Time Series Sample¶

In [1]:

# DAL ToolBox
# version 1.0.777

source("https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R")

#loading DAL
load_library("daltoolbox")

Loading required package: daltoolbox

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 


Attaching package: ‘daltoolbox’


The following object is masked from ‘package:base’:

    transform

Series for studying¶

In [2]:

data(sin_data)

In [3]:

library(ggplot2)
plot_ts(x=sin_data$x, y=sin_data$y) + theme(text = element_text(size=16))

sliding windows¶

In [4]:

sw_size <- 10
ts <- ts_data(sin_data$y, sw_size)
ts_head(ts, 3)

A matrix: 3 × 10 of type dbl
t9	t8	t7	t6	t5	t4	t3	t2	t1	t0
0.0000000	0.2474040	0.4794255	0.6816388	0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732
0.2474040	0.4794255	0.6816388	0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732	0.5984721
0.4794255	0.6816388	0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732	0.5984721	0.3816610

data sampling¶

In [5]:

test_size <- 3
samp <- ts_sample(ts, test_size)

In [6]:

#first five rows from training data
ts_head(samp$train, 5)

A matrix: 5 × 10 of type dbl
t9	t8	t7	t6	t5	t4	t3	t2	t1	t0
0.0000000	0.2474040	0.4794255	0.6816388	0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732
0.2474040	0.4794255	0.6816388	0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732	0.5984721
0.4794255	0.6816388	0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732	0.5984721	0.3816610
0.6816388	0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732	0.5984721	0.3816610	0.1411200
0.8414710	0.9489846	0.9974950	0.9839859	0.9092974	0.7780732	0.5984721	0.3816610	0.1411200	-0.1081951

In [7]:

#last five rows from training data
ts_head(samp$train[-c(1:(nrow(samp$train)-5)),])

A matrix: 5 × 10 of type dbl
t9	t8	t7	t6	t5	t4	t3	t2	t1	t0
-0.27941550	-0.03317922	0.2151200	0.4500441	0.6569866	0.8230809	0.9380000	0.9945988	0.9893582	0.9226042
-0.03317922	0.21511999	0.4500441	0.6569866	0.8230809	0.9380000	0.9945988	0.9893582	0.9226042	0.7984871
0.21511999	0.45004407	0.6569866	0.8230809	0.9380000	0.9945988	0.9893582	0.9226042	0.7984871	0.6247240
0.45004407	0.65698660	0.8230809	0.9380000	0.9945988	0.9893582	0.9226042	0.7984871	0.6247240	0.4121185
0.65698660	0.82308088	0.9380000	0.9945988	0.9893582	0.9226042	0.7984871	0.6247240	0.4121185	0.1738895

In [8]:

#testing data
ts_head(samp$test)

A matrix: 3 × 10 of type dbl
t9	t8	t7	t6	t5	t4	t3	t2	t1	t0
0.8230809	0.9380000	0.9945988	0.9893582	0.9226042	0.7984871	0.6247240	0.41211849	0.17388949	-0.07515112
0.9380000	0.9945988	0.9893582	0.9226042	0.7984871	0.6247240	0.4121185	0.17388949	-0.07515112	-0.31951919
0.9945988	0.9893582	0.9226042	0.7984871	0.6247240	0.4121185	0.1738895	-0.07515112	-0.31951919	-0.54402111