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---
title: "Data Science Methods for Forecasting in Energy and Economics"
date: 2025-07-10
author:
- name: Jonathan Berrisch
affiliations:
- ref: hemf
affiliations:
- id: hemf
name: University of Duisburg-Essen, House of Energy Markets and Finance
format:
revealjs:
embed-resources: true
footer: ""
logo: assets/logos_combined.png
theme: [default, clean.scss]
smaller: true
fig-format: svg
execute:
daemon: false
highlight-style: github
---
<!--
Render with: quarto preview /home/jonathan/git/PHD-Presentation/25_07_phd_defense/index.qmd --no-browser --port 6074
-->
## Outline
::: {.hidden}
$$
\newcommand{\A}{{\mathbb A}}
$$
:::
<br>
::: {style="font-size: 150%;"}
[{{< fa bars-staggered >}}]{style="color: #404040;"} &ensp; Introduction & Research Motivation
[{{< fa bars-staggered >}}]{style="color: #404040;"} &ensp; Overview of the Thesis
[{{< fa table >}}]{style="color: #404040;"} &ensp; Online Learning
[{{< fa circle-nodes >}}]{style="color: #404040;"} &ensp; Probabilistic Forecasting of European Carbon and Energy Prices
[{{< fa lightbulb >}}]{style="color: #404040;"} &ensp; Limitations
[{{< fa binoculars >}}]{style="color: #404040;"} &ensp; Contributions & Outlook
:::
## PHD DeFence
```{r, setup, include=FALSE}
# Compile with: rmarkdown::render("crps_learning.Rmd")
library(latex2exp)
library(ggplot2)
library(dplyr)
library(tidyr)
library(purrr)
library(kableExtra)
knitr::opts_chunk$set(
dev = "svglite" # Use svg figures
)
library(RefManageR)
BibOptions(
check.entries = TRUE,
bib.style = "authoryear",
cite.style = "authoryear",
style = "html",
hyperlink = TRUE,
dashed = FALSE
)
my_bib <- ReadBib("assets/library.bib", check = FALSE)
col_lightgray <- "#e7e7e7"
col_blue <- "#000088"
col_smooth_expost <- "#a7008b"
col_smooth <- "#187a00"
col_pointwise <- "#008790"
col_constant <- "#dd9002"
col_optimum <- "#666666"
```
```{r xaringan-panelset, echo=FALSE}
xaringanExtra::use_panelset()
```
```{r xaringanExtra-freezeframe, echo=FALSE}
xaringanExtra::use_freezeframe(responsive = TRUE)
```
# Outline
- [Motivation](#motivation)
- [The Framework of Prediction under Expert Advice](#pred_under_exp_advice)
- [The Continious Ranked Probability Scrore](#crps)
- [Optimality of (Pointwise) CRPS-Learning](#crps_optim)
- [A Simple Probabilistic Example](#simple_example)
- [The Proposed CRPS-Learning Algorithm](#proposed_algorithm)
- [Simulation Results](#simulation)
- [Possible Extensions](#extensions)
- [Application Study](#application)
- [Wrap-Up](#conclusion)
- [References](#references)
---
# Motivation
name: motivation
## Motivation
:::: {.columns}
::: {.column width="48%"}
The Idea:
- Combine multiple forecasts instead of choosing one
- Combination weights may vary over **time**, over the **distribution** or **both**
2 Popular options for combining distributions:
- Combining across quantiles (this paper)
- Horizontal aggregation, vincentization
- Combining across probabilities
- Vertical aggregation
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
::: {.panel-tabset}
## Time
```{r, echo = FALSE, fig.height=6}
par(mfrow = c(3, 3), mar = c(2, 2, 2, 2))
set.seed(1)
# Data
X <- matrix(ncol = 3, nrow = 15)
X[, 1] <- seq(from = 8, to = 12, length.out = 15) + 0.25 * rnorm(15)
X[, 2] <- 10 + 0.25 * rnorm(15)
X[, 3] <- seq(from = 12, to = 8, length.out = 15) + 0.25 * rnorm(15)
# Weights
w <- matrix(ncol = 3, nrow = 15)
w[, 1] <- sin(0.1 * 1:15)
w[, 2] <- cos(0.1 * 1:15)
w[, 3] <- seq(from = -2, 0.25, length.out = 15)^2
w <- (w / rowSums(w))
# Vis
plot(X[, 1],
lwd = 4,
type = "l",
ylim = c(8, 12),
xlab = "",
ylab = "",
xaxt = "n",
yaxt = "n",
bty = "n",
col = "#2050f0"
)
plot(w[, 1],
lwd = 4, type = "l",
ylim = c(0, 1),
xlab = "",
ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "#2050f0"
)
text(6, 0.5, TeX("$w_1(t)$"), cex = 2, col = "#2050f0")
arrows(13, 0.25, 15, 0.0, , lwd = 4, bty = "n")
plot.new()
plot(X[, 2],
lwd = 4,
type = "l", ylim = c(8, 12),
xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "purple"
)
plot(w[, 2],
lwd = 4, type = "l",
ylim = c(0, 1),
xlab = "",
ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "purple"
)
text(6, 0.6, TeX("$w_2(t)$"), cex = 2, col = "purple")
arrows(13, 0.5, 15, 0.5, , lwd = 4, bty = "n")
plot(rowSums(X * w), lwd = 4, type = "l", xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "#298829")
plot(X[, 3],
lwd = 4,
type = "l", ylim = c(8, 12),
xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "#e423b4"
)
plot(w[, 3],
lwd = 4, type = "l",
ylim = c(0, 1),
xlab = "",
ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "#e423b4"
)
text(6, 0.25, TeX("$w_3(t)$"), cex = 2, col = "#e423b4")
arrows(13, 0.75, 15, 1, , lwd = 4, bty = "n")
```
## Distribution
```{r, echo = FALSE, fig.height=6}
par(mfrow = c(3, 3), mar = c(2, 2, 2, 2))
set.seed(1)
# Data
X <- matrix(ncol = 3, nrow = 31)
X[, 1] <- dchisq(0:30, df = 10)
X[, 2] <- dnorm(0:30, mean = 15, sd = 5)
X[, 3] <- dexp(0:30, 0.2)
