A technique used to isolate the trend in a time series is: adjusted factor% seasonal index...

A technique used to isolate the trend in a time series is:

adjusted factor%

seasonal index correction factor

centered moving average

seasonal decomposition

Solutions

Expert Solution

Centered Moving Average

It is the only method among the available options to estimate and eliminate trend.

For your knowledge, brief of Centered Moving Average is given below

In statistics, a moving average (rolling average or running average) is a calculation to analyze data points by creating series of averages of different subsets of the full data set. It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter... 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 value in the subset. A moving average is commonly used with time-series data to smooth out short-term fluctuations and highlight longer-term trends or cycles.

orchestra answered 1 year ago

Next > < Previous

Related Solutions

There are four components to a time series: secular trend, cyclical variation, seasonal variation, and irregular...

There are four components to a time series: secular trend, cyclical variation, seasonal variation, and irregular variation. Research these components and explain the differences as well.

4. Time-series data contain both trend and seasonal variations. Use an example of quarterly data to...

4. Time-series data contain both trend and seasonal variations. Use an example of quarterly data to explain how you would measure the trend and how you would measure the seasonal variation please TYPE your answer and dont finish this question without writing a full page also do not copy the other answers on this site for this question, they are in complete

Q: When time series exhibit a trend the first differences should be used to fit a...

Q: When time series exhibit a trend the first differences should be used to fit a ARIMA model. True or False and Why? Q: Randomized experimental designs are considered the gold standard because the influence of confounders on the treatment effect estimation is limited through the process of random selection into treatment group and control group, so it leads to an unbiased estimation of the treatment effect. True or False and Why?

How can u solve the following question by using the method: Computing trend and seasonal factor...

How can u solve the following question by using the method: Computing trend and seasonal factor from linear regression line (without excel) Question: The following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. sales per quarter: 215, 240, 205, 190, 160, 195, 150, 140

Describe the following terms a) stationary time series b) seasonal time series c) box test (...

Describe the following terms a) stationary time series b) seasonal time series c) box test ( clearly define your notation and the null and alternative hypotheses)

Time series decomposition seeks to separate the time series (Y) into 4 components: trend (T), cycle...

Time series decomposition seeks to separate the time series (Y) into 4 components: trend (T), cycle (C), seasonal (S), and irregular (I). What is the difference between these components? The model can be additive or multiplicative.When we do use an additive model? When do we use a multiplicative model?

Trend or time-series analysis is another term used for ____ analysis Multiple Choice Ratio Horizontal Vertical...

Trend or time-series analysis is another term used for ____ analysis Multiple Choice Ratio Horizontal Vertical Diagonal Vertical analysis refers to: Multiple Choice Expressing each item in a financial statement as a percentage of the same base amount. Expressing each item in a financial statement in order of highest amount to lowest amount. Expressing each item in a financial statement as its trend over time. Expressing each item in a financial statement in order of highest importance to lowest importance....

Part (A): Using R to show how to decompose the seasonal time series data and then...

Part (A): Using R to show how to decompose the seasonal time series data and then subtract that effect from the data. This assignment is to practice using R to learn how to decompose seasonal time series data. Please use the data set in “A Little of R for Time Series” Section 2.4 (p.20): • > births <-scan("http://robjhyndman.com/tsdldata/data/nybirths.dat") > birthstimeseries <- ts(births, frequency=12, start=c(1946,1)) > birthstimeseries • Then you can use the following to show how you can decompose the time...

Two different forecasting techniques, Linear Regression and Trend-Seasonal, were used to forecast demand for cases of...

Two different forecasting techniques, Linear Regression and Trend-Seasonal, were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows. Predicted Demand Period Sales Regression Linear regression Trend-Seasonal 1 282 200 290 2 255 210 230 3 262 220 270 4 290 230 210 5 230 240 250 A) Compute the MAD and Bias for each technique. B) Construct a sentence to describe what the MAD and Bias tell the average...

A time series model is a forecasting technique that attempts to predict the future values of...

A time series model is a forecasting technique that attempts to predict the future values of a variable by using only historical data on that one variable. Here are some examples of variables you can use to forecast. You may use a different source other than the ones listed (be sure to reference the website). There are many other variables you can use, as long as you have values that are recorded at successive intervals of time. Currency price GNP...

Subjects