Time Series Prediction Research Articles

Weathering market swings: Does climate risk matter for agricultural commodity price predictability?

The challenges posed by climate change on the agricultural market have become a pressing concern. An accurate reading of future agricultural commodity prices can be an invaluable planning instrument for diverse interested parties. Here, we explore asset pricing implications of climate risk for the agricultural commodity market from January 2005 to December 2021. Through introducing a composite climate risk index based on the four individual climate risk measures of Faccini et al. (2023), our findings provide valuable insights into the time-series predictability of aggregate climate risk on future agricultural commodity returns, both in- and out-of-sample. This powerful predictability conveys substantial economic benefits to mean–variance investors and cannot be subsumed by conventional economic predictor variables. The evidence further suggests that physical risk, especially global warming, exhibits much stronger return predictability than transition risk. Moreover, we emphasize the pivotal role of climate risk in shaping supply dynamics and capturing investor attention, thereby serving as potential drivers of return predictability. Overall, these predictive insights hold important implications for risk management, investment strategies, and policy formulation in the agricultural commodity market.

Effective weight optimization strategy for precise deep learning forecasting models using EvoLearn approach

Time series analysis and prediction have attained significant attention from the research community in the past few decades. However, the prediction accuracy of the models highly depends on the models’ learning process. In order to optimize resource usage, a better learning methodology, in terms of accuracy and learning time, is needed. In this context, the current research work proposes EvoLearn, a novel method to improve and optimize the learning process of neural-based models. The presented technique integrates the genetic algorithm with back-propagation to train model weights during the learning process. The fundamental idea behind the proposed work is to select the best components from multiple models during the training process to obtain an adequate model. To demonstrate the applicability of EvoLearn, the method is tested on the state-of-the-art neural models (namely MLP, DNN, CNN, RNN, and GRU), and performances are compared. Furthermore, the presented study aims to forecast two types of time series, i.e. air pollution and energy consumption time series, using the developed framework. In addition, the considered neural models are tested on two datasets of each time series type. From the performance comparison and evaluation of EvoLearn using a one-tailed paired T-test against the conventional back-propagation-based learning approach, it was found that the proposed method significantly improves the prediction accuracy.

Feed-forward State of Charge estimation of LiFePO[formula omitted] batteries using time-series machine learning prediction with autoregressive models

This paper presents a Recurrent Neural Network (RNN) model for the State of Charge (SoC) estimation for an Electric Vehicle’s Lithium-Ion battery. A new Autoregressive modification is proposed to improve upon earlier published Machine Learning models to enhance the prediction accuracy and make results less sensitive to varying usage conditions. By building upon earlier published findings, assessing several models’ ability to estimate charge using a history window of sensory data, this work proposes, implements, and reports its methods of improving accuracy by introducing the State of Charge as part of sensory inputs in a Feed-Forward manner. The method is based on the Autoregressive implementation of neural network model training, where successively calculated charge values are used as input in the subsequent predictions, but the potential inaccuracies with those predictions that might otherwise cause significant and rapid divergence are accounted for within a modified recursive training procedure. As a result, with a slight increase in training time, the accuracy of the output prediction for three different realistic charge–discharge driving profiles doubled compared to the two best previously published models. Overall, the test results have demonstrated the usability of the current model in actual driving scenarios, making such models a viable replacement to estimation approaches used in electric vehicle battery management systems.

Enhancing Multi-step Brent Oil Price Forecasting with Ensemble Multi-scenario Bi-GRU Networks

Accurate crude oil price forecasting is crucial for economic stability, investment planning, and strategic decision-making across various industries. Despite numerous research efforts in applying deep learning to time-series forecasting, achieving high accuracy in multi-step predictions for volatile time-series like crude oil prices remains a significant challenge. Moreover, most existing approaches primarily focus on one-step forecasting, and the performance often varies depending on the dataset and specific case study. This paper introduces ensemble-based deep-learning models to capture Brent oil price volatility and enhance the multi-step price prediction. Our methodology employs a two-pronged approach. First, we present an empirical comparison of deep-learning models and architectures, including RNNs, CNNs, and transformers, for forecasting Brent oil prices. We also examine the impact of various external factors on forecasting accuracy. Then, we introduce a novel approach that employs ensemble GRU-based models to enhance prediction accuracy across multiple forecasting scenarios. Extensive experiments were conducted using a dataset of historical Brent prices encompassing the COVID-19 pandemic, which significantly impacted energy markets. The results demonstrate that the proposed model outperforms benchmark and established models, achieving a 9.3% reduction in MSE compared to the closest benchmark model for a 3-day forecasting horizon.

