From 2b22506fb83799f6b948c73607713becc7211717 Mon Sep 17 00:00:00 2001 From: Jonathan Berrisch Date: Sun, 15 Jun 2025 11:55:33 +0200 Subject: [PATCH] Update slides --- index.html | 665 ++++++++++++++++++++++++++++++++++------------------- 1 file changed, 431 insertions(+), 234 deletions(-) diff --git a/index.html b/index.html index 7321ad6..61a0edd 100644 --- a/index.html +++ b/index.html @@ -934,7 +934,7 @@ position: absolute; top: 0; left: 0; opacity: 0; -transform: translate(-50%, -50%); +transform: translate(-30%, 0%); transition: opacity 0.4s ease-in-out; z-index: 99; } @@ -25879,12 +25879,11 @@ Berrisch, J. ( void main(){ Rcpp::Timer timer; - Rcpp::Timer::ScopedTimer _(timer, "ST"); - - timer.tic(); - // Some more code - timer.toc(); -} // ScopedTimer will stop automatically + + timer.tic(); + // Code to be timed + timer.toc(); +}
@@ -25911,6 +25910,10 @@ Berrisch, J. (
  • Vertical aggregation
    • +
  • Combining at an angle +
    • @@ -25923,7 +25926,7 @@ Berrisch, J. (
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    @@ -27024,221 +27234,205 @@ Y_{t} = \mu + Y_{t-1} + \varepsilon_t \quad \text{with} \quad \varepsilon_t = \
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    @@ -27267,19 +27461,19 @@ Y_{t} = \mu + Y_{t-1} + \varepsilon_t \quad \text{with} \quad \varepsilon_t = \

    Using non equidistant knots in profoc is straightforward:

    -
    mod <- online(
-  y = Y,
-  experts = experts,
-  tau = 1:99 / 100,
-  b_smooth_pr = list(
-    knots = 9,
-    mu = 0.3,
-    sigma = 1,
-    nonc = 0,
-    tailweight = 1,
-    deg = 3
-  )
-)
    +
    mod <- online(
+  y = Y,
+  experts = experts,
+  tau = 1:99 / 100,
+  b_smooth_pr = list(
+    knots = 9,
+    mu = 0.3,
+    sigma = 1,
+    nonc = 0,
+    tailweight = 1,
+    deg = 3
+  )
+)

    Basis specification b_smooth_pr is internally passed to make_basis_mats().

    Profoc adjusts penatly and \(\lambda\)

    @@ -28236,6 +28430,9 @@ Li, Z., & Cao, J. (2022). General p-splines for non-uniform b-splines. a
    Marcjasz, G., Narajewski, M., Weron, R., & Ziel, F. (2023). Distributional neural networks for electricity price forecasting. Energy Economics, 125, 106843. DOI: 10.1016/j.eneco.2023.106843
    +
    +Taylor, J.W., & Meng, X. (2023). Angular combining of forecasts of probability distributions. arXiv Preprint arXiv:2305.16735. DOI: 10.48550/arXiv.2305.16735 +
    Wintenberger, O. (2017). Optimal learning with bernstein online aggregation. Machine Learning, 106(1), 119–141. DOI: 10.1007/s10994-016-5592-6
    @@ -29421,10 +29618,10 @@ window.QuartoSupport = function () { });