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 <- 5
ts <- ts_data(sin_data$y, sw_size)
ts_head(ts, 3)

A matrix: 3 × 5 of type dbl
t4	t3	t2	t1	t0
0.0000000	0.2474040	0.4794255	0.6816388	0.8414710
0.2474040	0.4794255	0.6816388	0.8414710	0.9489846
0.4794255	0.6816388	0.8414710	0.9489846	0.9974950

projection¶

In [5]:

io <- ts_projection(ts)

In [6]:

#input data
ts_head(io$input)

A matrix: 6 × 4 of type dbl
t4	t3	t2	t1
0.0000000	0.2474040	0.4794255	0.6816388
0.2474040	0.4794255	0.6816388	0.8414710
0.4794255	0.6816388	0.8414710	0.9489846
0.6816388	0.8414710	0.9489846	0.9974950
0.8414710	0.9489846	0.9974950	0.9839859
0.9489846	0.9974950	0.9839859	0.9092974

In [7]:

#output data
ts_head(io$output)

A matrix: 6 × 1 of type dbl
t0
0.8414710
0.9489846
0.9974950
0.9839859
0.9092974
0.7780732

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