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Res 342 Week 5 DQ 1.

Res 342 Week 5 DQ 1.

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Week 5 DQ 1: What are the components of a time series? What external factors might

affect each of the different components? What is the purpose of forecasting? Why

are forecasts not always correct? What are the limitations of forecasting?

There are four components of time series, trend (T), cycle (C), seasonal (S), and irregular (I).

These components Either Are assumed to follow an additive (Data of similar magnitude (short-

run or trend-free Time-Series data) with constant absolute Growth or decline) or a multiplicative

model (Data of Increasing or Decreasing magnitude (long-run or trend data) constant with

Percent Growth or decline) (Doane & Seward 2007).

Components of time series are:

*Trend-(T) is a general Movement over all years (t = 1, 2,..., N). Might Not Be Trend

very helpful for Predicting space launches by the dates Are steady and predictable.

*Cycle (C) is a repetitive up-and-down around the trend Movement That covers a small number

Several years.Over Periods of time (a typical forecasting situation) or undetectable Are cycles

Apr resemblance to trend.

*Seasonal (S) is a repetitive cyclical pattern Within a year.

*Irregular (I) is a random disturbance Apparent That FOLLOWS no pattern. It is Also Called the

random error or noise component Reflecting all Factors Other Than trend, cycle, and

seasonality. forecast is Referred to as a statistical forecast Mathematical formulas Because it

uses to Identify the patterns and trends while testing the results for reasonableness

Mathematical and confidence. There are two methods which are forescasting Time Series and

Causal models. Sim embargo, Time series has four components that are Trend models,

Decomposition Models, Smoothng Models, and ARIMA Models.

To define a forecast, is Referred to as a statistical forecast Mathematical formulas Because it

uses to Identify the patterns and trends while testing the results for reasonableness and

confidence.Time Mathematical series forecasting is the use of a model to forecast future events

based on past Known events: to predict data points Before They Are Measured. A time series

model will Generally Reflect The Fact That observations close together in time Will Be More

Closely Related Than Further observations apart. Often time series models will make use of the

natural one-way ordering of time so That values for a Given Period Will Be Expressed in Some

Way as deriving from past values, Rather Than from future values. Causal models work best for

Both patterns in variables and the Influence of causal factors. The word itself says "forecast"

this data will not be a true model!

Doane D.P & Seward L.E(2007)Applied Statistics in Business and Economics.

Boston,MA:McGraw-Hill/Irwin

http://www.decisioncraft.com/dmdirect/forecastingtechnique.htm

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Week 5 DQ 1: What are the components of a time series? What external factors might affect each of the different components????What is the purpose of forecasting??Why are forecasts not always correct??What are the limitations of forecasting? There are four components of time series, trend (T), cycle (C), seasonal (S), and irregular (I). These components Either Are assumed to follow an additive (Data of similar magnitude (short-run or trend-free Time-Series data) with constant absolute Growth or decline) or a multiplicative model (Data of Increasing or Decreasing magnitude (long-run or trend d ...
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