Scalable spatiotemporal prediction with Bayesian neural fields
Project description
Bayesian Neural Fields for Spatiotemporal Prediction
This is not an officially supported Google product.
Spatially referenced time series (i.e., spatiotemporal) datasets are ubiquitous in scientific, engineering, and business-intelligence applications. This repository contains an implementation of the Bayesian Neural Field (BayesNF) a novel spatiotemporal modeling method that integrates hierarchical probabilistic modeling for accurate uncertainty estimation with deep neural networks for high-capacity function approximation.
Bayesian Neural Fields infer joint probability distributions over field values at arbitrary points in time and space, which makes the model suitable for many data-analysis tasks including spatial interpolation, temporal forecasting, and variography. Posterior inference is conducted using variationally learned surrogates trained via mini-batch stochastic gradient descent for handling large-scale data.
Installation
bayesnf
can be installed from the Python Package Index
(PyPI) using:
python -m pip install bayesnf
The typical install time is 1 minute. This software is tested on Python 3.11
with a standard Debian GNU/Linux setup. The large-scale experiments in
scripts/
were run using TPU v3-8 accelerators.
To run BayesNF locally on medium to large-scale data, a GPU is
required at minimum.
Documentation and Tutorials
Please visit https://google.github.io/bayesnf
Quick start
# Load a dataframe with "long" format spatiotemporal data.
df_train = pd.read_csv('chickenpox.5.train.csv',
index_col=0, parse_dates=['datetime'])
# Build a BayesianNeuralFieldEstimator
model = BayesianNeuralFieldMAP(
width=256,
depth=2,
freq='W',
seasonality_periods=['M', 'Y'],
num_seasonal_harmonics=[2, 10],
feature_cols=['datetime', 'latitude', 'longitude'],
target_col='chickenpox',
observation_model='NORMAL',
timetype='index',
standardize=['latitude', 'longitude'],
interactions=[(0, 1), (0, 2), (1, 2)])
# Fit the model.
model = model.fit(
df_train,
seed=jax.random.PRNGKey(0),
ensemble_size=ensemble_size,
num_epochs=num_epochs)
# Make predictions of means and quantiles on test data.
df_test = pd.read_csv('chickenpox.5.test.csv',
index_col=0, parse_dates=['datetime'])
yhat, yhat_quantiles = model.predict(df_test, quantiles=(0.025, 0.5, 0.975))
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