Business Forecasting Topic 9 Flashcards

1
Q

non stationary

A

trend
- ACF dying out extremely slowly -> when data manifests a trend/drift observations tend to be on same side of mean and therefore large autocorrelations still occur at relatively long time lags

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2
Q

differencing series to achieve stationarity

A

differences normally from random series = not constant
sure differences are stationary = model them using one of standard box Jenkins model

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3
Q

first difference

A

differences in sales between successive months
observation lost at start of series

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4
Q

random walk model

A

ARIMA (0,1,0)
first differences of random walk series follow white noise process

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5
Q

ACF of random walk model

A

ACF - indicates non stationarity
take differences = new series is stationary characterised by white noise process -> though strictly such a process requires normal distribution of differences

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6
Q

ARIMA (1,1,0)

A

first differences follow first order autoregressive process
difference between observation for period 2 and 3 is function of difference between periods 1 and 2

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7
Q

ARIMA (0,1,1)

A

first differences followed by a first order moving average model

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8
Q

backshift operator (B)

A

placing in front of observation = has effect of lagging it by 1 period
BY1 = Yt-1

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9
Q

over differencing

A

more differences than required = should avoid
use of first differences in time series analysis = common (sometimes second)

artificially introduce moving average terms into model of differentiated time series = cause unnecessary complications

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10
Q

relationship with exponential smoothing models

A

shown forecasts obtained from exponential smoothing methods are same as those obtained from certain ARIMA models

Simple exponential smoothing forecasts with something constant of α -> ARIMA (0,1,1) forecasts where theta 1 = (1 - α)

holts method = ARIMA (0,2,2)

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