graph LR
Execution_Configuration["Execution & Configuration"]
Data_Management_Preprocessing["Data Management & Preprocessing"]
Experiment_Management_Framework["Experiment Management Framework"]
TimeMixer_Model_Core["TimeMixer Model Core"]
Evaluation_Metrics["Evaluation & Metrics"]
Execution_Configuration -- "Configures & Initiates" --> Experiment_Management_Framework
Data_Management_Preprocessing -- "Provides Data" --> Experiment_Management_Framework
Experiment_Management_Framework -- "Initializes & Trains Model" --> TimeMixer_Model_Core
TimeMixer_Model_Core -- "Returns Predictions" --> Experiment_Management_Framework
Experiment_Management_Framework -- "Applies Loss & Evaluates Performance" --> Evaluation_Metrics
Evaluation_Metrics -- "Provides Metrics" --> Experiment_Management_Framework
click Execution_Configuration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/TimeMixer/Execution_Configuration.md" "Details"
click Experiment_Management_Framework href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/TimeMixer/Experiment_Management_Framework.md" "Details"
click TimeMixer_Model_Core href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/TimeMixer/TimeMixer_Model_Core.md" "Details"
The TimeMixer project implements a robust, modular architecture for time series analysis, designed as an ML toolkit. Its primary data flow initiates from Execution & Configuration scripts, which hand over control to the Experiment Management Framework. This framework orchestrates the entire ML pipeline, fetching data from the Data Management & Preprocessing component, training and invoking the central TimeMixer Model Core, and leveraging the Evaluation & Metrics component for performance assessment. The TimeMixer Model Core itself is a sophisticated neural network that processes time series data through specialized decomposition and mixing layers. This clear separation of concerns into distinct components—data handling, experiment orchestration, core model logic, and evaluation—facilitates a highly maintainable, scalable, and extensible system, ideal for diverse time series forecasting, anomaly detection, classification, and imputation tasks.
Execution & Configuration [Expand]
The project's entry point, responsible for parsing command-line arguments, setting up configurations, and initiating the overall experiment flow.
Related Classes/Methods:
Handles the loading, transformation, normalization, and instance generation of various time series datasets, serving as the primary data source for all experiments.
Related Classes/Methods:
Experiment Management Framework [Expand]
The central orchestrator for machine learning experiments, managing the entire lifecycle including model building, training loops, validation, and testing across different time series tasks.
Related Classes/Methods:
exp/exp_basic.pyexp/exp_long_term_forecasting.pyexp/exp_short_term_forecasting.pyexp/exp_anomaly_detection.pyexp/exp_classification.pyexp/exp_imputation.py
TimeMixer Model Core [Expand]
The core neural network architecture of TimeMixer, implementing its unique decomposition, Past-Decomposable Mixing (PDM), and Future-Multipredictor Mixing (FMM) operations for time series processing. It internally utilizes various neural network utility layers.
Related Classes/Methods:
models/TimeMixer.pylayers/Autoformer_EncDec.pylayers/Embed.pylayers/StandardNorm.pylayers/AutoCorrelation.pylayers/SelfAttention_Family.py
Contains the necessary components for quantifying model performance, including various loss functions used during training and a suite of metrics for comprehensive evaluation.
Related Classes/Methods: