Time Series Forecasting Research Articles

Forecasting the Metal Ores Industry Index on the Tehran Stock Exchange: A Gated Recurrent Unit (GRU) Approach

This research offers an in-depth examination of predicting the closing prices of the metal ores industry index on the Tehran Stock Exchange (TSE) using a Gated Recurrent Unit (GRU) model. The GRU, a type of recurrent neural network, shows great promise for tasks involving time series forecasting. The historical daily price data from October 2017 to October 2022, was used in the study after carefully preprocessing it for further analysis. The study begins with a univariate analysis to reveal distribution characteristics and the relationships between essential variables. A customized GRU model that is trained on 70% of the time series data, with its performance assessed through metrics such as Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and the R-squared (R2) score is used for prediction. The results indicate that the GRU model provides accurate predictions for the metal ores industry index, outperforming traditional forecasting techniques. The model's recurrent nature enables it to capture both short-term and long-term temporal dependencies within the data. This research highlights the significant potential of GRU networks in the realm of financial forecasting. Future improvements will focus on hyperparameter optimization and further integrating additional input variables to enhance predictive accuracy.

Enhancing financial portfolio management with predictive analytics and scalable data modeling techniques

The financial industry faces an increasingly complex landscape for portfolio management, where data-driven insights are crucial for optimizing asset allocation and managing risk. This paper explores the integration of predictive analytics and scalable data modeling techniques in enhancing financial portfolio management. By leveraging machine learning algorithms, big data architectures, and real-time data processing, predictive analytics can forecast asset trends, detect market anomalies, and assess portfolio risk with high precision. We evaluate several predictive models, including time-series forecasting, neural networks, and ensemble methods, for their efficacy in financial prediction. The study also discusses the role of scalable data modeling frameworks, such as Apache Spark and cloud-based data lakes, in handling vast volumes of unstructured data across different markets and asset classes. Findings indicate that predictive analytics, when paired with robust data models, can deliver real-time, actionable insights, enhancing decision-making for fund managers and institutional investors. Furthermore, this paper highlights best practices for implementing scalable models in financial institutions, addressing challenges like data latency, model interpretability, and system scalability. By adopting these advanced analytics frameworks, portfolio managers can achieve improved risk-adjusted returns, better asset diversification, and enhanced adaptability to market volatility.

Reviews on Transformer-based Models for Financial Time Series Forecasting

Abstracts. Relationship extraction is a key step in information extraction and aims to identify entities from text data and identify semantic links between them, which is the foundation of knowledge graph construction. Improving relationship extraction efficiency can effectively enhance the quality of information extraction, which has an impact on the construction of a knowledge graph and subsequent downstream tasks. The paper improves the efficiency and quality of relationship extraction by optimizing the model, choosing the way of relationship extraction and proposing new evaluation parameter criteria. The paper summarizes the current state-of-the-art research results, and briefly describes SIEF, an evaluation mechanism that focuses on important sentences, Coreference Aid, which captures the internal structure of entities as well as the external fine-grained information, and FILR, a framework that extracts global document information and performs multi-granular logical reasoning. Aggregate semi-automated data augmentation by integrating large model languages, remote supervision and construction of domain graphs integrating domain information of entities, cross-article entity-centered relationship extraction capable of capturing connections between different articles, and the introduction of mean-accuracy averaging capable of evaluating the comprehensibility of the model are presented. This paper presents a vision of new and better optimization algorithms by describing the latest approaches to relationship extraction optimization and finding that they all have their own limitations.

Introducing ProsperNN—a Python package for forecasting with neural networks

We present the package prosper_nn, that provides four neural network architectures dedicated to time series forecasting, implemented in PyTorch. In addition, prosper_nn contains the first sensitivity analysis suitable for recurrent neural networks (RNN) and a heatmap to visualize forecasting uncertainty, which was previously only available in Java. These models and methods have successfully been in use in industry for two decades and were used and referenced in several scientific publications. However, only now we make them publicly available on GitHub, allowing researchers and practitioners to benchmark and further develop them. The package is designed to make the models easily accessible, thereby enabling research and application in various fields like demand and macroeconomic forecasting.

A self-driven ESN-DSS approach for effective COVID-19 time series prediction and modelling.

Since the outbreak of the COVID-19 epidemic, it has posed a great crisis to the health and economy of the world. The objective is to provide a simple deep-learning approach for predicting, modelling, and evaluating the time evolutions of the COVID-19 epidemic. The Dove Swarm Search (DSS) algorithm is integrated with the echo state network (ESN) to optimize the weight. The ESN-DSS model is constructed to predict the evolution of the COVID-19 time series. Specifically, the self-driven ESN-DSS is created to form a closed feedback loop by replacing the input with the output. The prediction results, which involve COVID-19 temporal evolutions of multiple countries worldwide, indicate the excellent prediction performances of our model compared with several artificial intelligence prediction methods from the literature (e.g., recurrent neural network, long short-term memory, gated recurrent units, variational auto encoder) at the same time scale. Moreover, the model parameters of the self-driven ESN-DSS are determined which acts as a significant impact on the prediction performance. As a result, the network parameters are adjusted to improve the prediction accuracy. The prediction results can be used as proposals to help governments and medical institutions formulate pertinent precautionary measures to prevent further spread. In addition, this study is not only limited to COVID-19 time series forecasting but also applicable to other nonlinear time series prediction problems.

Artificial Neural Networks and Gradient-Boosting Decision Trees in Time Series Forecasting of Earnings per Share in Poland

ABSTRACT Precise forecasting of earnings for publicly traded companies holds significant importance in achieving investment success. It is highly important in countries where the coverage of these companies by financial analysts’ forecasts is relatively small, like Poland. This paper examines the prediction errors of modern machine learning and deep learning techniques within univariate time series settings. These methods are applied to earnings per share (EPS) data for companies listed on the Polish stock market during the period spanning from the 2008–2009 financial crisis to the 2020 pandemic shock. The seasonal random walk (SRW) model achieved the lowest error.

Cross‐Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe

Cross‐Learning With Panel Data Modeling for Stacking and Forecast Time Series Employment in Europe

A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Using a Non-Convex Distance Metric

Regression, a supervised machine learning approach, establishes relationships between independent variables and a continuous dependent variable. It is widely applied in areas like price prediction and time series forecasting. The performance of regression models is typically assessed using error metrics such as the Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). However, these metrics present challenges including sensitivity to outliers (notably MSE and RMSE) and scale dependency, which complicates comparisons across different models. Additionally, traditional metrics sometimes yield values that are difficult to interpret across various problems. Consequently, there is a need for a metric that consistently reflects regression model performance, independent of the problem domain, data scale, and outlier presence. To overcome these shortcomings, this paper introduces a new regression accuracy measure based on the Hassanat distance, a non-convex distance metric. This measure is not only invariant to outliers but also easy to interpret as it provides an accuracy-like value that ranges from 0 to 1 (or 0–100%). We validate the proposed metric against traditional measures across multiple benchmarks, demonstrating its robustness under various model scenarios and data types. Hence, we suggest it as a new standard for assessing regression models’ accuracy.

Taming Data‐Driven Probability Distributions

ABSTRACTWe propose a deep learning approach to probabilistic forecasting of macroeconomic and financial time series. By allowing complex time series patterns to be learned from a data‐rich environment, our approach is useful for decision making that depends on the uncertainty of a large number of economic outcomes. In particular, it is informative for agents facing asymmetric dependence of their loss on the outcomes of possibly non‐Gaussian and nonlinear variables. We demonstrate the usefulness of the proposed approach on two different datasets where a machine learns patterns from the data. First, we illustrate the gains in predicting stock return distributions that are heavy tailed and asymmetric. Second, we construct macroeconomic fan charts that reflect information from a high‐dimensional dataset.

Daily runoff prediction based on lightweight Mamba with partial normalization

ABSTRACT Runoff prediction serves as the cornerstone for the effective management, allocation, and utilization of water resources, playing a key role in hydrological research. This study employs a newly reported deep learning model, Mamba, to forecast river daily runoff and compared the proposed model with various benchmark methods, including statistical models, machine learning methods, recurrent neural networks, and attention-based models. Application of these models is implemented on three hydrological stations situated along the middle and lower reaches of the Mississippi River. Daily runoff from 1983 to 2023 were used to build the model for 7-day prediction. Findings demonstrate the superiority of the Mamba model over its counterparts, showcasing its potential as a backbone model. In response to the necessity for a more lightweight approach, a refined variant of the Mamba model is proposed, called LightMamba. LightMamba incorporates partial normalization and MPM (Multi-Path-Mamba) to enhance its efficacy in discerning nonlinear trends and capturing long-term dependencies within the streamflow data. Notably, LightMamba achieves commendable performance with an average NSE of 0.904, 0.907, and 0.900 on the three stations. This study introduces an innovative backbone model for time series forecasting, which offers a novel approach to hybrid modeling for future daily runoff prediction.

