International Conference on Computer and Knowledge Engineering

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Real-Time Forecasting Using Mixed Frequency Time-Series Data

Authors :

Armin Khayati¹ Mohammad Taheri² Koorush Ziarati³

1- Computer Sci. & Eng. dept., Shiraz University, Shiraz, Iran, 2- Computer Sci. & Eng. dept., Shiraz University, Shiraz, Iran, 3- Computer Sci. & Eng. dept., Shiraz University, Shiraz, Iran,

Keywords :

Mixed-frequency data forecasting،Neural networks،Time series analysis،Real-time forecasting،High-frequency data،Deep learning

Abstract :

This paper addresses the challenge of forecasting time-series data, including mixed-frequency data. The proposed method integrates high-frequency features with low-frequency outputs by capturing essential dynamics and enhancing real-time decision-making. A novel objective is introduced to learn and ensemble a set of models with identical structures and parameters. By utilizing this base model within a larger framework, the method processes lagged inputs from various frequencies to generate accurate predictions. The model's performance was evaluated using three datasets: the Electricity Transformer Dataset, Individual Household Electric Power Consumption, and the Max Planck Weather Dataset. Results demonstrate that the proposed method outperforms state-of-the-art models in both high-frequency and low-frequency forecasting scenarios. Bayesian Hyperparameter Optimization was employed to fine-tune the model, further improving its performance with reduced computational effort. This approach opens new avenues for handling mixed-frequency data in various forecasting applications.