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tsar's Introduction

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(). 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 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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tsar's People

Contributors

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