Combined Time Series Research Articles

Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction

Automated EEG classification algorithms for seizures can facilitate the clinical diagnosis of epilepsy, enabling more expedient and precise classification. However, existing EEG signal preprocessing methods oriented towards artifact removal and signal enhancement have demonstrated suboptimal accuracy and robustness. In response to this challenge, we propose an Adaptive Dual-Modality Learning Model (ADML) for epileptic seizure prediction by combining time series imaging with Transformer-based architecture. Our approach effectively captures both temporal dependencies and spatial relationships in EEG signals through a specialized attention mechanism. Evaluated on the CHB-MIT and Bonn datasets, our method achieves 98.7% and 99.2% accuracy, respectively, significantly outperforming existing approaches. The model demonstrates strong generalization capability across datasets while maintaining computational efficiency. Cross-dataset validation confirms the robustness of our approach, with consistent performance above 96% accuracy. These results suggest that our dual-modality approach provides a reliable and practical solution for clinical epileptic seizure prediction.

Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer

Soil texture analysis is critical for advancing agricultural productivity, ensuring environmental sustainability, and maintaining ecosystem balance. Traditional sedimentation-based methods, such as the hydrometer technique, are fast and practical but prone to inaccuracies due to the effects of water-soluble substances. This study focuses on the practical framework of integrating pH (potential of hydrogen) and EC (electrical conductivity), as indicators of dissolved substances that influence soil texture estimation. Using the Ultrasound Penetration-based Digital Soil Texture Analyzer (USTA), this research combined ultrasound time series data with pH and EC measurements to predict sand, silt, and clay ratios through machine learning methods—support vector regression (SVR), Random Forest (RF), and multi-layer perceptron neural network (MLPNN). Simulations showed that RF yielded the best results, improving R2 values to 0.52, 0.33, and 0.31 for sand, silt, and clay, respectively. The enhanced model performance demonstrates the viability of integrating pH and EC with advanced machine learning techniques to improve soil texture analysis accuracy. These findings suggest that automated systems like USTA, with modular pH and EC sensors, can provide cost-effective, efficient alternatives to traditional methods, offering practical implications for soil management and agricultural optimization.

Time series data analysis and association rule mining in financial recommendation systems using Hadoop and Spark

Increasing amounts of financial data demand sophisticated analytics to develop sound recommendation models. This article discusses combining time series analysis and association rule mining for big data in Hadoop and Spark to enrich financial product recommendation engines. The paper is an integrated analysis of two types of prediction algorithms: AutoRegressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks to forecast user behavior and demand for financial services in the future from transactional history. The ARIMA model is used as the default while the LSTM model is used to represent non-linear dependencies and give a more dynamic forecast. association rule mining in particular the Apriori algorithm is used to find latent patterns and relationships between user transactions and financial products. This article illustrates how time series forecasting and association rule mining can be merged to bring a more useful financial recommendation. The hybrid approach, which combines both approaches, proves to increase user interaction and recommendation accuracy by 20% compared to the previous systems, according to experiments. The paper emphasises the possibilities of using big data in the construction of scalable, individualized financial recommendation systems.

A Hybrid Data Decomposition and Deep Learning Approach for Solana Price Prediction Incorporating Market Factors

In this study, a hybrid data decomposition model for predicting the Solana (SOL) price is proposed, integrated by advanced signal processing and machine learning techniques. The model utilizes Variational Mode Decomposition (VMD) to decompose the Solana price series into 10 intrinsic modes, each representing different frequency components of the data. These 10 decomposed modes are then used as features, along with other relevant market factors, including Solana volume, halving index, Solana trend, and Bitcoin closing price. Then, the Bidirectional Long Short-Term Memory (BiLSTM) network is chosen to capture the complex temporal dependencies in these features and forecast future prices. To evaluate the efficacy of the approach, this study compares the hybrid models performance with a baseline LSTM model, which uses only raw historical price data. The results demonstrate that the hybrid model outperforms the benchmark model, showing higher predictive accuracy and robustness. A trading simulation for models was then conducted, and the findings indicate a significant potential for the experiment model to function as an effective trading support system, reinforcing its applicability in practical market environments. This work contributes to the field of cryptocurrency price prediction by offering a more sophisticated methodology that combines time series decomposition with deep learning techniques, providing valuable insights for traders and investors.

