Apply 11
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# for financial analysis
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## as.zoo.data.frame zoo# for times series
library(timetk)## Warning: package 'timetk' was built under R version 4.3.3Goal: Apply Matt Dancho’s tutorial to state unemployment initial claims of New England states.
The following is the replication of Matt Dancho’s tutorial on this page
start_date <- "1989-01-01"
symbols_txt <- c("CTICLAIMS", # Connecticut
"MEICLAIMS", # Maine
"MAICLAIMS", # Massachusetts
"NHICLAIMS", # New Hampshire
"RIICLAIMS", # Rhode Island
"VTICLAIMS") # Vermont
claims_tbl <- tq_get(symbols_txt, get = "economic.data", from = start_date) %>%
mutate(symbol = fct_recode(symbol,
"Connecticut" = "CTICLAIMS",
"Maine" = "MEICLAIMS",
"Massachusetts" = "MAICLAIMS",
"New Hampshire" = "NHICLAIMS",
"Rhode Island" = "RIICLAIMS",
"Vermont" = "VTICLAIMS")) %>%
rename(claims = price)Plotting time series
Box plots
claims_tbl %>%
group_by(symbol) %>%
plot_time_series_boxplot(date, claims,
.period = "1 years",
.facet_ncol = 2)Regression plots
claims_tbl %>%
group_by(symbol) %>%
plot_time_series_regression(
.date_var = date,
.formula = log(claims) ~ as.numeric(date) + month(date, label = TRUE),
.facet_ncol = 2,
.show_summary = FALSE)Plotting Seasonality and Correlation
Correlation Plots
# ACF
claims_tbl %>%
group_by(symbol) %>%
plot_acf_diagnostics(
date, claims,
.lags = "12 months")# CCF
claims_tbl %>%
select(symbol, date, claims) %>%
group_by(symbol) %>%
plot_acf_diagnostics(
date, claims,
.ccf_vars = c(claims),
.lags = "12 months")Seasonality
claims_tbl %>%
plot_seasonal_diagnostics(date, claims)# Grouped
claims_tbl %>%
group_by(symbol) %>%
plot_seasonal_diagnostics(date, claims)STL Diagnostics
claims_tbl %>%
group_by(symbol) %>%
plot_stl_diagnostics(
date, claims,
.feature_set = c("observed", "season", "trend", "remainder"))## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 year## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 year## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 year## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 year## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 year## frequency = 13 observations per 1 quarter## trend = 53 observations per 1 yearTime Series Data Wrangling
Summarize by Time
claims_tbl %>%
group_by(symbol) %>%
summarise_by_time(
date,
.by = "quarter",
volume = sum(claims)
) %>%
plot_time_series(date, volume, .facet_ncol = 2, .interactive = FALSE)
claims_tbl %>%
group_by(symbol) %>%
summarise_by_time(date, .by = "month", adjusted = first(claims)) %>%
plot_time_series(date, adjusted, .facet_ncol = 2, .interactive = FALSE)
Filter By Time
claims_tbl %>%
group_by(symbol) %>%
filter_by_time(date, "2024-01-07", "2024") %>%
plot_time_series(date, claims, .facet_ncol = 2, .interactive = FALSE)
Padding Data
claims_tbl%>%
group_by(symbol) %>%
pad_by_time(date, .by = "day", .pad_value = 0)## # A tibble: 78,504 × 3
## # Groups: symbol [6]
## symbol date claims
## <fct> <date> <int>
## 1 Connecticut 1989-01-07 8345
## 2 Connecticut 1989-01-08 0
## 3 Connecticut 1989-01-09 0
## 4 Connecticut 1989-01-10 0
## 5 Connecticut 1989-01-11 0
## 6 Connecticut 1989-01-12 0
## 7 Connecticut 1989-01-13 0
## 8 Connecticut 1989-01-14 6503
## 9 Connecticut 1989-01-15 0
## 10 Connecticut 1989-01-16 0
## # ℹ 78,494 more rowsSliding (Rolling) Calculations
rolling <-claims_tbl %>%
head(10) %>%
mutate(rolling_avg = slidify_vec(claims, mean, .period = 2,
.align = "right",
.partial = TRUE))