Algorithms

Methods introduction

TFB evaluated a diverse range of methods, including statistical learning, machine learning, and deep learning methods.

Statistical Learning (SL): ARIMA, ETS, Kalman Filter (Kalman), and VAR

Machine Learning(ML): XGB Model (XGB) , Linear Regression (LR), and Random Forest (RF)

Deep Learning (DL):

  • RNN-based models (RNN)
  • CNN-based models (MICN, TimesNet, and TCN)
  • MLP-based models (NLinear, DLinear, TiDE, N-HiTS, and N-BEATS)
  • Transformer-based models (PatchTST, Crossformer, and FEDformer, Non-stationary Transformer (Stationary), Informer, and Triformer)
  • Model-Agnostic models (FiLM)
Categorization of comparison methods

Pipeline introduction

The below figure provides a visual overview of TFB’s pipeline.

  • The data layer is a repository of univariate and multivariate time series from diverse domains, structured according to their distinct characteristics, frequencies, and sequence lengths. The data is uniformly according to a standardized format.
  • The method layer supports embedding statistical learning, machine learning and deep learning methods. Additionally, TFB is designed to be compatible with any third-party TSF library, such as Darts, TSlib. Users can easily integrate forecasting methods implemented in third-party libraries into TFB by writing a simple Universal Interface, facilitating fair comparisons.
  • The evaluation layer offers support for a diverse range of evaluation strategies and metrics. And it also covers evaluation metrics found in other studies and enables the use of customized metrics for a more comprehensive assessment of method performance.
  • The reporting layer encompasses a logging system for tracking information, enabling the capture of experimental settings to enable traceability.
TFB Pipeline