I have questions on how to do this in R/Rstudio or smoothing methods. I have bee
ID: 2906569 • Letter: I
Question
I have questions on how to do this in R/Rstudio or smoothing methods. I have been struggling with using the codes. I have been practicing and tryign to do it, but I am really unsure at where I am with it. Just need something to compare it too! I don't understand why there isn't any information under the demand section!
(1) A memo written to the Chief Operations Officer of Transantiago that describes the model and method and presents the 3-day forecast (March 22, 23 and 24) at 15-minute intervals in as non-technical language as possible.
(2) A technical appendix to your memo that includes:
the name of the model/combination of model used
a description of the method or combination
all estimated equations, etc. associated with constructing the forecasts
the MAPE and MAE for the training period and the validation period
forecasts for the future period (March 22-24) at 15-minute intervals
a graph showing the fit of the final model for the entire period (including training, validation and future), fitted using the combined training and validation data
any additional information that would be useful in understanding or recreating your forecast
a list of any references consulting in preparing your answer
DATE TIME DEMAND 22-Mar-05 6:30 22-Mar-05 6:45 22-Mar-05 7:00 22-Mar-05 7:15 22-Mar-05 7:30 22-Mar-05 7:45 22-Mar-05 8:00 22-Mar-05 8:15 22-Mar-05 8:30 22-Mar-05 8:45 22-Mar-05 9:00 22-Mar-05 9:15 22-Mar-05 9:30 22-Mar-05 9:45 22-Mar-05 10:00 22-Mar-05 10:15 22-Mar-05 10:30 22-Mar-05 10:45 22-Mar-05 11:00 22-Mar-05 11:15 22-Mar-05 11:30 22-Mar-05 11:45 22-Mar-05 12:00 22-Mar-05 12:15 22-Mar-05 12:30 22-Mar-05 12:45 22-Mar-05 13:00 22-Mar-05 13:15 22-Mar-05 13:30 22-Mar-05 13:45 22-Mar-05 14:00 22-Mar-05 14:15 22-Mar-05 14:30 22-Mar-05 14:45 22-Mar-05 15:00 22-Mar-05 15:15 22-Mar-05 15:30 22-Mar-05 15:45 22-Mar-05 16:00 22-Mar-05 16:15 22-Mar-05 16:30 22-Mar-05 16:45 22-Mar-05 17:00 22-Mar-05 17:15 22-Mar-05 17:30 22-Mar-05 17:45 22-Mar-05 18:00 22-Mar-05 18:15 22-Mar-05 18:30 22-Mar-05 18:45 22-Mar-05 19:00 22-Mar-05 19:15 22-Mar-05 19:30 22-Mar-05 19:45 22-Mar-05 20:00 22-Mar-05 20:15 22-Mar-05 20:30 22-Mar-05 20:45 22-Mar-05 21:00 22-Mar-05 21:15 22-Mar-05 21:30 22-Mar-05 21:45 22-Mar-05 22:00 23-Mar-05 6:30 23-Mar-05 6:45 23-Mar-05 7:00 23-Mar-05 7:15 23-Mar-05 7:30 23-Mar-05 7:45 23-Mar-05 8:00 23-Mar-05 8:15 23-Mar-05 8:30 23-Mar-05 8:45 23-Mar-05 9:00 23-Mar-05 9:15 23-Mar-05 9:30 23-Mar-05 9:45 23-Mar-05 10:00 23-Mar-05 10:15 23-Mar-05 10:30 23-Mar-05 10:45 23-Mar-05 11:00 23-Mar-05 11:15 23-Mar-05 11:30 23-Mar-05 11:45 23-Mar-05 12:00 23-Mar-05 12:15 23-Mar-05 12:30 23-Mar-05 12:45 23-Mar-05 13:00 23-Mar-05 13:15 23-Mar-05 13:30 23-Mar-05 13:45 23-Mar-05 14:00 23-Mar-05 14:15 23-Mar-05 14:30 23-Mar-05 14:45 23-Mar-05 15:00 23-Mar-05 15:15 23-Mar-05 15:30 23-Mar-05 15:45 23-Mar-05 16:00 23-Mar-05 16:15 23-Mar-05 16:30 23-Mar-05 16:45 23-Mar-05 17:00 23-Mar-05 17:15 23-Mar-05 17:30 23-Mar-05 17:45 23-Mar-05 18:00 23-Mar-05 18:15 23-Mar-05 18:30 23-Mar-05 18:45 23-Mar-05 19:00 23-Mar-05 19:15 23-Mar-05 19:30 23-Mar-05 19:45 23-Mar-05 20:00 23-Mar-05 20:15 23-Mar-05 20:30 23-Mar-05 20:45 23-Mar-05 21:00 23-Mar-05 21:15 23-Mar-05 21:30 23-Mar-05 21:45 23-Mar-05 22:00 24-Mar-05 6:30 24-Mar-05 6:45 24-Mar-05 7:00 24-Mar-05 7:15 24-Mar-05 7:30 24-Mar-05 7:45 24-Mar-05 8:00 24-Mar-05 8:15 24-Mar-05 8:30 24-Mar-05 8:45 24-Mar-05 9:00 24-Mar-05 9:15 24-Mar-05 9:30 24-Mar-05 9:45 24-Mar-05 10:00 24-Mar-05 10:15 24-Mar-05 10:30 24-Mar-05 10:45 24-Mar-05 11:00 24-Mar-05 11:15 24-Mar-05 11:30 24-Mar-05 11:45 24-Mar-05 12:00 24-Mar-05 12:15 24-Mar-05 12:30 24-Mar-05 12:45 24-Mar-05 13:00 24-Mar-05 13:15 24-Mar-05 13:30 24-Mar-05 13:45 24-Mar-05 14:00 24-Mar-05 14:15 24-Mar-05 14:30 24-Mar-05 14:45 24-Mar-05 15:00 24-Mar-05 15:15 24-Mar-05 15:30 24-Mar-05 15:45 24-Mar-05 16:00 24-Mar-05 16:15 24-Mar-05 16:30 24-Mar-05 16:45 24-Mar-05 17:00 24-Mar-05 17:15 24-Mar-05 17:30 24-Mar-05 17:45 24-Mar-05 18:00 24-Mar-05 18:15 24-Mar-05 18:30 24-Mar-05 18:45 24-Mar-05 19:00 24-Mar-05 19:15 24-Mar-05 19:30 24-Mar-05 19:45 24-Mar-05 20:00 24-Mar-05 20:15 24-Mar-05 20:30 24-Mar-05 20:45 24-Mar-05 21:00 24-Mar-05 21:15 24-Mar-05 21:30 24-Mar-05 21:45 24-Mar-05 22:00Explanation / Answer
The data that you have provided is insufficient, you need to have the demand column fully populated with numbers then only you can go ahead.
And the problem is from the concept called time series analysis.
As in your case "demand" is the dependent variable so you need to have some prior information about it then only you can go ahead with further analysis or you can say forecasting the demand for future.
I am giving you the R-Code for time series analysis.
Just run that code once you get your demand column populated with "numbers".
R-Code:
#Checking presence of trend:
install.packages("devtools")
library(devtools)
#installing trendseason package from github
install_github("abhishekumrawal/trendseason")
library(trendseason)
plot(con_exp, type= "l")
ro.test(con_exp$Consumption..C.)
#Fitting linear trend
model <- lm(con_exp$Consumption..C.~ con_exp$Year, data = con_exp)
abline(lm(con_exp$Consumption..C.~ con_exp$Year, data = con_exp))
model <- lm(con_exp$Consumption..C.~con_exp$Year, data = con_exp)
abline(a = model$coefficients[1], b = model$coefficients[2])
model$coefficients
LSE_trend <- model$fitted.values
#Detrending
detrended <- con_exp$Consumption..C. - LSE_trend
plot(detrended, type = "l")
#Forecasting
X <- c(2001,2002,2003,2004,2005)
model_forecast<- model$coefficients[1] + model$coefficients[2]*X
model_forecast
#2.
case2 <- read.csv("case2.csv")
#i. trending Gross National Income (GNI) per capita based on Purchasing
#Power Parity (PPP) Exchange Rates (ER) measured in current USD
#testing presence of trend:
plot(case2$X.GNI.per.capita..PPP..current.international...., type = "l")
ro.test(case2$X.GNI.per.capita..PPP..current.international....)
#Fitting linear trend:
model <- lm(case2$X.GNI.per.capita..PPP..current.international....
~as.ts(case2$Series.Name), data = case2)
abline(a = model$coefficients[1], b = model$coefficients[2])
LSE_trend <- model$fitted.values
LSE_trend
#Detrending
detrended <- case2$X.GNI.per.capita..PPP..current.international.... - model$fitted.values
detrended2 <- case2$X.GNI.per.capita..PPP..current.international.... - LSE_trend
plot(detrended2, type = "l")
#ii. Population Total
#testing presence of trend:
ro.test(case2$X.Population..total.)
plot(case2$X.Population..total. , type = "l")
#Fitting linear trend:
model <- lm(case2$X.Population..total.
~as.ts(case2$Series.Name), data = case2)
abline(a = model$coefficients[1], b = model$coefficients[2])
LSE_trend <- model$fitted.values
LSE_trend
#Detrending
detrended <- case2$X.Population..total. - model$fitted.values
plot(detrended, type = "l")
#iii. Gross Domestic Product (GDP) (current USD)
#testing presence of trend:
ro.test(case2$GDP..current.US..)
plot(case2$GDP..current.US.. , type = "l")
#Fitting linear trend:
model <- lm(case2$GDP..current.US..
