Normalization Diff¶

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)
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

In [5]:

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

normalization¶

In [6]:

preproc <- ts_norm_diff()
preproc <- fit(preproc, ts)
tst <- transform(preproc, ts)
ts_head(tst, 3)
summary(tst[,9])
plot_ts(y=ts[,9]) + theme(text = element_text(size=16))

A matrix: 3 × 9 of type dbl
t8	t7	t6	t5	t4	t3	t2	t1	t0
0.9982009	0.9672887	0.9073861	0.8222178	0.7170790	0.5985067	0.4738732	0.3509276	0.23731412
0.9672887	0.9073861	0.8222178	0.7170790	0.5985067	0.4738732	0.3509276	0.2373141	0.14009662
0.9073861	0.8222178	0.7170790	0.5985067	0.4738732	0.3509276	0.2373141	0.1400966	0.06531964

       t0         
 Min.   :0.00000  
 1st Qu.:0.06333  
 Median :0.29337  
 Mean   :0.40975  
 3rd Qu.:0.75129  
 Max.   :1.00000

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