# Weights
w <- matrix(ncol = 3, nrow = 31)
w[, 1] <- sin(0.05 * 0:30)
w[, 2] <- cos(0.05 * 0:30)
w[, 3] <- seq(from = -2, 0.25, length.out = 31)^2
w <- (w / rowSums(w))
# Vis
plot(X[, 1],
lwd = 4,
type = "l",
xlab = "",
ylab = "",
xaxt = "n",
yaxt = "n",
bty = "n",
col = "#2050f0"
)
plot(X[, 2],
lwd = 4,
type = "l",
xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "purple"
)
plot(X[, 3],
lwd = 4,
type = "l",
xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "#e423b4"
)
plot(w[, 1],
lwd = 4, type = "l",
ylim = c(0, 1),
xlab = "",
ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "#2050f0"
)
text(12, 0.5, TeX("$w_1(x)$"), cex = 2, col = "#2050f0")
arrows(26, 0.25, 31, 0.0, , lwd = 4, bty = "n")
plot(w[, 2],
lwd = 4, type = "l",
ylim = c(0, 1),
xlab = "",
ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "purple"
)
text(15, 0.5, TeX("$w_2(x)$"), cex = 2, col = "purple")
arrows(15, 0.25, 15, 0, , lwd = 4, bty = "n")
plot(w[, 3],
lwd = 4, type = "l",
ylim = c(0, 1),
xlab = "",
ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "#e423b4"
)
text(20, 0.5, TeX("$w_3(x)$"), cex = 2, col = "#e423b4")
arrows(5, 0.25, 0, 0, , lwd = 4, bty = "n")
plot.new()
plot(rowSums(X * w), lwd = 4, type = "l", xlab = "", ylab = "", xaxt = "n", yaxt = "n", bty = "n", col = "#298829")
```
:::
:::
::::
# The Framework of Prediction under Expert Advice
## The Framework of Prediction under Expert Advice
### The sequential framework
:::: {.columns}
::: {.column width="48%"}
Each day, $t = 1, 2, ... T$
- The **forecaster** receives predictions $\widehat{X}_{t,k}$ from $K$ **experts**
- The **forecaster** assings weights $w_{t,k}$ to each **expert**
- The **forecaster** calculates her prediction:
\begin{equation}
\widetilde{X}_{t} = \sum_{k=1}^K w_{t,k} \widehat{X}_{t,k}.
\label{eq_forecast_def}
\end{equation}
- The realization for $t$ is observedilities
- Vertical aggregation
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
- The experts can be institutions, persons, or models
- The forecasts can be point-forecasts (i.e., mean or median) or full predictive distributions
- We do not need any assumptions concerning the underlying data
- `r Citet(my_bib, "cesa2006prediction")`
:::
::::
---
## The Regret
Weights are updated sequentially according to the past performance of the $K$ experts.
That is, a loss function $\ell$ is needed. This is used to compute the **cumulative regret** $R_{t,k}$
\begin{equation}
R_{t,k} = \widetilde{L}_{t} - \widehat{L}_{t,k} = \sum_{i = 1}^t \ell(\widetilde{X}_{i},Y_i) - \ell(\widehat{X}_{i,k},Y_i)
\label{eq_regret}
\end{equation}
The cumulative regret:
- Indicates the predictive accuracy of the expert $k$ until time $t$.
- Measures how much the forecaster *regrets* not having followed the expert's advice
Popular loss functions for point forecasting `r Citet(my_bib, "gneiting2011making")`:
.pull-left[
- $\ell_2$-loss $\ell_2(x, y) = | x -y|^2$
- optimal for mean prediction
]
.pull-right[
- $\ell_1$-loss $\ell_1(x, y) = | x -y|$
- optimal for median predictions
]
:::: {.columns}
::: {.column width="48%"}
- $\ell_2$-loss $\ell_2(x, y) = | x -y|^2$
- optimal for mean prediction
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
- $\ell_1$-loss $\ell_1(x, y) = | x -y|$
- optimal for median predictions
:::
::::
## Popular Aggregation Algorithms
#### The naive combination
\begin{equation}
w_{t,k}^{\text{Naive}} = \frac{1}{K}
\end{equation}
#### The exponentially weighted average forecaster (EWA)
\begin{align}
w_{t,k}^{\text{EWA}} & = \frac{e^{\eta R_{t,k}} }{\sum_{k = 1}^K e^{\eta R_{t,k}}}
=
\frac{e^{-\eta \ell(\widehat{X}_{t,k},Y_t)} w^{\text{EWA}}_{t-1,k} }{\sum_{k = 1}^K e^{-\eta \ell(\widehat{X}_{t,k},Y_t)} w^{\text{EWA}}_{t-1,k} }
\label{eq_ewa_general}
\end{align}
#### The polynomial weighted aggregation (PWA)
\begin{align}
w_{t,k}^{\text{PWA}} & = \frac{ 2(R_{t,k})^{q-1}_{+} }{ \|(R_t)_{+}\|^{q-2}_q}
\label{eq_pwa_general}
\end{align}
with $q\geq 2$ and $x_{+}$ the (vector) of positive parts of $x$.
## Optimality
In stochastic settings, the cumulative Risk should be analyezed `r Citet(my_bib, "wintenberger2017optimal")`:
\begin{align}
\underbrace{\widetilde{\mathcal{R}}_t = \sum_{i=1}^t \mathbb{E}[\ell(\widetilde{X}_{i},Y_i)|\mathcal{F}_{i-1}]}_{\text{Cumulative Risk of Forecaster}} \qquad\qquad\qquad \text{ and } \qquad\qquad\qquad
\underbrace{\widehat{\mathcal{R}}_{t,k} = \sum_{i=1}^t \mathbb{E}[\ell(\widehat{X}_{i,k},Y_i)|\mathcal{F}_{i-1}]}_{\text{Cumulative Risk of Experts}}
\label{eq_def_cumrisk}
\end{align}
There are two problems that an algorithm should solve in iid settings:
:::: {.columns}
::: {.column width="48%"}
### The selection problem
\begin{equation}
\frac{1}{t}\left(\widetilde{\mathcal{R}}_t - \widehat{\mathcal{R}}_{t,\min} \right) \stackrel{t\to \infty}{\rightarrow} a \quad \text{with} \quad a \leq 0.
\label{eq_opt_select}
\end{equation}
The forecaster is asymptotically not worse than the best expert $\widehat{\mathcal{R}}_{t,\min}$.
