Weight decay

Q: How deeply does Time Series Forecasting Using Foundation Models cover weight decay?

Defines the L2 penalty and shows a ridge parameter, but stops short of deriving its optimization behavior.

As explained in Time Series Forecasting Using Foundation Models

L2 regularization is a penalty method that discourages large model weights by adding their squared magnitudes to the optimization objective.

Weight decay is an L2 regularization technique that discourages large model weights by adding their squared magnitudes to the optimization objective. section 7.2.3

In the forecasting workflow, this idea appears through ridge regression, which estimates how exogenous features contribute to a TimesFM forecast. The implementation exposes a ridge setting alongside the covariate forecast mode. section 7.2.3

Configuring the ridge setting

A covariate forecast uses xreg_mode="xreg + timesfm" and explicitly passes ridge=0.0, making the regularization setting visible in the prediction call.

How Time Series Forecasting Using Foundation Models covers it

Cover of Time Series Forecasting Using Foundation Models

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Also explained in

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Grokking Machine LearningRidge regression is a linear regression technique that uses an L2 coefficient penalty, shrinking coefficients toward zero without usually making them zero.
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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIL2 regularization adds a penalty proportional to the squared values of model parameters to constrain model complexity.
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Deep Learning with PyTorch, Second EditionAn optimization method that shrinks parameters in proportion to their current values, generally distinct from L2 regularization for adaptive optimizers.
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Ensemble Methods for Machine LearningL2 regularization penalizes the squared magnitude of model parameters or other model complexity measures in an optimization objective.
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Deep Learning with R, Third EditionL2 regularization is a regularization method that adds a penalty proportional to the squared values of model parameters.
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Deep Learning with Python, Third Edition
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Machine Learning Algorithms in DepthA regularization method that adds a penalty proportional to the squared magnitude of model parameters to discourage large weights.
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Causal AIL2 regularization is a regularization method that penalizes the sum of squared parameter values.

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Related concepts

regularizationkind of · Regularization is a modeling technique that constrains or penalizes solutions during fitting or optimization to discourage undesirable complexity or behavior and improve generalization or estimation.
overfittingaddresses · Overfitting is a modeling condition in which a model fits patterns or idiosyncrasies in its training data too closely and consequently generalizes poorly to new data.
ridge regressionused by · Ridge regression is linear regression that applies an L2 penalty to coefficient magnitudes, shrinking estimates toward zero to reduce overfitting.
l2 normuses · The L2 norm is the nonnegative Euclidean magnitude of a vector, equal to the square root of the sum of its components’ squares, and to absolute value for a scalar.
AlexNetused by · AlexNet is a convolutional neural network architecture designed for image classification and recognized for its landmark performance in large-scale image recognition.

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