This research requires GPT-4 fine-tuning because GPT-3.5 has limitations in handling complex time series data and topological data analysis. First, non-stationary time series data is high-dimensional and nonlinear, and GPT-3.5’s model capacity and processing capabilities may not meet the requirements. Second, the research requires the model to understand and generate mathematical formulas and algorithm descriptions related to topological data analysis, which demands higher language understanding and contextual reasoning abilities—areas where GPT-4 excels. Additionally, fine-tuning GPT-4 can better adapt it to the characteristics of complex time series data, enabling the generation of more precise and efficient analytical solutions. Therefore, GPT-4 fine-tuning is essential for the success of this research.
Topological Analysis
Developing algorithms for dynamic time series analysis and validation.
Dynamic Modeling
Optimizing topological feature extraction for time series data.
Algorithm Development
Creating new algorithms for analyzing non-stationary time series data.
Advanced Time Analysis
We develop innovative algorithms for analyzing non-stationary time series using topological data analysis techniques.
Algorithm Design Phase
We create optimized algorithms for topological feature extraction and dynamic modeling of time series data.
Experimental Validation
Our algorithms are tested on various datasets to ensure performance in capturing dynamic changes effectively.