Bayesian Model Selection for Addressing Cold-Start Problems in Partitioned Time Series Prediction

Bayesian Model Selection for Addressing Cold-Start Problems in Partitioned Time Series Prediction

Chaotic attractor reconstruction using small reservoirs—the influence of topology

Abstract Forecasting timeseries based upon measured data is needed in a wide range of applications and has been the subject of extensive research. A particularly challenging task is the forecasting of timeseries generated by chaotic dynamics. In recent years reservoir computing has been shown to be an effective method of forecasting chaotic dynamics and reconstructing chaotic attractors from data. In this work strides are made toward smaller and lower complexity reservoirs with the goal of improved hardware implementability and more reliable production of adequate surrogate models. We show that a reservoir of uncoupled nodes more reliably produces long term timeseries predictions than more complex reservoir topologies. We then link the improved attractor reconstruction of the uncoupled reservoir with smaller spectral radii of the resulting surrogate systems. These results indicate that, the node degree plays an important role in determining whether the desired dynamics will be stable in the autonomous surrogate system which is attained via closed-loop operation of the trained reservoir. In terms of hardware implementability, uncoupled nodes would allow for greater freedom in the hardware architecture because no complex coupling setups are needed and because, for uncoupled nodes, the system response is equivalent for space and time multiplexing.

A novel stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation for modeling and simulating of financial indicators

The main purpose of this study is to develop a new discrete time stochastic ProFiVaS model based on decomposition of stochastic Vasicek differential equation which follows bouncing autoregressive AR1, with fixed variance and trend for modeling and simulating of international financial time series forecasting.This study offers the four discrete time stochastic models. The first model is determined as autoregressive integrated moving average-Arima1,1,1, and the parameter estimates of this model are found using the maximum likelihood method (MLM). Monte Carlo simulation is applied to the obtained parameters. This model is named MoCArima for short. Second, the MoCArima model whose parameters are estimated with particle swarm optimization (PSO) is determined as Proposed Monte Carlo (MC) Arima1,1,1, and is briefly named as ProMoCArima. The third model is determined as Fixed Variance Stochastic Model which has a bouncing autoregressive AR1 structure with constant variance and trend component. This model is briefly named as FiVaS. The parameter estimates are found using the MLM. The fourth model is determined as the Proposed Fixed Variance Stochastic Model. This model is briefly named as ProFiVaS. Structurally the same as the third model. The parameter estimates of both trend and stochastic parts of the FiVaS model are found by the PSO.Proposed models are applied to forecasting international different financial indicators to show its superiority and applicability. The performance and usage efficiency of the proposed four stochastic models are examined on a total of six different international financial data types, five of which are stock index data BIST100 (Türkiye), S&P500 (USA), DAX (Germany), FTSE100 (UK), NIKKEI225 (Japan), and one of which is the exchange rate EURO/USD. The ProFiVaS among all models provides the best performance on the all indices. The ProFiVaS can easily be extended to any real-world time series prediction requirements.The proposed models can be extended to all stationary and non-stationary linear or nonlinear autoregression models opening the door to modeling data types obtained from different sector fields. A part of the future plan of this work is to design a decision support system software that assigns a value of 1 when an investor needs to invest in a financial indicator, and 0 otherwise.

