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Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

Authors: Bryan Lim, Sercan Arik, Nicolas Loeff and Tomas Pfister

Paper link: https://arxiv.org/pdf/1912.09363.pdf

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.

Code Organisation

This repository contains the source code for the Temporal Fusion Transformer, along with the training and evaluation routines for the experiments described in the paper.

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.

Running Default Experiments

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.

Customising Scripts for New Datasets

To re-use the hyperparameter optimization scripts for new datasets, we need to add a new experiment -- which involves the creation of a new data formatter and config updates.

Step 1: Implement custom data formatter

First, create a new python file in data_formatters (e.g. example.py) which contains a data formatter class (e.g. ExampleFormatter). This should inherit base.GenericDataFormatter and provide implementations of all abstract functions. An implementation example can be found in volatility.py.

Step 2: Update configs.py

Add a name for your new experiement to the default_experiments attribute in expt_settings.configs.ExperimentConfig (e.g. example).

default_experiments = ['volatility', 'electricity', 'traffic', 'favorita', 'example']

Next, add an entry in data_csv_path mapping the experiment name to name of the csv file containing the data:

@property
  def data_csv_path(self):
    csv_map = {
        'volatility': 'formatted_omi_vol.csv',
        'electricity': 'hourly_electricity.csv',
        'traffic': 'hourly_data.csv',
        'favorita': 'favorita_consolidated.csv',
        'example': 'mydata.csv'  # new entry here!
    }

    return os.path.join(self.data_folder, csv_map[self.experiment])

Lastly, add your custom data formatter to the factory function:

def make_data_formatter(self):
    """Gets a data formatter object for experiment.

    Returns:
      Default DataFormatter per experiment.
    """

    data_formatter_class = {
        'volatility': data_formatters.volatility.VolatilityFormatter,
        'electricity': data_formatters.electricity.ElectricityFormatter,
        'traffic': data_formatters.traffic.TrafficFormatter,
        'example': data_formatters.example.ExampleFormatter, # new entry here!
    }

As an optional step, change the number of random search iterations if required:

@property
  def hyperparam_iterations(self):
    
    my_search_iterations=1000
    
    if self.experiment == 'example':
      return my_search_iterations
    else:
      return 240 if self.experiment == 'volatility' else 60

Step 3: Run training script

Full hyperparameter optimization can then be run as per the previous section, e.g.:

python3 -m script_hyperparam_opt example . yes yes