| Summary: | Abstract Time-series forecasting is a practical goal in many areas of science and engineering. Common approaches for forecasting future events often rely on highly parameterized or black-box models. However, these are associated with a variety of drawbacks including critical model assumptions, uncertainties in their estimated input hyperparameters, and computational cost. All of these can limit model selection and performance. Here, we introduce a learning algorithm that avoids these drawbacks. A variety of data types including chaotic systems, macroeconomic data, wearable sensor recordings, and population dynamics are used to show that Forecasting through Recurrent Topology (FReT) can generate multi-step-ahead forecasts of unseen data. With no free parameters or even a need for computationally costly hyperparameter optimization procedures in high-dimensional parameter space, the simplicity of FReT offers an attractive alternative to complex models where increased model complexity may limit interpretability/explainability and impose unnecessary system-level computational load and power consumption constraints.
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