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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) International Journal of Informatics Engineering and Computing
I Wayan Ordiyasa
Universitas Respati Yogyakarta
Computer Science & IT Engineering

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Journal : international journal of informatics engineering and computing

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Geometric Structured Trend Tunneling: A Hybrid VARIMA-SVR Model for Synthetic Stock Time Series Generation I Wayan Ordiyasa; Ahmad Sahal; Gladies Serren Kutani
International Journal of Informatics Engineering and Computing Vol. 3 No. 1 (2026): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/g9r7y321

Abstract

This study presents a novel hybrid framework, Geometric Structured Trend Tunneling (GSTT), for generating synthetic multivariate time series data, specifically applied to stock price data of Medco Energi Internasional (MEDC), a major player in Indonesia’s energy sector. The proposed model integrates the statistical power of Vector Autoregressive Integrated Moving Average (VARIMA) with the nonlinear pattern-capturing capability of Support Vector Regression (SVR), enabling high-fidelity reconstruction of temporal structures and feature dependencies in financial datasets. The dataset used spans over two decades (2003–2024) and includes core trading indicators such as Open, High, Low, and Close prices. Experimental results demonstrate that GSTT achieves excellent performance across multiple evaluation metrics, including MAE, RMSE, R², and KS tests, while preserving inter-feature correlations and distributional fidelity. Visual comparisons and descriptive statistics further confirm the model’s ability to replicate realistic market behavior. Unlike deep generative models such as GANs or VAEs, GSTT offers a more interpretable, stable, and computationally efficient alternative for financial data augmentation, simulation, and robust AI training. This work contributes a scalable solution for addressing data scarcity in financial modeling, with potential applications in backtesting, risk analysis, and algorithmic trading simulations.
Optimizing Sunspot Forecasts: An In-Depth Analysis of the ConcaveLSTM Model I Wayan Ordiyasa; Mohammad Diqi; Marselina Endah Hiswati; Aulia Fadillah Wani Wandani
International Journal of Informatics Engineering and Computing Vol. 2 No. 1 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v2i1.103

Abstract

This work examines how effectively the ConcaveLSTM model can forecast sunspot numbers, recognizing their importance in space weather. The model addresses the complex and changing sunspot characteristics to improve forecasting accuracy. By comparing different model variations, this research identifies optimal combinations of input steps and LSTM units that enhance forecast performance while avoiding overfitting. The study showcases the capability of specific architectures concerning detail versus computational cost, using evaluation metrics such as RMSE, MAE, MAPE, and R2. Considering factors like limited data availability and the complexity of solar phenomena, the ConcaveLSTM model could be a valuable tool for predicting solar activity. This research advances understanding of space weather forecasting through machine learning and offers guidance for further model development and future investigations.
Co-Authors Ahmad Wakhid Aulia Fadillah Wani Wandani Dian Rhesa Rahmayanti Gladies Serren Kutani Ida Hafizah Lila Retnani Utami Marselina Endah Hiswati Marselina Endah Hiswati Mohammad Diqi Mohammad Diqi Putra Wanda Rainbow Tambunan Sahal, Ahmad Supardi RS Umar Basuki Wijaya, Nurhadi