TimeSynth: A Framework for Uncovering Systematic Biases in Time Series Forecasting

• URL: http://arxiv.org/abs/2602.11413v1
• Date: Wed, 11 Feb 2026 22:31:29 GMT
• Title: TimeSynth: A Framework for Uncovering Systematic Biases in Time Series Forecasting
• Authors: Md Rakibul Haque, Vishwa Goudar, Shireen Elhabian, Warren Woodrich Pettine,
• Abstract summary: Time Synth is a structured framework that emulates key properties of real world time series.<n>We evaluate four model families Linear, Multi Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Transformers.
• Score: 0.9332987715848716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series forecasting is a fundamental tool with wide ranging applications, yet recent debates question whether complex nonlinear architectures truly outperform simple linear models. Prior claims of dominance of the linear model often stem from benchmarks that lack diverse temporal dynamics and employ biased evaluation protocols. We revisit this debate through TimeSynth, a structured framework that emulates key properties of real world time series,including non-stationarity, periodicity, trends, and phase modulation by creating synthesized signals whose parameters are derived from real-world time series. Evaluating four model families Linear, Multi Layer Perceptrons (MLP), Convolutional Neural Networks (CNNs), and Transformers, we find a systematic bias in linear models: they collapse to simple oscillation regardless of signal complexity. Nonlinear models avoid this collapse and gain clear advantages as signal complexity increases. Notably, Transformers and CNN based models exhibit slightly greater adaptability to complex modulated signals compared to MLPs. Beyond clean forecasting, the framework highlights robustness differences under distribution and noise shifts and removes biases of prior benchmarks by using independent instances for train, test, and validation for each signal family. Collectively, TimeSynth provides a principled foundation for understanding when different forecasting approaches succeed or fail, moving beyond oversimplified claims of model equivalence.

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