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
### The convex aggregation problem
\begin{equation}
\frac{1}{t}\left(\widetilde{\mathcal{R}}_t - \widehat{\mathcal{R}}_{t,\pi} \right) \stackrel{t\to \infty}{\rightarrow} b \quad \text{with} \quad b \leq 0 .
\label{eq_opt_conv}
\end{equation}
The forecaster is asymptotically not worse than the best convex combination $\widehat{X}_{t,\pi}$ in hindsight (**oracle**).
:::
::::
## Optimality
Satisfying the convexity property \eqref{eq_opt_conv} comes at the cost of slower possible convergence.
According to `r Citet(my_bib, "wintenberger2017optimal")`, an algorithm has optimal rates with respect to selection \eqref{eq_opt_select} and convex aggregation \eqref{eq_opt_conv} if
\begin{align}
\frac{1}{t}\left(\widetilde{\mathcal{R}}_t - \widehat{\mathcal{R}}_{t,\min} \right) & =
\mathcal{O}\left(\frac{\log(K)}{t}\right)\label{eq_optp_select}
\end{align}
\begin{align}
\frac{1}{t}\left(\widetilde{\mathcal{R}}_t - \widehat{\mathcal{R}}_{t,\pi} \right) & =
\mathcal{O}\left(\sqrt{\frac{\log(K)}{t}}\right)
\label{eq_optp_conv}
\end{align}
Algorithms can statisfy both \eqref{eq_optp_select} and \eqref{eq_optp_conv} depending on:
- The loss function
- Regularity conditions on $Y_t$ and $\widehat{X}_{t,k}$
- The weighting scheme
## Optimality
According to `r Citet(my_bib, "cesa2006prediction")` EWA \eqref{eq_ewa_general} satisfies the optimal selection convergence \eqref{eq_optp_select} in a deterministic setting if the:
- Loss $\ell$ is exp-concave
- Learning-rate $\eta$ is chosen correctly
Those results can be converted to stochastic iid settings `r Citet(my_bib, "kakade2008generalization")` `r Citet(my_bib, "gaillard2014second")`.
The optimal convex aggregation convergence \eqref{eq_optp_conv} can be satisfied by applying the kernel-trick. Thereby, the loss is linearized:
\begin{align}
\ell^{\nabla}(x,y) = \ell'(\widetilde{X},y) x
\end{align}
$\ell'$ is the subgradient of $\ell$ in its first coordinate evaluated at forecast combination $\widetilde{X}$.
Combining probabilistic forecasts calls for a probabilistic loss function
:::: {.notes}
We apply Bernstein Online Aggregation (BOA). It lets us weaken the exp-concavity condition while almost keeping the optimalities \ref{eq_optp_select} and \ref{eq_optp_conv}.
::::
## The Continuous Ranked Probability Score
:::: {.columns}
::: {.column width="48%"}
**An appropriate choice:**
\begin{align*}
\text{CRPS}(F, y) & = \int_{\mathbb{R}} {(F(x) - \mathbb{1}\{ x > y \})}^2 dx
\label{eq_crps}
\end{align*}
It's strictly proper `r Citet(my_bib, "gneiting2007strictly")`.
Using the CRPS, we can calculate time-adaptive weight $w_{t,k}$. However, what if the experts' performance is not uniform over all parts of the distribution?
The idea: utilize this relation:
\begin{align*}
\text{CRPS}(F, y) = 2 \int_0^{1} \text{QL}_p(F^{-1}(p), y) \, d p.
\label{eq_crps_qs}
\end{align*}
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
to combine quantiles of the probabilistic forecasts individually using the quantile-loss (QL):
\begin{align*}
\text{QL}_p(q, y) & = (\mathbb{1}\{y < q\} -p)(q - y)
\end{align*}
</br>
**But is it optimal?**
CRPS is exp-concave `r fontawesome::fa("check", fill ="#00b02f")`
`r fontawesome::fa("arrow-right", fill ="#000000")` EWA \eqref{eq_ewa_general} with CRPS satisfies \eqref{eq_optp_select} and \eqref{eq_optp_conv}
QL is convex, but not exp-concave `r fontawesome::fa("exclamation", fill ="#ffa600")`
`r fontawesome::fa("arrow-right", fill ="#000000")` Bernstein Online Aggregation (BOA) lets us weaken the exp-concavity condition while almost keeping optimal convergence
:::
::::
## CRPS-Learning Optimality
For convex losses, BOAG satisfies that there exist a $C>0$ such that for $x>0$ it holds that
\begin{equation}
P\left( \frac{1}{t}\left(\widetilde{\mathcal{R}}_t - \widehat{\mathcal{R}}_{t,\pi} \right) \leq C \log(\log(t)) \left(\sqrt{\frac{\log(K)}{t}} + \frac{\log(K)+x}{t}\right) \right) \geq
1-e^{x}
\label{eq_boa_opt_conv}
\end{equation}
`r fontawesome::fa("arrow-right", fill ="#000000")` Almost optimal w.r.t *convex aggregation* \eqref{eq_optp_conv} `r Citet(my_bib, "wintenberger2017optimal")` .
The same algorithm satisfies that there exist a $C>0$ such that for $x>0$ it holds that
\begin{equation}
P\left( \frac{1}{t}\left(\widetilde{\mathcal{R}}_t - \widehat{\mathcal{R}}_{t,\min} \right) \leq
C\left(\frac{\log(K)+\log(\log(Gt))+ x}{\alpha t}\right)^{\frac{1}{2-\beta}} \right) \geq
1-e^{x}
\label{eq_boa_opt_select}
\end{equation}
if $Y_t$ is bounded, the considered loss $\ell$ is convex $G$-Lipschitz and weak exp-concave in its first coordinate.
This is for losses that satisfy **A1** and **A2**.
## CRPS-Learning Optimality
:::: {.columns}
::: {.column width="48%"}
**A1**
For some $G>0$ it holds
for all $x_1,x_2\in \mathbb{R}$ and $t>0$ that
$$ | \ell(x_1, Y_t)-\ell(x_2, Y_t) | \leq G |x_1-x_2|$$
**A2** For some $\alpha>0$, $\beta\in[0,1]$ it holds
for all $x_1,x_2 \in \mathbb{R}$ and $t>0$ that
\begin{align*}
\mathbb{E}[
& \ell(x_1, Y_t)-\ell(x_2, Y_t) | \mathcal{F}_{t-1}] \leq \\
& \mathbb{E}[ \ell'(x_1, Y_t)(x_1 - x_2) |\mathcal{F}_{t-1}] \\
& +
\mathbb{E}\left[ \left. \left( \alpha(\ell'(x_1, Y_t)(x_1 - x_2))^{2}\right)^{1/\beta} \right|\mathcal{F}_{t-1}\right]
\end{align*}
`r fontawesome::fa("arrow-right", fill ="#000000")` Almost optimal w.r.t *selection* \eqref{eq_optp_select} `r Citet(my_bib, "gaillard2018efficient")`.
