started tft part in background

thesis/sections/methodology.tex

@@ -36,12 +36,17 @@ pregnancies, which would make the prediction task ambiguous.
 
 %TODO: show that a model without marker context and one with them is trained, to show the impact of the markers
 
+%TODO: show that for initial testing and fine tuning the dataset was reduced to 10% of size to speed up early impressions of performance
+%TODO: show that initial feature set with only temperature as observable and only pregnancy "chance" as target
+% did not show promise on small 10% dataset, so the features got extended
 
 \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}
 \subsubsection{Temporal Fusion Transformer}\label{subsubsec:temporal_fusion_transformer}
 \subsubsection{Input \& Output Modeling}\label{subsubsec:input_output_modeling}
 
+% build in the probability curve for the pregnancy chance
+
 \subsection{Model Training}\label{subsec:model_training}
 \subsubsection{Training Setup}\label{subsubsec:training_setup}
 \subsubsection{Hyperparameter Tuning}\label{subsubsec:hyperparameter_tuning}