guide belongs to a course at a university taught by a professor in Fall. Since its upload, it has received 14 views.

×

## Reviews for Forecasting Models

×

×

### What is Karma?

#### You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!

Date Created: 11/15/15
"Forecasting Models” Please respond to the following: What is the difference between a causal model and a time­ series model? Give an example  of when each would be used. Casual forecasting methods are subject to the discretion of the forecaster. There are  several informal methods which do not have strict algorithms, but rather modest and unstructured guidance. One can forecast based on, for example, linear relationships. If one variable is linearly  related to the other for a long enough period of time, it may be beneficial to predict such a  relationship in the future. This is quite different from the aforementioned model of seasonality  whose graph would more closely resemble a sine or cosine wave. The most important factor  when performing this operation is using concrete and substantiated data. Forecasting off of  another forecast produces inconclusive and possibly erroneous results. Some forecasting methods use the assumption that it is possible to identify the underlying factors that might influence the variable that is being forecast. For example, including  information about weather conditions might improve the ability of a model to predict umbrella  sales. This is a model of seasonality which shows a regular pattern of up and down fluctuations.  In addition to weather, seasonality can also be due to holidays and customs such as predicting  that sales in college football apparel will be higher during football season as opposed to the off  season. Time series forecasting is the use of a model to predict future values based on previously  observed values.  Time series data have a natural temporal ordering. This makes time series analysis  distinct from other common data analysis problems, in which there is no natural ordering of the  observations (e.g. explaining people's wages by reference to their respective education levels,  where the individuals' data could be entered in any order). A stochastic model for a time series  will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural  one­way ordering of time so that values for a given period will be expressed as deriving in some  way from past values, rather than from future values. Time series are very frequently plotted via  line charts. Wikipedia. (n.d.). Retrieved from http://en.wikipedia.org/wiki/Forecasting  Time series. (08oc). Retrieved from http://en.wikipedia.org/wiki/Time_series  What are some of the problems and drawbacks of the moving average forecasting model? A moving average forecast model is based on an artificially constructed time series in  which the value for a given time period is replaced by the mean of that value and the values for  some number of preceding and succeeding time periods. As you may have guessed from the  description, this model is best suited to time­series data; i.e. data that changes over time. For  example, many charts of individual stocks on the stock market show 20, 50, 100 or 200 day  moving averages as a way to show trends.  One of the drawbacks to this model is forecast value for any given period is an average of the previous periods, then the forecast will always appear to "lag" behind either increases or  decreases in the observed (dependent) values. For example, if a data series has a noticeable  upward trend then a moving average forecast will generally provide an underestimate of the  values of the dependent variable.  The moving average method has an advantage over other forecasting models in that it  does smooth out peaks and troughs (or valleys) in a set of observations. However, it also has  several disadvantages. In particular this model does not produce an actual equation. Therefore, it  is not all that useful as a medium­long range forecasting tool. It can only reliably be used to  forecast one or two periods into the future.  Gould, S. (2011). Class movingaveragemodel. Retrieved from  http://openforecast.sourceforge.net/docs/net/sourceforge/openforecast/models/MovingAv erageModel.html  How do you determine how many observations to average in a moving average model?  A moving average is a set of numbers, each of which is the average of the corresponding  subset of a larger set of datum points. Given a series of numbers and a fixed subset size, the first  element of the moving average is obtained by taking the average of the initial fixed subset of the  number series. Then the subset is modified by "shifting forward", that is excluding the first  number of the series and including the next number following the original subset in the series.  This creates a new subset of numbers, which is averaged. This process is repeated over the entire data series. The plot line connecting all the (fixed) averages is the moving average. A moving  average may also use unequal weights for each datum value in the subset to emphasize particular values in the subset. Wikipedia. (2011). Retrieved from http://en.wikipedia.org/wiki/Moving_average  How do you determine the weightings to use in a weighted moving average model? A weighted average is any average that has multiplying factors to give different weights to data  at different positions in the sample window. Mathematically, the moving average is the  convolution of the datum points with a fixed weighting function. One application is removing  pixelisation from a digital graphical image. In technical analysis of financial data, a weighted moving average (WMA) has the specific  meaning of weights that decrease in arithmetical progression. In an n­day WMA the latest day  has weight n, the second latest n − 1, etc., down to one. WMA weights n = 15 The denominator is a triangle number equal to  In the more general case the  denominator will always be the sum of the individual weights. When calculating the WMA across successive values, the difference between the numerators of  WMA M+1 and WMA  iM np M+1 − pM − ... −M−n+1 If we denote the sum M  + ... +M−n+1by TotalM,  then The graph above shows how the weights decrease, from highest weight for the most recent  datum points, down to zero. It can be compared to the weights in the exponential moving average which follows. Wikipedia. (2011). Retrieved from http://en.wikipedia.org/wiki/Moving_average

×

×

### BOOM! Enjoy Your Free Notes!

×

Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'

## Why people love StudySoup

Jim McGreen Ohio University

#### "Knowing I can count on the Elite Notetaker in my class allows me to focus on what the professor is saying instead of just scribbling notes the whole time and falling behind."

Kyle Maynard Purdue

#### "When you're taking detailed notes and trying to help everyone else out in the class, it really helps you learn and understand the material...plus I made \$280 on my first study guide!"

Steve Martinelli UC Los Angeles

#### "There's no way I would have passed my Organic Chemistry class this semester without the notes and study guides I got from StudySoup."

Parker Thompson 500 Startups

#### "It's a great way for students to improve their educational experience and it seemed like a product that everybody wants, so all the people participating are winning."

Become an Elite Notetaker and start selling your notes online!
×

### Refund Policy

#### STUDYSOUP CANCELLATION POLICY

All subscriptions to StudySoup are paid in full at the time of subscribing. To change your credit card information or to cancel your subscription, go to "Edit Settings". All credit card information will be available there. If you should decide to cancel your subscription, it will continue to be valid until the next payment period, as all payments for the current period were made in advance. For special circumstances, please email support@studysoup.com

#### STUDYSOUP REFUND POLICY

StudySoup has more than 1 million course-specific study resources to help students study smarter. If you’re having trouble finding what you’re looking for, our customer support team can help you find what you need! Feel free to contact them here: support@studysoup.com

Recurring Subscriptions: If you have canceled your recurring subscription on the day of renewal and have not downloaded any documents, you may request a refund by submitting an email to support@studysoup.com