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
**Lemma 1**
\begin{align}
2\overline{\widehat{\mathcal{R}}}^{\text{QL}}_{t,\min}
& \leq \widehat{\mathcal{R}}^{\text{CRPS}}_{t,\min}
\label{eq_risk_ql_crps_expert} \\
2\overline{\widehat{\mathcal{R}}}^{\text{QL}}_{t,\pi}
& \leq \widehat{\mathcal{R}}^{\text{CRPS}}_{t,\pi} .
\label{eq_risk_ql_crps_convex}
\end{align}
Pointwise can outperform constant procedures
QL is convex but not exp-concave:
`r fontawesome::fa("arrow-right")` Almost optimal convergence w.r.t. *convex aggregation* \eqref{eq_boa_opt_conv} `r fontawesome::fa("check", fill ="#00b02f")` </br>
For almost optimal congerence w.r.t. *selection* \eqref{eq_boa_opt_select} we need to check **A1** and **A2**:
QL is Lipschitz continuous:
`r fontawesome::fa("arrow-right")` **A1** holds `r fontawesome::fa("check", fill ="#ffa600")` </br>
:::
::::
## CRPS-Learning Optimality
:::: {.columns}
::: {.column width="48%"}
Conditional quantile risk: $\mathcal{Q}_p(x) = \mathbb{E}[ \text{QL}_p(x, Y_t) | \mathcal{F}_{t-1}]$.
`r fontawesome::fa("arrow-right")` convexity properties of $\mathcal{Q}_p$ depend on the
conditional distribution $Y_t|\mathcal{F}_{t-1}$.
**Proposition 1**
Let $Y$ be a univariate random variable with (Radon-Nikodym) $\nu$-density $f$, then for the second subderivative of the quantile risk
$\mathcal{Q}_p(x) = \mathbb{E}[ \text{QL}_p(x, Y) ]$
of $Y$ it holds for all $p\in(0,1)$ that
$\mathcal{Q}_p'' = f.$
Additionally, if $f$ is a continuous Lebesgue-density with $f\geq\gamma>0$ for some constant $\gamma>0$ on its support $\text{spt}(f)$ then
is $\mathcal{Q}_p$ is $\gamma$-strongly convex.
Strong convexity with $\beta=1$ implies **A2** `r fontawesome::fa("check", fill ="#ffa600")` `r Citet(my_bib, "gaillard2018efficient")`
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
`r fontawesome::fa("arrow-right")` **A1** and **A2** give us almost optimal convergence w.r.t. selection \eqref{eq_boa_opt_select} `r fontawesome::fa("check", fill ="#00b02f")` </br>
**Theorem 1**
The gradient based fully adaptive Bernstein online aggregation (BOAG) applied pointwise for all $p\in(0,1)$ on $\text{QL}$ satisfies
\eqref{eq_boa_opt_conv} with minimal CRPS given by
$$\widehat{\mathcal{R}}_{t,\pi} = 2\overline{\widehat{\mathcal{R}}}^{\text{QL}}_{t,\pi}.$$
If $Y_t|\mathcal{F}_{t-1}$ is bounded
and has a pdf $f_t$ satifying $f_t>\gamma >0$ on its
support $\text{spt}(f_t)$ then \ref{eq_boa_opt_select} holds with $\beta=1$ and
$$\widehat{\mathcal{R}}_{t,\min} = 2\overline{\widehat{\mathcal{R}}}^{\text{QL}}_{t,\min}$$.
:::
::::
## A Probabilistic Example
:::: {.columns}
::: {.column width="48%"}
Simple Example:
\begin{align}
Y_t & \sim \mathcal{N}(0,\,1) \\
\widehat{X}_{t,1} & \sim \widehat{F}_{1} = \mathcal{N}(-1,\,1) \\
\widehat{X}_{t,2} & \sim \widehat{F}_{2} = \mathcal{N}(3,\,4)
\label{eq:dgp_sim1}
\end{align}
- True weights vary over $p$
- Figures show the ECDF and calculated weights using $T=25$ realizations
- Pointwise solution creates rough estimates
- Pointwise is better than constant
- Smooth solution is better than pointwise
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
::: {.panel-tabset}
## CDFs
```{r, echo = FALSE, fig.width=7, fig.height=6, fig.align='center', cache = FALSE}
source("assets/01_common.R")
load("assets/crps_learning/01_motivation_01.RData")
ggplot(df, aes(x = x, y = y, xend = xend, yend = yend)) +
stat_function(
fun = pnorm, n = 10000,
args = list(mean = dev[2], sd = experts_sd[2]),
aes(col = "Expert 2"), size = 1.5
) +
stat_function(
fun = pnorm, n = 10000,
args = list(mean = dev[1], sd = experts_sd[1]),
aes(col = "Expert 1"), size = 1.5
) +
stat_function(
fun = pnorm,
n = 10000,
size = 1.5, aes(col = "DGP") # , linetype = "dashed"
) +
geom_point(aes(col = "ECDF"), size = 1.5, show.legend = FALSE) +
geom_segment(aes(col = "ECDF")) +
geom_segment(data = tibble(
x_ = -5,
xend_ = min(y),
y_ = 0,
yend_ = 0
), aes(x = x_, xend = xend_, y = y_, yend = yend_)) +
theme_minimal() +
theme(
text = element_text(size = text_size),
legend.position = "bottom",
legend.key.width = unit(1.5, "cm")
) +
ylab("Probability p") +
xlab("Value") +
scale_colour_manual(NULL, values = c("#969696", "#252525", col_auto, col_blue)) +
guides(color = guide_legend(
