Future work

make causality a separate lecture 10, add on diff-in-diff with cannabis example.

add example coverage calculations Lect 2 or ARIMA, eventually move part of the material from l02 to a separate lecture on Model evaluation and forecasting. forecast::accuracy(). caret::postResample(obs = y_test, pred = y_dnn) Overfitting. B&D Ch. 9 Remember that all predictive inference is based on the assumptions that the relationships between the variables and their dynamics will be the same in the future.

add a lecture on panel data analysis
expand on wavelets, add example of acoustic data
appendix or lecture on changepoint analysis
GLM or another generalized model with classification example for fisheries or ecology.
local stationarity, warping, time motifs, graph representation of time series
from NOAA project with G.N.: "Environmental Statistics 2: identification of different types of rare events in time series (appendix?), time-series cross-validation (l 12?), and GAMLSS (lect 12 on TSREG2 - done; lect 3 - done)"

Conventions and format examples

Spellings

a.k.a. changepoint dataset heteroskedasticity homoskedasticity hyperparameter nondeterministic nonlinear nonnegative nonparametric nonstationarity non-existent non-monotonic non-normal non-overlapping non-seasonal scatterplot vs.

Format

$p$-value $\mathrm{WN}(0,\sigma^2)$ $N(0,1)$ $X_t \sim$ I(2)

\boldsymbol \dots (not \ldots or \cdots)

Use italics for highlights in text, not bold.

Use 'single quotes' in text whenever possible.

#| code-fold: false

Space and capital letter after a comment sign:

This is a comment

Cite @Brockwell:Davis:2002 or [@Brockwell:Davis:2002] or [@Rebane:Pearl:1987;@Pearl:2009]

Recall the classical decomposition $$ Y_t = M_t + S_t + \epsilon_t, $${#eq-trseas}

model as @eq-trseas is

Riksbank Prize

Figures

fig-height use default (5) for 1-2 plots per row #| fig-height: 3 for 3 plots per row #| fig-height: 7 for decompose or 2 rows #| fig-height: 9 for 3 rows

#| label: fig-shampoo
#| fig-cap: "Monthly shampoo sales over three years and a corresponding sample ACF."

p1 <- forecast::autoplot(shampoo) +
    xlab("Year") +
    ylab("Sales") +
    theme_light()
p2 <- forecast::ggAcf(shampoo) +
    ggtitle("") +
    xlab("Lag (months)") +
    theme_light()
p1 + p2 +
    plot_annotation(tag_levels = 'A') &
    theme_light()

Notes and examples

::: {.callout-note} text :::

::: {.callout-note icon=false}

Example: Secchi

text :::

Table manual formatting

from 0 to $d_{L}$	from $d_{L}$ to $d_{U}$	from $d_{U}$ to $4 - d_{U}$	from $4 - d_{U}$ to $4 - d_{L}$	from $4 - d_{L}$ to 4
Reject $H_{0}$, positive autocorrelation	Neither accept $H_{1}$ or reject $H_{0}$	Do not reject $H_{0}$	Neither accept $H_{1}$ or reject $H_{0}$	Reject $H_{0}$, negative autocorrelation

: Regions of rejection of the null hypothesis for the Durbin--Watson test {#tbl-DW}

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indigo423 / tsar