Reconstructing half a century of coregonine recruitment reveals species-specific dynamics and synchrony across the Laurentian Great Lakes

Abstract Understanding how multiple species and populations vary in their recruitment dynamics can elucidate the processes driving recruitment across space and time. Lake Whitefish (Coregonus clupeaformis) and Cisco (C. artedi) are socioecologically important fishes across their range; however, many Laurentian Great Lakes populations have experienced declining, poor, or sporadic recruitment in recent decades. We integrated catch and age data from 38 long-term surveys across each of the Great Lakes and Lake Simcoe, resulting in a combined time series spanning 1960–2019. We estimated Lake Whitefish and Cisco year-class strength (YCS) in each lake using longitudinal mixed-effects regressions of relative cohort abundance. We subsequently quantified interspecific, spatial, and temporal synchrony in YCS using correlation and dynamic factor analyses. Lake Whitefish YCS was positively spatially synchronous on average, and YCS in all six lakes was elevated during the 1980s–1990s. In contrast, Cisco YCS was sporadic, not spatially synchronous, and highly variable around long-term, lake-specific means. YCS was not synchronous between species in any lake. Collectively, our analyses demonstrate that these species exhibit differential recruitment dynamics that may be regulated by species-specific factors. Results from this study can be leveraged in future research on the causes and consequences of cross-species, cross-basin recruitment variability.

Corporate social responsibility and firm performance: the role of firm reputation

Purpose This study aims to examine the role of firm reputation as a mediator in the relationship between corporate social responsibility and firm performance, along with the underlying mechanism. Design/methodology/approach This research is quantitative research that aims to explain the phenomenon of the relationship between the implementation of corporate social responsibility and firm performance. A total of 573 companies listed on the Indonesia Stock Exchange for the 2019–2021 period were sampled in this study. The analysis method in this study combines time series data and cross-section data called pooled data. Findings This research found a significant positive association between corporate social responsibility and firm reputation as well firm reputation is positively related to firm performance. In contrast, corporate social responsibility directly affects firm performance insignificantly. Based on the analyses, firm reputation mediates the effect of corporate social responsibility on firm performance. Research limitations/implications This study is limited due to its reliance on secondary data related to publicly listed companies from 2019 to 2021, which involves various downsides, that is many businesses still have not disclosed their engagement in corporate social responsibility practices. This study will ultimately yield practical implications for organizational managers, as it emphasizes the significance of corporate social responsibility in enhancing firm success. Originality/value The research presented in this paper contributes to the existing body of knowledge on corporate social responsibility by examining the impact of corporate social responsibility on firm performance. Furthermore, this study provides additional evidence for shared value creation by explaining the impact of corporate social responsibility on firm performance.

AI-Driven Predictive Maintenance in Data Infrastructure: A Multi-Modal Framework for Enhanced System Reliability

This article presents a comprehensive framework for implementing artificial intelligence-driven predictive maintenance in modern data infrastructure environments. While traditional maintenance approaches have relied on reactive or scheduled interventions, the proposed framework leverages multiple AI technologies, including machine learning, natural language processing, and reinforcement learning, to create a proactive maintenance ecosystem. The methodology integrates diverse data streams from infrastructure components, including sensor data, system logs, and historical maintenance records, to predict potential failures and optimize maintenance schedules. The approach combines time series analysis for trend identification, natural language processing for unstructured data analysis, and reinforcement learning for dynamic schedule optimization. Implementation across multiple case studies, including cloud service providers and manufacturing environments, demonstrates significant improvements in system reliability, reduction in unplanned downtime, and optimization of maintenance resource allocation. The results indicate that AI-driven predictive maintenance substantially outperforms traditional approaches in both accuracy and cost-effectiveness. This article contributes to the growing field of intelligent infrastructure management and provides practical guidelines for organizations seeking to enhance their data infrastructure reliability through advanced predictive maintenance strategies.

ANALYSIS OF FACTORS AFFECTING STOCK PRICES OF COMPANIES IN THE AGRICULTURE SECTOR

Based on data from BPS (Statistics Indonesia) for 2019-2021, the agricultural sector in Indonesia dominated employment and attracted investment interest from various parties. This study analyzes the impact of fundamental analysis and macroeconomic variables on the fluctuations in stock prices of agricultural sector companies from 2019 to 2023. The aim is to examine the effects of Return on Assets (ROA), Price-Earnings Ratio (PER), Debt to Equity Ratio (DER), and Earnings Price Ratio (EPR) on the stock price movements of agricultural sector companies listed on the Indonesia Stock Exchange (IDX). The study employs a descriptive analysis method, involving the collection, organization, and analysis of data to accurately depict the investigated issue. Data collection was conducted at the IDX from 2019 to 2023 through library research methods, using secondary data in the form of financial reports of agricultural sector companies available on www.idx.co.id. The study utilizes a simple regression analysis model with Eviews 6.0 software to process statistical and econometric data. The panel data combines time series and cross-section data, analyzed using fixed effect and random effect approaches to estimate the regression model. Estimation methods include pooled regression/pooled least squares, assuming constant coefficients for each time and space. The research results indicate that the LSDV/FEM panel regression model is the best, with variables such as DER, Earnings Per Share (EPS), PER, ROA, world oil prices, and the rupiah exchange rate collectively having a significant impact on stock prices in the agricultural sector. Additionally, classical assumption analysis shows that the data is stationary, normally distributed, free from multicollinearity, heteroscedasticity, and autocorrelation. The managerial implication of these findings is that investors should consider purchasing stocks in the agricultural sector, taking into account fundamental conditions, macroeconomic factors, and technical analysis.

The Impact of Cash Liquidity and Financial Leverage on The Performance of Iraqi Banks: A Comparative Study Between Islamic and Conventional Banks

Degree of financial leverage refers to the percentage of bank assets financed through debt, which banks must employ to achieve returns and cover costs, as well as the availability of cash at maturity. Therefore, the problem of the study arises in how the indicators of leverage and cash liquidity affect the indicators of financial performance in the Iraqi banking sector. That is why the study aims to use econometrics to demonstrate the impact of leverage and cash liquidity on the financial performance indicators represented by return on assets (ROA) and net profit margin (NPM) for a sample of 38 conventional and Islamic banks for the period 2010-2022. The Dynamic Panel Data methodology was used to determine the relationship between variables, which combines time series and cross-sectional, precisely the generalized moment method (GMM). Results show that financial leverage negatively and significantly impacts ROA and NPM indicators in the total sample and Islamic and conventional banks, except for the positive relationship with ROA in conventional banks. The results also showed the positive impact of cash liquidity on ROA and NPM indicators in the total sample and Islamic and conventional banks, except for its negative impact on ROA indicators in conventional banks. The results also showed the negative impact of the cash credit (cash credit to total assets ratio) on ROA and NPM indicators in the total sample and Islamic and conventional banks. Paper type: Research paper

Assessing extreme significant wave height in China’s coastal waters under climate change

Accurately estimating the return values of significant wave height is essential for marine and coastal infrastructure, particularly as climate change intensifies the frequency and intensity of extreme wave events. Traditional models, which assume stationarity in wave data, often underestimate future risks by neglecting the impacts of climate change on wave dynamics. Combining time series decomposition and recurrence analysis, the research develops a nonstationary framework to predict significant wave height. The stochastic component is modelled using a stationary probability distribution, while the deterministic component is predicted based on sea surface temperature projections from CMIP6 climate scenarios. The model evaluation demonstrates strong predictive capability for both stochastic and deterministic components. Application of the model to China’s coastal waters reveals significant discrepancies between stationary and nonstationary return value estimates. Compared to conventional distribution models, the nonstationary model predicts substantial increases in extreme wave heights. These findings underscore the importance of adopting nonstationary models to more accurately assess future risks posed by extreme wave events in a changing climate.

An Evaluation of Sentinel-3 SYN VGT Products in Comparison to the SPOT/VEGETATION and PROBA-V Archives

Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to the SPOT/VEGETATION (SPOT VGT) base products archive. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle, a continuous series of data products from the combined data archives of SPOT VGT (1998–2014), PROBA-V (2013–2020) and Sentinel-3 SYN VGT (from 2018 onwards) are available to users, the consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. In past years, important changes have been implemented in the SYN VGT processing baseline. The archive of SYN VGT products is therefore intrinsically inconsistent, leading to different consistency levels with SPOT VGT and PROBA-V throughout the years. A spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and an intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for three distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have indeed resulted in better-quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive; a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications.

Pengaruh Suku Bunga Terhadap Inflasi dan Pertumbuhan Ekonomi Di Indonesia

The Covid-19 outbreak dealt a severe blow to Indonesia's economy. The global economic conditions were similarly affected, and Indonesia was no exception. Data shows that over the past five years, inflation and Indonesia's economic development have experienced fluctuations. Monetary policy, particularly interest rates, and inflation are two elements that influence economic growth. This research combines time series data from 2019 to 2023 using secondary data from Indonesia's Central Bureau of Statistics (BPS). The study employs quantitative and descriptive analytical methods using the structural equation modeling (SEM) approach, specifically Partial Least Square (PLS). The research findings indicate that: (1) interest rates have a positive and significant effect on inflation; (2) interest rates have a positive but insignificant effect on economic growth; and (3) inflation acts as a mediator between interest rates and economic growth in Indonesia.

The Influence of Education Level, Technology and Regional Original Income on Economic Growth in Districts/Cities of Bali Province

Bali Province, which is an international tourism destination, must be able to improve its human resources by utilizing technology and allocating stable local revenue. The purpose of this study was to analyze the effect of education level, technology and local revenue on economic growth in the Regency/City of Bali Province, both simultaneously and partially. The data sources used in this study were secondary sources with 45 observation points that combined time series and cross-section data. The results of the study showed that 1) the level of education, technology and local revenue had a positive and significant effect simultaneously on economic growth in the Regency/City of Bali Province, 2) the level of education and local revenue partially had a positive effect on economic growth in the Regency/City of Bali Province, 3) Technology partially had a negative effect on economic growth in the Regency/City of Bali Province. Based on the results of the study, to increase economic growth, investment in human resource development needs to be carried out by increasing access and quality of education for the community, then the local government also needs to evaluate and adjust the technology implementation strategy in order to provide a positive contribution, and finally the local government can optimize sources of local revenue and increase the efficiency of its management.

The Impact of Fiscal Decentralization on Development Performance in West Kalimantan with Opportunistic Behavior as a Mediating Variable

This study aims to analyze the impact of fiscal decentralization on development performance in West Kalimantan Province with opportunistic behavior as a mediating variable. The background of this research is the importance of fiscal decentralization as a policy instrument to encourage regional development. However, there are concerns that opportunistic behavior in regional financial management can reduce the effectiveness of fiscal decentralization. The research method used is panel data analysis that combines time series and cross-section data from 14 districts/cities in West Kalimantan during the 2017-2022 period. The results of the study show that budget allocation does not have a significant influence on opportunistic behavior but has a significant positive effect on economic performance, while transfer funds actually increase fiscal opportunistic behavior and significantly negatively affect economic performance. In addition, opportunistic fiscal behavior has been proven to reduce regional economic performance. These findings underscore the importance of better fiscal management to minimize opportunistic behavior and maximize the benefits of fiscal decentralization.