Vector SHAP Values for Machine Learning Time Series Forecasting

ABSTRACTWe propose a new vector SHapley Additive exPlanations (SHAP) to interpret machine learning models for forecasting time series using lags of predictor variables. Unlike the standard SHAP measuring the contribution of each lag of each predictor variable, the proposed vector SHAP measures the contribution of the vector of the lags of each variable. The vector SHAP has an advantage of faster computation over the standard SHAP. Some desirable properties of the vector SHAP (vector local accuracy, vector missingness, and vector consistency) are established. A Monte Carlo simulation shows that the vector SHAP has a much faster computing time than the SHAP; the difference of the standard SHAP and the vector SHAP is small; the sampling SHAP is sensitive to the sampling proportion in a range of practical application; the vector SHAP mitigates the sensitivity issue. The vector SHAP is applied to the realized volatility of world major stock price indices of 16 countries for forecasting the realized volatility of South Korea stock price index, KOSPI. Further vectoring by regions of Europe, North America, and Asia yields vector SHAP value for each region which is very close to the sum of vector SHAP values of the countries of the region, illustrating usefulness of the strategy of vectoring.

Dengue Prediction using Short-Term Memory

Dengue is a severe infectious disease on the rise in Malaysia, and there is a demand for artificial intelligence to support the health system. However, the application of deep learning, specifically Long Short-Term Memory (LSTM) time series forecasting, has not been explored by many in dengue prediction studies. However, considering the availability of daily weather data being collected, the ability of LSTM to capture long term dependencies can be leveraged in forecasting dengue cases. Therefore, this study investigates the performance and viability of LSTM time series forecasting on predicting dengue cases. An LSTM model is developed and evaluated to be compared to a Support Vector Regression (SVR) model by utilising the availability of a dengue dataset consisting of weather and climate data. The results indicated LSTM time series forecasting performed better than SVR, with R2 and MAE scoring 0.75 and 8.76. In short, LSTM has shown better performance and, in addition, capturing trends in the rise and fall of dengue cases. Altogether, this research could contribute to the fight against the increase of dengue cases without relying on forecasted weather data but instead, history.

What we can do with one qubit in quantum machine learning: ten classical machine learning problems that can be solved with a single qubit

This work focuses on quantum machine learning and analyzes the power of one single qubit to solve classical machine learning problems. We explore possible strategies to address traditional supervised, unsupervised, and reinforcement learning tasks. In particular, we study binary and multinomial classification, regression problems, time series forecasting, clustering, and quantum reinforcement learning. Our results suggest that the same methodology could be used to address all three types of learning with different measurement strategies and, despite the strong limitation of reduced data dimensionality of the candidate problems, a single qubit can achieve similar or even improved performance with respect to state-of-the-art classic machine learning methods in many cases. As simulating the evolution of one qubit state is computationally efficient, our study enables the possibility to use the qubit model as a candidate solution to implement simple decision-making machine learning models in hardware with extremely low memory resources such as embedded systems or edge devices. The outcomes provided could be also of interest in academia and for the construction of demonstrators using low-scale contemporary quantum hardware.

Cyber Attacks Detection And Mitigation Using Machine Learning In Smart Grid Systems

The increasing complexity and interconnectedness of smart grid systems have heightened their vulnerability to cyber threats, necessitating advanced solutions for securing these critical infrastructures. This study aims to explore the potential of AI-driven predictive analytics in enhancing the cybersecurity and operational efficiency of smart grids. By leveraging the systematic approach of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review of 1,526 initial articles was conducted, which was subsequently narrowed down through rigorous screening and evaluation to a final set of 127 high-quality studies. The review reveals that machine learning models, such as neural networks and time series forecasting, significantly enhance the early detection and mitigation of cyber threats, allowing grid operators to take proactive measures to safeguard against disruptions. Additionally, the integration of AI with real-time data analytics was found to optimize load forecasting, predict equipment failures, and improve overall grid resilience, leading to reduced downtime and operational costs. However, significant challenges related to data privacy, scalability, and integration with legacy systems persist. The findings suggest that techniques like federated learning and blockchain integration could address these challenges, though further research is needed to enhance model robustness and efficiency. This review provides critical insights into the practical applications of AI in smart grid cybersecurity, highlighting both the benefits and the barriers that must be overcome to achieve widespread adoption.

Predictive Modeling for Bike Rental Demand: Enhancing Operational Efficiency and User Satisfaction in Urban Mobility

This project focuses on developing a predictive model to forecast bike rental demand by analyzing historical rental data, weather information, and temporal variables. The model will employ machine learning techniques, such as regression analysis, time series forecasting, and ensemble methods, to capture the complex relationships between these factors and rental demand.