~as.ts(case2$Series.Name), data = case2)
abline(a = model$coefficients[1], b = model$coefficients[2])
LSE_trend <- model$fitted.values
LSE_trend
#Detrending
detrended <- case2$GDP..current.US.. - model$fitted.values
plot(detrended, type = "l")
#iv. iv. Gross Domestic Product (GDP) Growth (annual %)
#testing presence of trend:
ro.test(case2$GDP.growth..annual...)
plot(case2$GDP.growth..annual... , type = "l")
#v.v. Life Expectancy at birth (years)
#testing presence of trend:
case2$X.Life.expectancy.at.birth..total..years..[9] <- NA
y <- as.vector(case2[c(1:8),6])
ro.test(y)
plot(y, type = "l")
#Fitting linear trend:
model <- lm(y ~as.ts(case2[c(1:8),1]), data = case2)
model$coefficients
abline(a = model$coefficients[1], b = model$coefficients[2])
LSE_trend <- model$fitted.values
LSE_trend
#Detrending
detrended <- c(64,64,65,65,65,66,66,66) - LSE_trend
plot(detrended, type = "l")
#3.
#reading data
case3 <- read.csv("case3.csv")
#testing for the presence of trend
ro.test(case3$Sales..in.million.dollars.)
plot(case3, type="l")
plot(MA_trend, type = "l")
#trend estimation using moving average
library(TTR)
MA_trend<-SMA(case3$Sales..in.million.dollars.,5)
?SMA
#detrending
detrended <- case3$Sales..in.million.dollars.- MA_trend
plot(detrended, type = "l")
#forecasting
require(forecast)
plot(forecast(case3$Sales..in.million.dollars., h = 3))
#4.
case4 <- read.csv("case4,5,7.csv")
y <- ts(case4$No..of.Airline.Passangers.in.Thousands,
start = c(1949,1), end = c(1960,12), frequency = 12)
#testing presence of trend
ro.test(y)
plot(y, type = "l")
#Fitting
MA_trend<-SMA(y, 12)
#detrending
detrended <- y- MA_trend
plot(detrended, type = "l")
#Seasonality
library(trendseason)
friedman.test(detrended,12)
#decomposition using OLSE
month <- seasonaldummy(y)
LM <- lm(y~time(y)+month)
plot(LM$fitted.values, type = "l")
plot(forecast(LM$fitted.values, h=5))
#5.
case5 <- read.csv("case4,5,7.csv")
y <- ts(case5$No..of.Airline.Passangers.in.Thousands,
start = c(1949,1), end = c(1960,12), frequency = 12)
#testing presence of trend
ro.test(y)
plot(y, type = "l")
#Fitting
MA_trend<-SMA(y, 12)
#detrending
detrended <- y- MA_trend
plot(detrended, type = "l")
#Seasonality
#library(trendseason)
friedman.test(detrended,12)
#decomposition
decomp <- stl(y, s.window = 12, t.window = 12)
plot(decomp)
#forecasting
#require(forecast)
plot(forecast(decomp, h = 5))
#6.
case6 <- read.csv("case8.csv")
y <- ts(case6$Demand..in.thousand.units.,
start = c(2001,1), end = c(2005,4), frequency = 4)
#testing presence of trend
ro.test(y)
plot(y, type = "l")
#Seasonality
#library(trendseason)
friedman.test(detrended,12)
#decomposition
decomp <- stl(y, s.window = 12, t.window = 12)
plot(decomp)
#forecasting
#require(forecast)
plot(forecast(decomp, h = 2))
#7.
case7 <- read.csv("case4,5,7.csv")
y <- ts(case7$No..of.Airline.Passangers.in.Thousands,
start = c(1949,1), end = c(1960,12), frequency = 12)
#testing presence of trend
ro.test(y)
plot(y, type = "l")
#Fitting
MA_trend<-SMA(y, 12)
#detrending
detrended <- y- MA_trend
plot(detrended, type = "l")
#Seasonality
#library(trendseason)
friedman.test(detrended,12)
#decomposition
decomp <- decompose(y, type = "additive")
plot(decomp)
#forecasting
#require(forecast)
plot(forecast(decomp$x, h = 5))
#8.
case8 <- read.csv("case6.csv")
y <- ts(case8[c(116:1),2],
start = c(2005,4), end = c(2014,11), frequency = 12)
#testing presence of trend
ro.test(y)
plot(y, type = "l")
#Seasonality
#library(trendseason)
friedman.test(detrended,12)
#decomposition
decomp <- decompose(y, type = "additive")
plot(decomp)
#forecasting
#require(forecast)
plot(forecast(decomp$x, h = 5))
Just change my variables to your variables once you get the data !!!