aryan-jadon / Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow

This repository contains the implementation of paper Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting with different loss functions in Tensorflow. We have compared 14 regression loss functions performance on 4 different datasets.

Regression Loss Functions Performance Evaluation in Time Series Forecasting using Temporal Fusion Transformers

This repository contains the implementation of paper Temporal Fusion Transformers for Interpretable 
Multi-horizon Time Series Forecasting with different loss functions in Tensorflow. 
We have compared 14 regression loss functions performance on 4 different datasets. 
Summary of experiment with instructions on how to replicate this experiment can be find below.

About Temporal Fusion Transformers

Paper Link: https://arxiv.org/pdf/1912.09363.pdf Authors: Bryan Lim, Sercan Arik, Nicolas Loeff and Tomas Pfister

Abstract - Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i.e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target. While several deep learning models have been proposed for multi-step prediction, they typically comprise black-box models which do not account for the full range of inputs present in common scenarios. In this paper, we introduce the Temporal Fusion Transformer (TFT) -- a novel attention-based architecture which combines high-performance multi-horizon forecasting with interpretable insights into temporal dynamics. To learn temporal relationships at different scales, the TFT utilizes recurrent layers for local processing and interpretable self-attention layers for learning long-term dependencies. The TFT also uses specialized components for the judicious selection of relevant features and a series of gating layers to suppress unnecessary components, enabling high performance in a wide range of regimes. On a variety of real-world datasets, we demonstrate significant performance improvements over existing benchmarks, and showcase three practical interpretability use-cases of TFT.

Majority of this repository work is taken from - https://github.com/google-research/google-research/tree/master/tft.

Experiments Summary and Our Paper

Cite Our Paper

@misc{jadon2022comprehensive,
      title={A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting}, 
      author={Aryan Jadon and Avinash Patil and Shruti Jadon},
      year={2022},
      eprint={2211.02989},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Paper Link - https://arxiv.org/abs/2211.02989

Replicating this Repository and Experiments

Downloading Data and Running Default Experiments

The key modules for experiments are organised as:

• data_formatters: Stores the main dataset-specific column definitions, along with functions for data transformation and normalization. For compatibility with the TFT, new experiments should implement a unique GenericDataFormatter (see base.py), with examples for the default experiments shown in the other python files.
• expt_settings: Holds the folder paths and configurations for the default experiments,
• libs: Contains the main libraries, including classes to manage hyperparameter optimisation (hyperparam_opt.py), the main TFT network class (tft_model.py), and general helper functions (utils.py)

Scripts are all saved in the main folder, with descriptions below:

• run.sh: Simple shell script to ensure correct environmental setup.
• script_download_data.py: Downloads data for the main experiment and processes them into csv files ready for training/evaluation.
• script_train_fixed_params.py: Calibrates the TFT using a predefined set of hyperparameters, and evaluates for a given experiment.
• script_hyperparameter_optimisation.py: Runs full hyperparameter optimization using the default random search ranges defined for the TFT.

Our four default experiments are divided into volatility, electricity, traffic, andfavorita. To run these experiments, first download the data, and then run the relevant training routine.

Step 1: Download data for default experiments

To download the experiment data, run the following script:

python3 -m script_download_data $EXPT $OUTPUT_FOLDER

where $EXPT can be any of {volatility, electricity, traffic, favorita}, and $OUTPUT_FOLDER denotes the root folder in which experiment outputs are saved.

Step 2: Train and evaluate network

To train the network with the optimal default parameters, run:

python3 -m script_train_fixed_params $EXPT $OUTPUT_FOLDER $USE_GPU 

where $EXPT and $OUTPUT_FOLDER are as above, $GPU denotes whether to run with GPU support (options are {'yes' or'no'}).

For full hyperparameter optimization, run:

python3 -m script_hyperparam_opt $EXPT $OUTPUT_FOLDER $USE_GPU yes

where options are as above.

Running Experiments with Loss Functions

Move the Downloaded Dataset to their Respective Experiment Folder

Run Experiment Script

python3 running_experiments.py