Author
library(forecast)
library(pracma)
library(RTransferEntropy)
library(lmtest)
Immigration, Refugees and Citizenship Canada (IRCC), Permanent Residents – Monthly IRCC Updates
Canada - Admissions of Permanent Residents by Province/Territory, Census Division and Census Subdivision of the Intended Destination (2018 ranking), January 2015 - May 2019
Seasonal Decomposition
IRCC_admissions <- read.csv("IRCC_admissions.csv")
Toronto.ts <- ts(IRCC_admissions$Toronto, frequency = 12, start = 2015)
Toronto.ts.components <- decompose(Toronto.ts)
autoplot(Toronto.ts.components)
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)
ON.ts <- ts(IRCC_admissions$ON, frequency = 12, start = 2015)
ON.ts.components <- decompose(ON.ts)
autoplot(ON.ts.components)
![](data:image/png;base64,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)
Vancouver.ts <- ts(IRCC_admissions$Vancouver, frequency = 12, start = 2015)
Vancouver.ts.components <- decompose(Vancouver.ts)
autoplot(Vancouver.ts.components)
![](data:image/png;base64,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)
BC.ts <- ts(IRCC_admissions$BC, frequency = 12, start = 2015)
BC.ts.components <- decompose(BC.ts)
autoplot(BC.ts.components)
![](data:image/png;base64,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)
Cross-Correlation
E. E. Holmes, M. D. Scheuerell, and E. J. Ward, Applied Time Series Analysis for Fisheries and Environmental Sciences. Section 4.4: Correlation within and among time series
length(Toronto.ts)
[1] 53
ccf(Toronto.ts, Vancouver.ts, main = NA, lag = 52, ylab="Cross-correlation")
![](data:image/png;base64,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)
ccf(Toronto.ts, ON.ts, main = NA, lag = 52, ylab="Cross-correlation")
![](data:image/png;base64,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)
ccf(Vancouver.ts, BC.ts, main = NA, lag = 52, ylab="Cross-correlation")
![](data:image/png;base64,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)
ccf(ON.ts, BC.ts, main = NA, lag = 52, ylab="Cross-correlation")
![](data:image/png;base64,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)
Sample Entropy
pracma, Approximate And Sample Entropy
Sample Entropy with Rcpp
sample_entropy(Toronto.ts)
[1] 2.564949
sample_entropy(ON.ts)
[1] 1.871802
sample_entropy(Vancouver.ts)
[1] 1.824549
sample_entropy(BC.ts)
[1] 3.135494
approx_entropy(Toronto.ts)
[1] 0.4996574
approx_entropy(ON.ts)
[1] 0.4241658
approx_entropy(Vancouver.ts)
[1] 0.4778336
approx_entropy(BC.ts)
[1] 0.4569236
Simon Behrendt, Thomas Dimpfl, Franziska J. Peter, and David J. Zimmermann, RTransferEntropy
calc_te(Toronto.ts, Vancouver.ts)
[1] 0.05672144
calc_te(Toronto.ts, ON.ts)
[1] 0.01701271
calc_te(ON.ts, BC.ts)
[1] 0.02579484
Granger Causality
lmtest, Test For Granger Causality
grangertest(Toronto.ts, ON.ts)
Granger causality test
Model 1: ON.ts ~ Lags(ON.ts, 1:1) + Lags(Toronto.ts, 1:1)
Model 2: ON.ts ~ Lags(ON.ts, 1:1)
Res.Df Df F Pr(>F)
1 49
2 50 -1 1.4876 0.2284
grangertest(Vancouver.ts, BC.ts)
Granger causality test
Model 1: BC.ts ~ Lags(BC.ts, 1:1) + Lags(Vancouver.ts, 1:1)
Model 2: BC.ts ~ Lags(BC.ts, 1:1)
Res.Df Df F Pr(>F)
1 49
2 50 -1 1.9343 0.1706
grangertest(Toronto.ts, Vancouver.ts)
Granger causality test
Model 1: Vancouver.ts ~ Lags(Vancouver.ts, 1:1) + Lags(Toronto.ts, 1:1)
Model 2: Vancouver.ts ~ Lags(Vancouver.ts, 1:1)
Res.Df Df F Pr(>F)
1 49
2 50 -1 1.7999 0.1859
grangertest(ON.ts, BC.ts)
Granger causality test
Model 1: BC.ts ~ Lags(BC.ts, 1:1) + Lags(ON.ts, 1:1)
Model 2: BC.ts ~ Lags(BC.ts, 1:1)
Res.Df Df F Pr(>F)
1 49
2 50 -1 3.5083 0.06703 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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