A deep kernel regression-based forecasting framework for temperature-induced strain in large-span bridges

The strain data from health monitoring systems of large-span bridges is influenced by various load effects, with the extraction and forecasting of temperature-induced strain being particularly significant for precise analysis and early warning of monitoring data. This paper presents a forecasting framework for temperature-induced strain in large-span bridges, employing a deep kernel regression (DKR) approach that integrates deep learning with Bayesian regression to enhance accuracy and certainty. Initially, this paper addresses the influence of additional response increment induced by vehicle strain effects and employs a robust data smoothing algorithm to extract temperature-induced effect components from measured strain data offline. Subsequently, a DKR model is proposed, integrating a long short-term memory (LSTM) layer with a fully connected layer. The output of the deep learning module serves as the kernel function parameter for the Gaussian process regression (GPR) module, and the GPR module with updated hyperparameters is used for time series forecasting. This method effectively extracts and utilizes time series features from historical data alongside key environmental factors, enabling real-time forecasting of strain effects and significantly improving the performance and utility of health monitoring systems in large-span bridges. Compared to commonly used time series forecasting algorithms, the algorithm proposed in this paper exhibits significantly improved accuracy, stability, and certainty. Through comparing the inference time, it was verified that the algorithm can meet the performance requirements of real-time inference, which underscores the model's potential as a robust tool in bridge structural health monitoring.

Forecasting economic growth by combining local linear and standard approaches

Today, developing economies are of major importance for global macroeconomic development. However, the empirical analysis and especially the forecasting of macroeconomic time series remain difficult due to a lack of sufficient data, data frequency, high volatility, and non-linear developments. These difficulties require more sophisticated approaches to obtain reliable forecasts. Therefore, we propose an improved forecasting method especially for growth data based on a data-driven local linear trend estimation with an extended iterative plug-in algorithm for determining the bandwidth endogenously. This approach allows a smooth trend estimation that takes care of temporary changes in trend processes. Further, the naïve random walk model is extended for forecasting by including a local linear, time-varying drift. We apply this method to GDP development for six developing and two advanced economies and compare different forecast combinations. The combinations that include the local linear approach and the random walk with a local linear trend improve forecasting accuracy and reduce variance.

Big Data Analytics for Forecasting Tourism Recovery in Bali Island Using Multivariate Time Series

Bali is a famous tourist area and can significantly contribute to the Indonesian tourism sector. The COVID-19 pandemic has made Indonesian tourism, including Bali tourism, experience a decline. In March 2022, COVID-19 cases decreased, and the government began to relax some policies. The tourism sector is vital in economic recovery efforts after the COVID-19 pandemic. Therefore, it is necessary to identify tourism recovery to determine strategies and policies related to Indonesian tourism, especially in Bali. Multivariate time series forecasting of tourism demand can be used to identify tourism recovery using several significant data sources. The methods used are Vector Autoregressive (VAR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data used are the monthly official number of tourists, room occupancy rate, Google Trends, number of booking.com user reviews, and nighttime light intensity in Bali Province from January 2019 to December 2022. The results show that the best forecasting method is VAR, and modeling with multivariate time series forecasting can improve the performance of forecasting results. In addition, big data can be used as a source of supporting data that can provide better forecasting results, and the size of the dataset affects the selection of the best model. Furthermore, the descriptive and forecasting analysis results show that Bali tourism has experienced post-pandemic tourism recovery. The strategies and policies of the Bali government to restore Bali tourism faster are good enough.

Forecasting techniques based on the use of time series models for forecasting expenditures on social protection and social security

In the context of a military conflict, it is growing the number of people who are in difficult life circumstances and need state assistance. Therefore, the issue of forecasting spending on social protection and social security is becoming increasingly relevant. The aim of the paper is to consider modern methods of analyzing and forecasting time series and propose their application to determine the need for spending on social protection and social security. The article provides a brief overview of regression models, their advantages and features of use for analyzing and predicting nonlinear and linear processes described by time series of statistical indicators used in the population social protection system. The paper offers a combination of predictions that can be made based on locally linear and nonlinear models, such as threshold autoregression (TAR) and exponential autoregression (ExAR), which have known quality characteristics. This approach primarily concerns time series that contain data on structural changes in the analyzed processes. Using combined predictions, it is possible to estimate the contribution of each component for both different and selected time points using variable weights. To build forecasts, depending on the scenario of distribution of social protection and social security expenditures, it is proposed to use Bayes networks. Their advantages are flexibility, high quality of results, the possibility of structural and parametric optimization and adaptation to new data and conditions, support for quality assessment by appropriate sets of statistical criteria, which makes it possible to analyze thoroughly intermediate and final results. It helps to increase the adequacy of models and the high quality of the final result of their application.

IFNN: Intuitionistic Fuzzy Logic Based Neural Network Model for Time Series Forecasting

IFNN: Intuitionistic Fuzzy Logic Based Neural Network Model for Time Series Forecasting