nrow = 2,
byrow = FALSE # ,
# override.aes = list(
# size = c(1.5, 1.5, 1.5, 1.5)
# )
)) +
scale_x_continuous(limits = c(-5, 7.5))
```
## Weights
```{r, echo = FALSE, fig.width=7, fig.height=6, fig.align='center', cache = FALSE}
source("assets/01_common.R")
load("assets/crps_learning/01_motivation_02.RData")
ggplot() +
geom_line(data = weights[weights$var != "1Optimum", ], size = 1.5, aes(x = prob, y = val, col = var)) +
geom_line(
data = weights[weights$var == "1Optimum", ], size = 1.5, aes(x = prob, y = val, col = var) # , linetype = "dashed"
) +
theme_minimal() +
theme(
text = element_text(size = text_size),
legend.position = "bottom",
legend.key.width = unit(1.5, "cm")
) +
xlab("Probability p") +
ylab("Weight w") +
scale_colour_manual(
NULL,
values = c("#969696", col_pointwise, col_p_constant, col_p_smooth),
labels = modnames[-c(3, 5)]
) +
guides(color = guide_legend(
ncol = 3,
byrow = FALSE,
title.hjust = 5,
# override.aes = list(
# linetype = c(rep("solid", 5), "dashed")
# )
)) +
ylim(c(0, 1))
```
::::
:::
:::
## The Smoothing Procedure
:::: {.columns}
::: {.column width="48%"}
We are using penalized cubic b-splines:
Let $\varphi=(\varphi_1,\ldots, \varphi_L)$ be bounded basis functions on $(0,1)$ Then we approximate $w_{t,k}$ by
\begin{align}
w_{t,k}^{\text{smooth}} = \sum_{l=1}^L \beta_l \varphi_l = \beta'\varphi
\end{align}
with parameter vector $\beta$. The latter is estimated penalized $L_2$-smoothing which minimizes
\begin{equation}
\| w_{t,k} - \beta' \varphi \|^2_2 + \lambda \| \mathcal{D}^{d} (\beta' \varphi) \|^2_2
\label{eq_function_smooth}
\end{equation}
with differential operator $\mathcal{D}$
Smoothing can be applied ex-post or inside of the algorithm ( `r fontawesome::fa("arrow-right", fill ="#000000")` [Simulation](#simulation)).
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
We receive the constant solution for high values of $\lambda$ when setting $d=1$
<center>
<img src="assets/crps_learning/weights_lambda.gif">
</center>
:::
::::
# The Proposed CRPS-Learning Algorithm
---
## The Proposed CRPS-Learning Algorithm
:::: {.columns}
::: {.column width="48%"}
**Initialization:**
Array of expert predicitons: $\widehat{X}_{t,k,p}$
Vector of Prediction targets: $Y_t$
Starting Weights: $w_0=(w_{0,1},\ldots, w_{0,K})$,
Penalization parameter: $\lambda\geq 0$
B-spline and penalty matrices $B$ and $D$ on $\mathcal{P}= (p_1,\ldots,p_M)$
Hat matrix: $$\mathcal{H} = B(B'B+ \lambda D'D)^{-1} B'$$
Cumulative Regret: $R_{0,k} = 0$
Range parameter: $E_{0,k}=0$
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
**Core**:
for(t in 1:T) { for(p in $\mathcal{P}$) {
&nbsp;&nbsp;&nbsp;&nbsp; $\widetilde{X}_{t,k}(p) = \sum_{k=1}^K w_{t-1,k,p} \widehat{X}_{t,k}(p)$ .grey[\# Prediction]
&nbsp;&nbsp;&nbsp;&nbsp; for(k in 1:K){
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; $r_{t,k,p} = \text{QL}_p^{\nabla}(\widehat{X}_{t,k}(p),Y_t) - \text{QL}_p^{\nabla}(\widetilde{X}_{t}(p),Y_t)$
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; $E_{t,k,p} = \max(E_{t-1,k,p}, |r_{t,k,p}|)$
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; $\eta_{t,k,p}=\min\left(1/2E_{t,k,p}, \sqrt{\log(K)/ \sum_{i=1}^t (r^2_{i, k,p})}\right)$
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; $R_{t,k,p} = R_{t-1,k,p} + \frac{1}{2} \left( r_{t,k,p} \left( 1+ \eta_{t,k,p} r_{t,k,p} \right) + 2E_{t,k,p} \mathbb{1}(\eta_{t,k,p}r_{t,k,p} > \frac{1}{2}) \right)$
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; $w_{t,k,p} = \eta_{t,k,p} \exp \left(- \eta_{t,k,p} R_{t,k,p} \right) w_{0,k,p} / \left( \frac{1}{K} \sum_{k = 1}^K \eta_{t,k,p} \exp \left( - \eta_{t,k,p} R_{t,k,p}\right) \right)$
&nbsp;&nbsp; }.grey[\#k]}.grey[\#p]
&nbsp;&nbsp; for(k in 1:K){
&nbsp;&nbsp;&nbsp;&nbsp; $w_{t,k} = \mathcal{H} w_{t,k}(\mathcal{P})$ .grey[\# Smoothing]
} .grey[\#k]} .grey[\#t]
:::
::::
## Simulation Study
:::: {.columns}
::: {.column width="48%"}
Data Generating Process of the [simple probabilistic example](#simple_example)
- Constant solution $\lambda \rightarrow \infty$
- Pointwise Solution of the proposed BOAG
- Smoothed Solution of the proposed BOAG
- Weights are smoothed during learning
- Smooth weights are used to calculate Regret, adjust weights, etc.