A hybrid carbon price forecasting model combining time series clustering and data augmentation

Stable and accurate carbon price forecasts are crucial for carbon asset management and policy design. Therefore, various advanced models have been proposed to forecast carbon price, and decomposition–reconstruction models occupy a major position. On this basis, this study proposes an improved decomposition–reconstruction framework and constructs a novel hybrid forecasting model Informer-DA-BOHB-TVFEMD-CL. The hybrid model decomposes carbon price by TVFEMD to obtain different volatility characteristics and classifies these features by time series clustering, where the high-frequency components are retained and the low-frequency components are reconstructed. Then, the training set was expanded by the generative data augment technique to improve the model's generalization ability and avoid overfitting. Finally, the advanced deep learning model Informer and the hyperparameter optimization technique BOHB are used to model the training set, and the prediction results of each subcomponent are integrated to obtain the final prediction results. Experiments are conducted in EU-ETS, Guangdong, Hubei, and Chinese national carbon market. Experimental results show that the stability and prediction performance of the proposed hybrid model are better than those of the benchmark models, proving the applicability of the improved decomposition–reconstruction idea and the validity of the hybrid model. Meanwhile, the proposed model has demonstrated good profitability in carbon trading.

Assessing the Impact of Reduced Subsidized Fertilizer Usage on Agricultural Productivity in Aceh Province, Indonesia

The agricultural sector is pivotal in Indonesia's economy, significantly contributing to the community's agricultural output. Fertilizer demand in this sector is a critical factor influencing agricultural issues, particularly concerning government policies on subsidized fertilizer distribution. This study aims to analyze the impact of restricting subsidized fertilizer usage on agricultural production in Aceh Province, focusing on how these restrictive policies affect farmers' economic aspects, productivity, and welfare. The research employs a quantitative analysis method utilizing secondary data from scientific publications, journals, books, financial reports from relevant institutions, and other measurable sources. The study uses panel data, combining time series and cross-sectional data, and applies a panel data regression model. The econometric model employs path analysis, with data processing conducted using EViews. Results indicate that limiting Urea fertilizer subsidies does not significantly affect agricultural production. Similarly, the restriction of NPP and Super Phosphate 36 (SP-36) fertilizer subsidies does not significantly impact overall agricultural production. The analysis concludes that Urea fertilizer subsidies have consistently supported agricultural production, although their significance decreases post-subsidy restrictions. NPP fertilizer subsidies, which previously had a significantly negative impact on agricultural production, lost their significance after the restrictions. SP-36 fertilizer subsidies do not show a significant impact in either period. These findings underscore the importance of adjusting fertilizer subsidy policies to optimize agricultural production in Aceh.

Remote sensing reveals trends in vegetative recovery and land cover transformation post-reclamation at tar creek superfund site

The harmful effects of mining waste on human and ecosystem health make reclamation of former mine sites an environmental management priority. However, field-based monitoring of reclamation requires significant investments of time, labor, and money. Remote sensing offers a less expensive alternative to field-based monitoring, but work is still needed to determine which metrics can be reliably estimated using remote sensing techniques. This study uses remote sensing to examine over 20 years of reclamation efforts at Tar Creek Superfund Site and to assess revegetation after site restoration. Using the Normalized Difference Vegetation Index (NDVI), we quantify key factors affecting vegetation recovery and stability after reclamation across 123 surface mining waste cleanup locations within Tar Creek. Leveraging long-term imagery from Landsat and high-resolution PlanetScope imagery, we combine time series analysis of vegetation regrowth and landcover change detection for a comprehensive picture of recovery at each site. Across all reclamation sites, the average recovery duration was 3.5 years, and average recovery rate was 0.1 NDVI year−1. After vegetative growth had plateaued, reclaimed sites had an average NDVI of 0.70. All reclamation areas were converted from majority barren landcover to vegetated landcover classes after reclamation, and vegetative stability at reclamation sites was high (70% of reclaimed area saw continuous vegetative cover from 2017 to 2023). These results demonstrate a strong potential for multi-method remote sensing techniques in tracking and explaining vegetation recovery after reclamation and represent a cost-effective approach for real-time monitoring of reclamation progress and outcomes.

