How will you detrend a time series?

1.	How will you detrend a time series?
Answer» Linear regression can be used to model the Time Series data with linear indices (Ex: 1, 2,...N). The resulting model’s residuals are a representation of the time series devoid of the TREND. In case, if some trend is left over to be seen in the residuals (like what it seems to be with ‘Figure1’ with MYDATA below as an EXAMPLE), then you MIGHT wish to add few predictors to the lm() call (like a forecast:: seasonal dummy, forecast::Fourier or may be a lag of the series itself), until the trend is filtered. Code snippet: trModel <- lm(myData ~ c(1:length(myData))) plot(resid(trModel), type="l") # resid(trModel) contains the de-trended series

Answer»

Linear regression can be used to model the Time Series data with linear indices (Ex: 1, 2,...N). The resulting model’s residuals are a representation of the time series devoid of the TREND.

In case, if some trend is left over to be seen in the residuals (like what it seems to be with ‘Figure1’ with MYDATA below as an EXAMPLE), then you MIGHT wish to add few predictors to the lm() call (like a forecast:: seasonal dummy, forecast::Fourier or may be a lag of the series itself), until the trend is filtered.

Code snippet:

trModel <- lm(myData ~ c(1:length(myData))) plot(resid(trModel), type="l") # resid(trModel) contains the de-trended series

Discussion

No Comment Found

Related InterviewSolutions

We have below data with 10 transactions. What is the “Lift Ratio” for “if white then blue”?
Transactions#FaceplateColorsPurchased1redwhitegreen2whiteorange3whiteblue4redwhiteorange5redblue6whiteblue7whiteorange8redwhitebluegreen9redwhiteblue10yellow
We have below data with 10 transactions. What is the performance measure “Confidence” for “if white then blue”?
Transaction#FaceplateColorsPurchased1redwhitegreen2whiteorange3whiteblue4redwhiteorange5redblue6whiteblue7whiteorange8redwhitebluegreen9redwhiteblue10yellow
We have below data with 10 transactions. What is the performance measure “Support” for “if white then blue”?
UnivSATTop10AcceptSFRatioExpensesGradRateBrown131089221322,70494CalTech141510025663,57581CMU12606259925,02672Columbia131076241231,51088Comell128083331321,86490Dartmouth134089231032,16295Duke131590301231,58595Georgetown125574241220,12692Harvard140091141139,52597JohnHopkins13057544758,69187MIT138094301034,87091Northwestern126085391128,05289NotreDame125581421315,12294PennState108138541810,18580Priceton13759114830,22095Purdue10052890199,06669Stanford136090201236,45093TexasA&M10754967258,70467UCBerkeley124095401715,14078UChicago129075501338,38087UMichigan118065681615,47085UPenn128580361127,55390UVA122577441413,34992UWisconsin108540691511,85771Yale137595191143,51496
SATAverageSAT score of new freshmenTop10% new freshmen in top 10% of highschool classAccept% of applicants accepted SFRatioStudent to faculty ratioSExpensesEstimated annual expensesGradRateGraduation Rate(%)
Consider universities dataset below. Data for 25 undergraduate programs at business schools in US universities in 1995. The dataset excludes image variables (student satisfaction, employer satisfaction, dean’s opinions, etc.). Given this
We have trained/executed our model with the given dataset. We have noticed that we have used a regression model and it is suffering from multicollinearity. Is it possible to improvise on our model without losing any information?
When is Ridge regression used and when is Lasso regression (ideally)?