- Smooth ex-post solution
- Weights are smoothed after the learning
- Algorithm always uses non-smoothed weights
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
::: {.panel-tabset}
## QL Deviation
![](assets/crps_learning/pre_vs_post.gif)
## CRPS vs. Lambda
CRPS Values for different $\lambda$ (1000 runs)
![](assets/crps_learning/pre_vs_post_lambda.gif)
::::
:::
::::
## Simulation Study
The same simulation carried out for different algorithms (1000 runs):
<center>
<img src="assets/crps_learning/algos_constant.gif">
</center>
## Simulation Study
:::: {.columns}
::: {.column width="48%"}
**New DGP:**
\begin{align}
Y_t & \sim \mathcal{N}\left(\frac{\sin(0.005 \pi t )}{2},\,1\right) \\
\widehat{X}_{t,1} & \sim \widehat{F}_{1} = \mathcal{N}(-1,\,1) \\
\widehat{X}_{t,2} & \sim \widehat{F}_{2} = \mathcal{N}(3,\,4) \label{eq_dgp_sim2}
\end{align}
`r fontawesome::fa("arrow-right", fill ="#000000")` Changing optimal weights
`r fontawesome::fa("arrow-right", fill ="#000000")` Single run example depicted aside
`r fontawesome::fa("arrow-right", fill ="#000000")` No forgetting leads to long-term constant weights
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
**Weights of expert 2**
```{r, echo = FALSE, fig.width=7, fig.height=5, fig.align='center', cache = FALSE}
load("assets/crps_learning/changing_weights.rds")
mod_labs <- c("Optimum", "Pointwise", "Smooth", "Constant")
names(mod_labs) <- c("TOptimum", "Pointwise", "Smooth", "Constant")
colseq <- c(grey(.99), "orange", "red", "purple", "blue", "darkblue", "black")
weights_preprocessed %>%
mutate(w = 1 - w) %>%
ggplot(aes(t, p, fill = w)) +
geom_raster(interpolate = TRUE) +
facet_grid(Mod ~ ., labeller = labeller(Mod = mod_labs)) +
theme_minimal() +
theme(
# plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm"),
text = element_text(size = 15),
legend.key.height = unit(1, "inch")
) +
scale_x_continuous(expand = c(0, 0)) +
xlab("Time t") +
scale_fill_gradientn(
limits = c(0, 1),
colours = colseq,
breaks = seq(0, 1, 0.2)
) +
ylab("Weight w")
```
:::
::::
## Simulation Results
The simulation using the new DGP carried out for different algorithms (1000 runs):
<center>
<img src="assets/crps_learning/algos_changing.gif">
</center>
## Possible Extensions
:::: {.columns}
::: {.column width="48%"}
**Forgetting**
- Only taking part of the old cumulative regret into account
- Exponential forgetting of past regret
\begin{align*}
R_{t,k} & = R_{t-1,k}(1-\xi) + \ell(\widetilde{F}_{t},Y_i) - \ell(\widehat{F}_{t,k},Y_i) \label{eq_regret_forget}
\end{align*}
**Fixed Shares** `r Citet(my_bib, "herbster1998tracking")`
- Adding fixed shares to the weights
- Shrinkage towards a constant solution
\begin{align*}
\widetilde{w}_{t,k} = \rho \frac{1}{K} + (1-\rho) w_{t,k}
\label{fixed_share_simple}.
\end{align*}
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
**Non-Equidistant Knots**
- Non-equidistant spline-basis could be used
- Potentially improves the tail-behavior
- Destroys shrinkage towards constant
<center>
<img src="assets/crps_learning/uneven_grid.gif">
</center>
:::
::::
## Application Study: Overview
:::: {.columns}
::: {.column width="29%"}
Data:
- Forecasting European emission allowances (EUA)
- Daily month-ahead prices
- Jan 13 - Dec 20 (Phase III, 2092 Obs)
Combination methods:
- Naive, BOAG, EWAG, ML-PolyG, BMA
Tuning paramter grids:
- Smoothing Penalty: $\Lambda= \{0\}\cup \{2^x|x\in \{-4,-3.5,\ldots,12\}\}$
- Learning Rates: $\mathcal{E}= \{2^x|x\in \{-1,-0.5,\ldots,9\}\}$
:::
::: {.column width="2%"}
:::
::: {.column width="69%"}
```{r, echo = FALSE, fig.width=7, fig.height=5, fig.align='center', cache = FALSE}
load("assets/crps_learning/overview_data.rds")
data %>%
ggplot(aes(x = Date, y = value)) +
geom_line(size = 1, col = col_blue) +
theme_minimal() +
ylab("Value") +
facet_wrap(. ~ name, scales = "free", ncol = 1) +
theme(
text = element_text(size = 15),
strip.background = element_blank(),
strip.text.x = element_blank()
) -> p1
data %>%
ggplot(aes(x = value)) +
geom_histogram(aes(y = ..density..), size = 1, fill = col_blue, bins = 50) +
ylab("Density") +
xlab("Value") +
theme_minimal() +
theme(
strip.background = element_rect(fill = col_lightgray, colour = col_lightgray),
text = element_text(size = 15)
) +
facet_wrap(. ~ name, scales = "free", ncol = 1, strip.position = "right") -> p2
overview <- cowplot::plot_grid(plotlist = list(p1, p2), align = "hv", axis = "tblr", rel_widths = c(0.65, 0.35))
overview
```
:::
::::
## Application Study: Experts
Simple exponential smoothing with additive errors (**ETS-ANN**):
\begin{align*}
Y_{t} = l_{t-1} + \varepsilon_t \quad \text{with} \quad l_t = l_{t-1} + \alpha \varepsilon_t \quad \text{and} \quad \varepsilon_t \sim \mathcal{N}(0,\sigma^2)
\end{align*}
Quantile regression (**QuantReg**): For each $p \in \mathcal{P}$ we assume:
\begin{align*}
F^{-1}_{Y_t}(p) = \beta_{p,0} + \beta_{p,1} Y_{t-1} + \beta_{p,2} |Y_{t-1}-Y_{t-2}|
\end{align*}
ARIMA(1,0,1)-GARCH(1,1) with Gaussian errors (**ARMA-GARCH**):
\begin{align*}
