Time Series. The code ts (datVec, start%=c(1960,3), frequency-12) creates a time series with monthly observations (frequency-12), with first observation in March 1960 (start%3Dc(1960,3)) and with values specified in the vector datVec. Suppose z1, 22,..., zn is a time series. Then we define the exponentially weighted moving average of this time series as follows: select a starting value mo and select a discount factor 8. Then calculate m1, m2, ..., mn recursively as follows: for t = 1, 2, ...,n et = zt - mt-1 m = m-1 +(1 – 8)e (a) Write a function tsEwma (tsDat, mo=0, delta=0.7) where tsDat is a time series, mo is the starting value mo and delta is 6. The function should return m1, m2,..., m, in the form of a time series. %3D (b) In general, looping over named objects is much slower than looping over objects which do not have names. This principle also applies to time series: looping over a vector is much quicker than looping over a time series. Use this observation to improve the execution speed of your function which should still return a time series. Investigate the difference in speed between the functions in parts (a) and (b) by using the function system.time.
Time Series. The code ts (datVec, start%=c(1960,3), frequency-12) creates a time series with monthly observations (frequency-12), with first observation in March 1960 (start%3Dc(1960,3)) and with values specified in the vector datVec. Suppose z1, 22,..., zn is a time series. Then we define the exponentially weighted moving average of this time series as follows: select a starting value mo and select a discount factor 8. Then calculate m1, m2, ..., mn recursively as follows: for t = 1, 2, ...,n et = zt - mt-1 m = m-1 +(1 – 8)e (a) Write a function tsEwma (tsDat, mo=0, delta=0.7) where tsDat is a time series, mo is the starting value mo and delta is 6. The function should return m1, m2,..., m, in the form of a time series. %3D (b) In general, looping over named objects is much slower than looping over objects which do not have names. This principle also applies to time series: looping over a vector is much quicker than looping over a time series. Use this observation to improve the execution speed of your function which should still return a time series. Investigate the difference in speed between the functions in parts (a) and (b) by using the function system.time.
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
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Chapter1: Computer Networks And The Internet
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Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Transcribed Image Text:1. Time Series. The code
ts (datVec, start=c(1960,3), frequency-12)
creates a time series with monthly observations (frequency=12), with first observation in March 1960
(start=c(1960o,3)) and with values specified in the vector datVec.
Suppose z1, 22, ..., zn is a time series. Then we define the exponentially weighted moving average of this
time series as follows: select a starting value mo and select a discount factor 8. Then calculate m1, m2,
..., mn recursively as follows: for t = 1, 2, ..., n
et = Zt - mt-1
my = m-1 + (1– 8)e
(a) Write a function tsEwma(tsDat, mo=0, delta=0.7) where tsDat is a time series, mo is the starting
value mo and delta is 8. The function should return m1, m2,..., mn in the form of a time series.
(b) In general, looping over named objects is much slower than looping over objects which do not have
names. This principle also applies to time series: looping over a vector is much quicker than looping
over a time series. Use this observation to improve the execution speed of your function which should
still return a time series. Investigate the difference in speed between the functions in parts (a) and (b)
by using the function system.time.
(o) Write a fungtion colled
which tol
mante the following
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