diff --git a/.gitignore b/.gitignore index 66e7e48..10e3b85 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,11 @@ # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 +.env +__pycache__/ +.RData +.Rhistory + # User-specific stuff .idea/ diff --git a/code/new_realtime/.RData b/code/new_realtime/.RData deleted file mode 100644 index dbeff96..0000000 Binary files a/code/new_realtime/.RData and /dev/null differ diff --git a/code/new_realtime/.Rhistory b/code/new_realtime/.Rhistory deleted file mode 100644 index 597ab21..0000000 --- a/code/new_realtime/.Rhistory +++ /dev/null @@ -1,11 +0,0 @@ -install.package("cpm") -install.packages("cpm") -install.packages("cpm") -install.packages("signal", "zoo", "pracma") -install.packages("signal", "zoo", "pracma") -install.packages("signal", "pracma") -install.packages("signal") -install.packages("signal") 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non-padding-implementation by using a dedicated feature +% show, that a different loss / temporal loss wheighting or position aware loss could help +% show, the problem of the model using its past targets, even though they are not observable and how I dealt with this -> explain and use exogenous +% introduce change of quantiles to single variable output and why, quantiles loss other than .5 was almost always just 0 or 1, so no significant predicitve quality in this, and this only makes predictions harder for model +% explain why class imablances are there, and what I did to counteract this, also explain, why special weighting is useful for something like the pregnancy risk prediction +% show the work that needed to be done for inference -> padding values for future observables + + +Notes for training: + - one head 512 hidden size make very "smoothed" out curves, which result in ver conservative predictions + +Intermediate training results: +- first full training: + - first feature config with ov over and fertility did not show much promise, as it detected the ov too late, essentially when it was already over + - either there is no patterns detectable in advance, or there are not enough features yet + - soft of "soft ceiling" at 0.3 loss, many models get there quickly and then "bounce" around \subsection{Time-Series Modeling Approach}\label{subsec:time-series_modeling_approach} \subsubsection{Limitations of Traditional Time-Series Models}\label{subsubsec:limitations_of_traditional_time-series_models}