Synthesis and quality assessment of combined time-series and static medical data using a real-world time-series generative adversarial network

This study addresses challenges related to privacy issues in utilizing medical data, particularly the protection of personal information. To overcome this obstacle, the research focuses on data synthesis using real-world time-series generative adversarial networks (RTSGAN). A total of 53,005 data were synthesized using the dataset of 15,799 patients with colorectal cancer. The results of the quantitative evaluation of the synthetic data’s quality are as follows: the Hellinger distance ranged from 0 to 0.25; the train on synthetic, test on real (TSTR) and train on real, test on synthetic (TRTS) results showed an average area under the curve of 0.99 and 0.98; a propensity mean squared error was 0.223. The synthetic and real data were similar in the qualitative methods including t-SNE and histogram analyses. The application of synthetic data in predicting five-year survival in colorectal cancer patients demonstrates comparable performance to models based on real data. This study employs distance to closest records and membership inference test to assess potential privacy exposure, revealing minimal risk. This study demonstrated that it is feasible to synthesize medical data, including time-series data, using the RTSGAN, and the synthetic data can be evaluated to accurately reflect the characteristics of real data through quantitative and qualitative methods as well as by utilizing real-world artificial intelligence models.

The power of mineral: Shock of the global supply chain from resource nationalism

State intervention has long affected the stability of the global mineral and raw material markets. Global energy transition has continued to boost demand for copper, lithium, cobalt, and nickel. It has exacerbated the global prevalence of resource nationalism. Research on resource nationalism has focused on the interaction of economic, political, cultural, and social elements within countries. The lack of understanding of the global economic effects of resource nationalism limits our flexibility in responding to upheavals in the global resource order. We combine time series counterfactual analysis and input–output modeling to advance our understanding of the worldwide supply chain effects of resource nationalism. We analyze the impacts of resource political behavior on global supply chains based on several representative episodes of resource nationalism. While major industrialized countries suffer more absolute welfare losses, formerly developed countries in the Global South face higher price effects. Moreover, nationalist resource shocks are not the only cause of welfare losses in the metals supply chain sector. Still, they are also harming various industries and groups, from food and energy to services. Such differences in price and welfare effects depend on the type of mineral and the resource power of the event-initiating country, on the one hand, and are influenced by the global industrial division of labor, on the other. More losses in developing countries come from the agricultural sector and primary manufacturing than from the severity of the losses suffered by the manufacturing and service sectors in industrial and rich countries. This is a reminder to pay more attention to the unequal distribution of economic pressures across countries and sectors in the global energy transition.

Investigating the sign of stratocumulus adjustments to aerosols in the ICON global storm-resolving model

Abstract. Aerosols can cause brightening of stratocumulus clouds, thereby cooling the climate. Observations and models disagree on the magnitude of this cooling, partly because of the aerosol-induced liquid water path (LWP) adjustment, with climate models predicting an increase in the LWP and satellites observing a weak decrease in response to increasing aerosols. With higher-resolution global climate models, which allow the simulation of mesoscale circulations in which stratocumulus clouds are embedded, there is hope to start bridging this gap. In this study, we present boreal summertime simulations conducted with the ICOsahedral Non-hydrostatic (ICON) global storm-resolving model (GSRM). Compared to geostationary satellite data, ICON produces realistic cloud coverage in the stratocumulus regions; however, these clouds look cumuliform, and the sign of LWP adjustments disagrees with observations. We investigate this disagreement with a causal approach, which combines time series with knowledge of cloud processes, allowing us to diagnose the sources of observation–model discrepancies. The positive ICON LWP adjustment results from a superposition of processes, with an overestimated positive response due to (1) precipitation suppression, (2) a lack of wet scavenging, and (3) cloud deepening under a weak inversion, despite (4) small negative influences from cloud-top entrainment enhancement. We also find that precipitation suppression and entrainment enhancement occur at different intensities during the day and the night, implying that daytime satellite studies suffer from selection bias. This causal methodology can guide modelers on how to modify model parameterizations and setups to reconcile conflicting studies concerning the sign and magnitude of LWP adjustments across different data sources.