Y_{t} = \mu + \phi(Y_{t-1}-\mu) + \theta \varepsilon_{t-1} + \varepsilon_t \quad \text{with} \quad \varepsilon_t = \sigma_t Z, \quad \sigma_t^2 = \omega + \alpha \varepsilon_{t-1}^2 + \beta \sigma_{t-1}^2 \quad \text{and} \quad Z_t \sim \mathcal{N}(0,1)
\end{align*}
ARIMA(0,1,0)-I-EGARCH(1,1) with Gaussian errors (**I-EGARCH**):
\begin{align*}
Y_{t} = \mu + Y_{t-1} + \varepsilon_t \quad \text{with} \quad \varepsilon_t = \sigma_t Z, \quad \log(\sigma_t^2) = \omega + \alpha Z_{t-1}+ \gamma (|Z_{t-1}|-\mathbb{E}|Z_{t-1}|) + \beta \log(\sigma_{t-1}^2) \quad \text{and} \quad Z_t \sim \mathcal{N}(0,1)
\end{align*}
ARIMA(0,1,0)-GARCH(1,1) with student-t errors (**I-GARCHt**):
\begin{align*}
Y_{t} = \mu + Y_{t-1} + \varepsilon_t \quad \text{with} \quad \varepsilon_t = \sigma_t Z, \quad \sigma_t^2 = \omega + \alpha \varepsilon_{t-1}^2 + \beta \sigma_{t-1}^2 \quad \text{and} \quad Z_t \sim t(0,1, \nu)
\end{align*}
## Results
::: {.panel-tabset}
## Significance
```{r, echo = FALSE, fig.width=7, fig.height=5.5, fig.align='center', cache = FALSE, results='asis'}
load("assets/crps_learning/bernstein_application_study_estimations+learnings_rev1.RData")
quantile_loss <- function(X, y, tau) {
t(t(y - X) * tau) * (y - X > 0) + t(t(X - y) * (1 - tau)) * (y - X < 0)
}
QL <- FCSTN * NA
for (k in 1:dim(QL)[1]) {
QL[k, , ] <- quantile_loss(FCSTN[k, , ], as.numeric(yoos), Qgrid)
}
## TABLE AREA
KK <- length(mnames)
TTinit <- 1 ## without first, as all comb. are uniform
RQL <- apply(QL[1:KK, -c(1:TTinit), ], c(1, 3), mean)
dimnames(RQL) <- list(mnames, Qgrid)
RQLm <- apply(RQL, c(1), mean, na.rm = TRUE)
# sort(RQLm - RQLm[K + 1])
##
qq <- apply(QL[1:KK, -c(1:TTinit), ], c(1, 2), mean)
# t.test(qq[K + 1, ] - qq[K + 3, ])
# t.test(qq[K + 1, ] - qq[K + 4, ])
library(xtable)
Pall <- numeric(KK)
for (i in 1:KK) Pall[i] <- t.test(qq[K + 1, ] - qq[i, ], alternative = "greater")$p.val
Mall <- (RQLm - RQLm[K + 1]) * 10000
Mout <- matrix(Mall[-c(1:(K + 3))], 5, 6)
dimnames(Mout) <- list(moname, mtname)
Pallout <- format(round(Pall, 3), nsmall = 3)
Pallout[Pallout == "0.000"] <- "<.001"
Pallout[Pallout == "1.000"] <- ">.999"
MO <- K
IDX <- c(1:K)
OUT <- t(Mall[IDX])
OUT.num <- OUT
class(OUT.num) <- "numeric"
xxx <- OUT.num
xxxx <- OUT
table <- OUT
table_col <- OUT
i.p <- 1
for (i.p in 1:MO) {
xmax <- -min(Mall) * 5 # max(Mall)
xmin <- min(Mall)
cred <- rev(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, .8, .5)) # , .5,0,0,0,1,1,1) ## red
cgreen <- rev(c(.5, .5, .55, .6, .65, .7, .75, .8, .85, .9, .95, 1, 1, .9)) # , .5,0,1,1,1,0,0) ## green
cblue <- rev(c(.55, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5)) # , .5,1,1,0,0,0,1) ## blue
crange <- c(xmin, xmax) ## range
## colors in plot:
fred <- round(approxfun(seq(crange[1], crange[2], length = length(cred)), cred)(pmin(xxx[, i.p], xmax)), 3)
fgreen <- round(approxfun(seq(crange[1], crange[2], length = length(cgreen)), cgreen)(pmin(xxx[, i.p], xmax)), 3)
fblue <- round(approxfun(seq(crange[1], crange[2], length = length(cblue)), cblue)(pmin(xxx[, i.p], xmax)), 3)
tmp <- format(round(xxx[, i.p], 3), nsmall = 3)
xxxx[, i.p] <- paste("\\cellcolor[rgb]{", fred, ",", fgreen, ",", fblue, "}", tmp, " {\\footnotesize (", Pallout[IDX[i.p]], ")}", sep = "")
table[, i.p] <- paste0(tmp, " (", Pallout[i.p], ")")
table_col[, i.p] <- rgb(fred, fgreen, fblue, maxColorValue = 1)
} # i.p
table_out <- kbl(table, align = rep("c", ncol(table)))
for (cols in 1:ncol(table)) {
table_out <- table_out %>%
column_spec(cols, background = table_col[, cols])
}
table_out %>%
kable_material()
```
```{r, echo = FALSE, fig.width=7, fig.height=5.5, fig.align='center', cache = FALSE, results='asis'}
MO <- 6
OUT <- Mout
OUT.num <- OUT
class(OUT.num) <- "numeric"
xxx <- OUT.num
xxxx <- OUT
i.p <- 1
table2 <- OUT
table_col2 <- OUT
for (i.p in 1:MO) {
xmax <- -min(Mall) * 5 # max(Mall)
xmin <- min(Mall)
cred <- rev(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, .8, .5)) # , .5,0,0,0,1,1,1) ## red
cgreen <- rev(c(.5, .5, .55, .6, .65, .7, .75, .8, .85, .9, .95, 1, 1, .9)) # , .5,0,1,1,1,0,0) ## green
cblue <- rev(c(.55, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5)) # , .5,1,1,0,0,0,1) ## blue
crange <- c(xmin, xmax) ## range
## colors in plot:
fred <- round(approxfun(seq(crange[1], crange[2], length = length(cred)), cred)(pmin(xxx[, i.p], xmax)), 3)
fgreen <- round(approxfun(seq(crange[1], crange[2], length = length(cgreen)), cgreen)(pmin(xxx[, i.p], xmax)), 3)
fblue <- round(approxfun(seq(crange[1], crange[2], length = length(cblue)), cblue)(pmin(xxx[, i.p], xmax)), 3)
tmp <- format(round(xxx[, i.p], 3), nsmall = 3)
xxxx[, i.p] <- paste("\\cellcolor[rgb]{", fred, ",", fgreen, ",", fblue, "}", tmp, " {\\footnotesize (", Pallout[K + 3 + 5 * (i.p - 1) + 1:5], ")}", sep = "")
table2[, i.p] <- paste0(tmp, " (", Pallout[K + 3 + 5 * (i.p - 1) + 1:5], ")")
table_col2[, i.p] <- rgb(fred, fgreen, fblue, maxColorValue = 1)
} # i.p
table_out2 <- kableExtra::kbl(table2, align = rep("c", ncol(table2)))
for (cols in 1:ncol(table2)) {
