Daily Forecasting for Annual Time Series Datasets Using Similarity-Based Machine Learning Methods: A Case Study in the Energy Market

TL;DR

This paper introduces a novel machine learning framework that converts annual macroeconomic indicators into high-frequency daily forecasts, demonstrated through energy security index prediction using similarity measures and XGBoost.

Contribution

The study presents a new approach combining time series similarity and machine learning to enable daily energy security forecasting from annual data.

Findings

Volume Brent is the most suitable proxy for energy security.

Model achieved R squared of 0.981 on training and 0.945 on testing.

Forecast captures short-term fluctuations with specific peak and decline patterns.

Abstract

The policy environment of countries changes rapidly, influencing macro-level indicators such as the Energy Security Index. However, this index is only reported annually, limiting its responsiveness to short-term fluctuations. To address this gap, the present study introduces a daily proxy for the Energy Security Index and applies it to forecast energy security at a daily frequency.The study employs a two stage approach first, a suitable daily proxy for the annual Energy Security Index is identified by applying six time series similarity measures to key energy related variables. Second, the selected proxy is modeled using the XGBoost algorithm to generate 15 day ahead forecasts, enabling high frequency monitoring of energy security dynamics.As the result of proxy choosing, Volume Brent consistently emerged as the most suitable proxy across the majority of methods. The model demonstrated strong performance, with an R squared of 0.981 on the training set and 0.945 on the test set, and acceptable error metrics . The 15 day forecast of Brent volume indicates short term fluctuations, with a peak around day 4, a decline until day 8, a rise near day 10, and a downward trend toward day 15, accompanied by prediction intervals.By integrating time series similarity measures with machine learning based forecasting, this study provides a novel framework for converting low frequency macroeconomic indicators into high frequency, actionable signals. The approach enables real time monitoring of the Energy Security Index, offering policymakers and analysts a scalable and practical tool to respond more rapidly to fast changing policy and market conditions, especially in data scarce environments.