AutoCyclic: Deep Learning Optimizer for Time Series Data Prediction

Abstract

Time series prediction, a pivotal component in various domains, faces the intricate task of capturing complex patterns and dependencies within sequential data. Autocyclic Learning Rate (Auto-Cyclic) emerges as a groundbreaking solution, seamlessly integrating cosine cyclic learning rates with a meticulous emphasis on autocorrelation and variance. In the realm of time series forecasting, where longterm dependencies and dynamic patterns pose significant challenges, AutoCyclic represents a transformative approach. By dynamically adjusting learning rates based on the nuanced characteristics of time series data, AutoCyclic not only addresses issues such as underfitting and overfitting, but also provides exceptional robustness to outliers. Through rigorous evaluations on diverse datasets, including ETTm2, M4, and WindTurbine, AutoCyclic consistently outperforms traditional optimizers such as Adams Optimizer and Cosine Cyclic Learning Rate, showcasing the lowest Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) values. What sets AutoCyclic apart is not only its predictive accuracy, but also its astute exploitation of autocorrelation, which provides a unique advantage in discerning and leveraging inherent patterns in time series data. This intrinsic capability positions AutoCyclic as a transformative tool, redefining the landscape of time series forecasting methodologies and opening new dimensions in the utilization of autocorrelation for more insightful and accurate predictions.