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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(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 ô. Then calculate m¡, m2, ..., m, recursively as follows: for t =1, 2, . , n m = My–1 + (1 – 8)et (a) Write a function tsEwma(tsDat, m0=0, delta=0.7) where tsDat is a time series, m0 is the starting value mo and delta is 6. The function should return m1, m2, . , m, 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.

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(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 ô. Then calculate m¡, m2, ..., m, recursively as follows: for t =1, 2, . , n m = My–1 + (1 – 8)et (a) Write a function tsEwma(tsDat, m0=0, delta=0.7) where tsDat is a time series, m0 is the starting value mo and delta is 6. The function should return m1, m2, . , m, 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.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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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(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 d. Then calculate m,, m2, ..., mn recursively as follows: for t = 1, 2, . , n et = Z4 – m–1 mų = M–1 + (1 – 8)et (a) Write a function tsEwma(tsDat, m0=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. (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.
Transcribed Image Text: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(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 d. Then calculate m,, m2, ..., mn recursively as follows: for t = 1, 2, . , n et = Z4 – m–1 mų = M–1 + (1 – 8)et (a) Write a function tsEwma(tsDat, m0=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. (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.
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