Normalization Global Min-Max¶

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# 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¶

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

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

       t0          
 Min.   :-0.99929  
 1st Qu.:-0.55091  
 Median : 0.05397  
 Mean   : 0.02988  
 3rd Qu.: 0.63279  
 Max.   : 0.99460

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library(ggplot2)
plot_ts(y=ts[,10]) + theme(text = element_text(size=16))

normalization¶

In [6]:

preproc <- ts_norm_gminmax()
preproc <- fit(preproc, ts)
tst <- transform(preproc, ts)
ts_head(tst, 3)
summary(tst[,10])
plot_ts(y=ts[,10]) + theme(text = element_text(size=16))

A matrix: 3 × 10 of type dbl
t9	t8	t7	t6	t5	t4	t3	t2	t1	t0
0.5004502	0.6243512	0.7405486	0.8418178	0.9218625	0.9757058	1.0000000	0.9932346	0.9558303	0.8901126
0.6243512	0.7405486	0.8418178	0.9218625	0.9757058	1.0000000	0.9932346	0.9558303	0.8901126	0.8001676
0.7405486	0.8418178	0.9218625	0.9757058	1.0000000	0.9932346	0.9558303	0.8901126	0.8001676	0.6915877

       t0        
 Min.   :0.0000  
 1st Qu.:0.2246  
 Median :0.5275  
 Mean   :0.5154  
 3rd Qu.:0.8174  
 Max.   :0.9985

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