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expose/src/expose.tex

@@ -13,7 +13,7 @@
 % Document
 \begin{document}
 
-    \title{Developing a Data-Driven Framework for Advanced Time-Series Analysis:
+    \title{Developing a Meta-Framework for Advanced Time-Series Analysis:
     Integrating Transformer Architectures with Health Data}
     \author{Alexander Blank}
     \date{October 2024}
@@ -21,19 +21,17 @@
 
 
     \section{Introduction}\label{sec:introduction}
-    Time-series data is a cornerstone of many fields, including finance, health, and climate science,
-    where it plays a crucial role in forecasting, anomaly detection, and classification tasks.
-    Predictive models based on time-series data are widely used in applications such as weather forecasting,
-    stock price prediction, health monitoring, and security systems.
-    In recent years, transformer-based architectures have shown significant improvements in time-series analysis,
-    outperforming traditional models like LSTMs in capturing complex temporal patterns.
-    As the field of time-series analysis rapidly evolves, new models and architectures are continually developed to
-    meet the growing demands of high-frequency data analysis.
+    Time-series data is critical in fields like finance, health, and climate science, where understanding
+    temporal patterns and forecasting future trends, which rely on accurate modeling, are essential.
+    Recently, transformer-based architectures have demonstrated improved performance over traditional models like
+    LSTMs and GRUs, particularly in capturing long-term dependencies and complex temporal patterns.
 
-    This exposé outlines the development of a modular, data-driven framework designed to integrate advanced transformer
-    architectures for time-series analysis.
-    To demonstrate its capabilities, the framework will be applied in a case study focused on high-frequency
-    ovulation cycle health data, showcasing its adaptability across various model architectures.
+    This exposé outlines a project that aims to develop a meta-framework that builds upon existing tools like
+    PyTorch-Forecasting, extending them to handle the unique challenges of high-frequency health data.
+    The framework will provide a configurable pipeline for dataset creation, model training and evaluation, allowing
+    easy adaptions fro various time-series tasks, and will introduce support for foundation models and fine-tuning.
+    A case study on ovulation cycle monitoring and analysis will demonstrate the framework's capabilities in
+    adaptability, performance, and interpretability.
 
 
     \section{Related Work}\label{sec:related-work}
@@ -166,3 +164,62 @@
 
 
 \end{document}
+
+Title:
+Developing a Meta-Framework for Configurable Time-Series Forecasting: Extending PyTorch Forecasting for Health Data
+
+1. Introduction
+Time-series data is critical in fields like finance, health, and climate science, where forecasting, anomaly detection, and classification tasks rely on accurate modeling. Recently, transformer-based architectures have demonstrated improved performance over traditional models like LSTMs in capturing long-term dependencies in time-series data.
+
+This project proposes the development of a meta-framework that builds upon existing tools like PyTorch Forecasting, extending them to handle the unique challenges of high-frequency health data. The framework will provide a configurable pipeline for dataset creation, model training, and evaluation, allowing easy adaptation to various time-series tasks, and will introduce support for foundation models. A case study on ovulation cycle monitoring will demonstrate the framework's adaptability and scalability.
+
+2. Related Work
+Traditional models like LSTMs have been widely used but often struggle with long-term dependencies in time-series data. Transformer-based architectures like the Temporal Fusion Transformer (TFT) and PatchTST have emerged as more scalable and interpretable alternatives.
+
+Frameworks such as PyTorch Lightning, Amazon’s GluonTS, and Meta’s Prophet provide tools for time-series analysis but often require significant customization and are limited in their flexibility for handling different data sources and tasks. PyTorch Forecasting, which includes implementations of models like TFT, is powerful but lacks a configurable pipeline for handling raw datasets, and it does not yet integrate with foundation models.
+
+This project aims to bridge these gaps by building a meta-framework that integrates PyTorch Forecasting, supports foundation models, and provides a configurable data pipeline tailored for health data.
+
+3. Research Questions and Objectives
+The primary objective is to develop a meta-framework that extends PyTorch Forecasting with a configurable pipeline and support for foundation models, specifically for health-related time-series data. The framework will be designed to handle high-frequency data like ovulation cycle monitoring and be adaptable for future use cases.
+
+Key research questions include:
+
+How can a configurable pipeline be designed to integrate data processing, model training, and evaluation for time-series forecasting?
+What challenges arise when applying transformer models to high-frequency health data, and how can these be addressed?
+How can a foundation model be pre-trained on health data and fine-tuned for specific tasks like ovulation cycle monitoring?
+4. Methodology
+The proposed meta-framework will consist of three main components: dataset creation, model training, and evaluation/deployment. It will leverage PyTorch Forecasting while extending its capabilities with custom data preprocessing and support for foundation models.
+
+4.1 Dataset Creation
+The dataset creation phase will involve designing a pipeline that extracts data from sources like MongoDB, processes it using NumPy and Pandas, and outputs time-series datasets ready for forecasting models. The pipeline will handle the specific needs of high-frequency health data (e.g., handling missing values, feature engineering).
+
+4.2 Model Training
+Two models will be used for the case study:
+
+Temporal Fusion Transformer (TFT) for interpretability.
+PatchTST for high-performance time-series forecasting.
+The training pipeline will be highly configurable, allowing users to define model parameters, preprocessing steps, and evaluation metrics via configuration files.
+
+4.3 Foundation Model Integration
+In addition to existing models, the framework will allow for the training and fine-tuning of a foundation model, pre-trained on health-related time-series data and adapted for specific tasks like ovulation cycle monitoring.
+
+4.4 Model Evaluation and Deployment
+Evaluation metrics will be chosen based on the case study’s results, focusing on both performance and interpretability. If successful, the system will include deployment options for real-world application.
+
+5. Case Study
+The case study will evaluate the framework using ovulation cycle data (recorded every 5 minutes, 288 measurements per day). The goal is to demonstrate how the framework can manage high-frequency health data, apply transformer-based models like TFT for interpretability, and optimize performance using PatchTST.
+
+This case study will also show how a foundation model can be fine-tuned for specific health tasks, providing insights into how these models can generalize across different types of physiological data.
+
+6. Timeline
+Given the exploratory nature of the project, the timeline will focus on key milestones rather than rigid deadlines:
+
+Initial Exploration: Refining the framework design and integrating PyTorch Forecasting (Month 1-2).
+Dataset Creation: Developing the data pipeline and preprocessing health data (Month 3).
+Model Training: Implementing and training TFT and PatchTST models (Month 4).
+Foundation Model Integration: Pretraining and fine-tuning the foundation model (Month 5).
+Evaluation and Conclusion: Assessing model performance and writing up results (Month 6).
+7. Conclusion
+This project will deliver a configurable meta-framework for time-series forecasting that extends PyTorch Forecasting by integrating health data preprocessing, flexible model training, and foundation model support. The case study on ovulation cycle monitoring will demonstrate its applicability and scalability, potentially leading to broader use in health-related time-series forecasting.
+