table_out2 <- table_out2 %>%
column_spec(1 + cols,
background = table_col2[, cols]
)
}
table_out2 %>%
kable_material() %>%
column_spec(1, bold = T)
```
## QL
```{r, echo = FALSE, fig.width=13, fig.height=5.5, fig.align='center', cache = FALSE}
##### Performance across probabilities
M <- length(mnames)
Msel <- c(1:K, K + 1, K + 1 + 2 + 1:4 * 5 - 2) ## experts + naive + smooth
modnames <- mnames[Msel]
tCOL <- c(
"#E6CC00", "#CC6600", "#E61A1A", "#99004D", "#F233BF",
"#666666", "#0000CC", "#1A80E6", "#1AE680", "#00CC00"
)
t(RQL) %>%
as_tibble() %>%
select(Naive) %>%
mutate(Naive = 0) %>%
mutate(p = 1:99 / 100) %>%
pivot_longer(-p, values_to = "Loss differences") -> dummy
t(RQL) %>%
as_tibble() %>%
select(mnames[Msel]) %>%
mutate(p = 1:99 / 100) %>%
pivot_longer(!p & !Naive) %>%
mutate(`Loss differences` = value - Naive) %>%
select(-value, -Naive) %>%
rbind(dummy) %>%
mutate(
p = as.numeric(p),
name = stringr::str_replace(name, "-P-smooth", ""),
name = factor(name, levels = stringr::str_replace(mnames[Msel], "-P-smooth", ""), ordered = T),
`Loss differences` = `Loss differences` * 1000
) %>%
ggplot(aes(x = p, y = `Loss differences`, colour = name)) +
geom_line(linewidth = 1) +
theme_minimal() +
theme(
text = element_text(size = text_size),
legend.position = "bottom"
) +
xlab("Probability p") +
scale_color_manual(NULL, values = tCOL) +
guides(colour = guide_legend(nrow = 2, byrow = TRUE))
```
## Cumulative Loss Difference
```{r, echo = FALSE, fig.width=13, fig.height=5.5, fig.align='center', cache = FALSE}
DQL <- t(apply(apply(QL[1:KK, -c(1:TTinit), ], c(1, 2), mean), 1, cumsum))
rownames(DQL) <- mnames
t(DQL) %>%
as_tibble() %>%
select(Naive) %>%
mutate(
`Difference of cumulative loss` = 0,
Date = ytime[-c(1:(TT + TTinit + 1))],
name = "Naive"
) %>%
select(-Naive) -> dummy
data <- t(DQL) %>%
as_tibble() %>%
select(mnames[Msel]) %>%
mutate(Date = ytime[-c(1:(TT + TTinit + 1))]) %>%
pivot_longer(!Date & !Naive) %>%
mutate(`Difference of cumulative loss` = value - Naive) %>%
select(-value, -Naive) %>%
rbind(dummy) %>%
mutate(
name = stringr::str_replace(name, "-P-smooth", ""),
name = factor(name, levels = stringr::str_replace(mnames[Msel], "-P-smooth", ""))
)
data %>%
ggplot(aes(x = Date, y = `Difference of cumulative loss`, colour = name)) +
geom_line(size = 1) +
theme_minimal() +
theme(
text = element_text(size = text_size),
legend.position = "bottom"
) +
scale_color_manual(NULL, values = tCOL) +
guides(colour = guide_legend(nrow = 2, byrow = TRUE))
```
## Weights (BOAG P-Smooth)
```{r, echo = FALSE, fig.width=13, fig.height=5.5, fig.align='center', cache = FALSE}
load("assets/crps_learning/weights_data.RData")
weights_data %>%
ggplot(aes(Date, p, fill = w)) +
geom_raster(interpolate = TRUE) +
facet_grid(Mod ~ .) +
theme_minimal() +
theme(
plot.margin = unit(c(0.2, 0.2, 0.2, 0.2), "cm"),
text = element_text(size = text_size),
legend.key.height = unit(0.9, "inch")
) +
ylab("p") +
scale_fill_gradientn(
limits = c(0, 1),
colours = colseq,
breaks = seq(0, 1, 0.2)
) +
scale_x_date(expand = c(0, 0))
```
## Weights (Last)
```{r, echo = FALSE, fig.width=13, fig.height=5.5, fig.align='center', cache = FALSE}
load("assets/crps_learning/weights_example.RData")
weights %>%
ggplot(aes(x = p, y = weights, col = Model)) +
geom_line(size = 1.5) +
theme_minimal() +
theme(
plot.margin = unit(c(0.2, 0.3, 0.2, 0.2), "cm"),
text = element_text(size = text_size),
legend.position = "bottom",
legend.title = element_blank(),
panel.spacing = unit(1.5, "lines")
) +
scale_color_manual(NULL, values = tCOL[1:K]) +
facet_grid(. ~ K)
```
::::
## Wrap-Up
:::: {.columns}
::: {.column width="48%"}
Potential Downsides:
- Pointwise optimization can induce quantile crossing
- Can be solved by sorting the predictions
Upsides:
- Pointwise learning outperforms the Naive solution significantly
- Online learning is much faster than batch methods
- Smoothing further improves the predictive performance
- Asymptotically not worse than the best convex combination
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
Important:
- The choice of the learning rate is crucial
- The loss function has to meet certain criteria
The [`r fontawesome::fa("github")` profoc](https://profoc.berrisch.biz/) R Package:
- Implements all algorithms discussed above
- Is written using RcppArmadillo `r fontawesome::fa("arrow-right", fill ="#000000")` its fast
- Accepts vectors for most parameters
- The best parameter combination is chosen online
- Implements
- Forgetting, Fixed Share
- Different loss functions + gradients
:::
::::
:::: {.notes}
Execution Times:
T = 5000
Opera:
Ml-Poly > 157 ms
Boa > 212 ms
Profoc:
Ml-Poly > 17
BOA > 16
## Columns Template
:::: {.columns}
::: {.column width="48%"}
Baz
:::
::: {.column width="2%"}
:::
::: {.column width="48%"}
foo
:::
::::
## Paneltabset Template
::: {.panel-tabset}
## Baz
Bar
## Bam
Foo
::::
# References
```{r refs1, echo=FALSE, results="asis"}
PrintBibliography(my_bib, .opts = list(style = "text"))
```
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