Evaluating machine learning models in predicting GRI drought indicators (case study: Ajabshir area)

Examining the condition of groundwater resources and the impact of droughts is valuable for effective water resources management. Today, machine learning (ML) models are recognized as one of the useful tools in time series predictions. In this study, the groundwater condition of one of the most important aquifers in northwest Iran was investigated using MODFLOW, followed by estimating the groundwater resource index (GRI) utilizing the multivariate adaptive regression spline (MARS) and least squares support vector regression (LSSVR) for a period between 2001 and 2019. Meteorological and hydrological drought indicators along with precipitation and flow rate were used as input variables for prediction. The simulation results revealed a groundwater level decrease since the aquifer withdrawal amount is more than the recharge amount. Besides, results showed that there is a limited interaction between surface water and groundwater resources, mainly caused by the decrease in the river flow and aquifer groundwater level drop. Both ML models performed well in GRI estimation, using groundwater flow, streamflow drought index, standardized precipitation index, and runoff as input variables. The performance of the MARS model with RMSE, MAE, and NSE error evaluation criteria of 0.37, − 0.19, and 0.83, respectively, exerted slightly better results than LSSVR with RMSE, MAE, and NSE of 0.48, − 0.06, and 0.80, respectively. The findings reveal the appropriate performance of both models in forecasting drought indicators, highlighting the necessity of using ML models in hydrology and drought prediction problems.

A systematic method of long-sequence prediction of natural gas supply in IES based on spatio-temporal causal network of multi-energy

Natural gas plays an important role in the energy peak-shaving process of Integrated Energy Systems (IES). Accurate prediction of natural gas supply over longer time scales is one of the most important conditions to ensure the stability and security of IES energy supply. However, as the sequence length of the predicted time series increases, it becomes more difficult to extract valid dependencies from the data. At the same time, there are complex spatio-temporal coupling characteristics of various energy resources in the IES, which makes the construction of long sequence prediction models for natural gas supply very difficult. To solve this problem, we propose a systematic method for long-sequence prediction with natural gas supply in IES based on spatio-temporal causal network of multi-energy. First, the proposed approach uses a causal analysis algorithm to represent the complex coupled spatio-temporal relationships of multiple energy resources in an IES. Then, the natural gas time-series are decomposed through the Variational Modal Decomposition (VMD), to reveal its inherent trend and cyclical characteristics. Finally, the long-sequence prediction model is proposed by fusing the attention mechanism into Graph Convolutional Neural Network (GCN). Attentional mechanisms are introduced to obtain important long-term dependencies in the long sequence prediction process. We use a integrated energy dataset from Spain to test the validity and superiority of the method. The results show that the proposed model in this paper improves the Root Mean Square Error (RMSE) by at least 9.5% and reduces the Mean Absolute Error (MAE) by at least 14.5% compared to several baseline models.

Improving Cleaning of Solar Systems through Machine Learning Algorithms

The study focuses on the importance of maintaining photovoltaic (PV) systems for optimal performance in sustainable energy generation. It highlights the impact of dust accumulation on reducing system efficiency and proposes a method to predict system performance, aiding in scheduling cleaning activities effectively. Two prediction models are developed: one using time-series prediction techniques (LSTM, ARIMA, SARIMAX) to forecast Performance Ratio (PR), and another employing ensemble voting classifiers (RF, Log, GBM) to predict the need for cleaning. The SARIMAX model performs best, achieving high accuracy in PR prediction (R2 = 92.12%), while the classification model accurately predicts cleaning needs (91%). The research provides valuable insights for improving maintenance strategies and enhancing the efficiency and sustainability of PV systems.

Forecasting international tourist arrivals in South Korea: a deep learning approach

Purpose This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism. Design/methodology/approach This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism. Findings This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management. Originality/value This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

Comparison of Interval Type-3 Mamdani and Sugeno Models for Fuzzy Aggregation Applied to Ensemble Neural Networks for Mexican Stock Exchange Time Series Prediction

In this work, interval type-2 and type-3 fuzzy systems were designed, of Mamdani and Sugeno types, for time series prediction. The aggregation performed by the type-2 and type-3 fuzzy systems was carried out by using the results of an optimized ensemble neural network (ENN) obtained with the particle swarm optimization algorithm. The time series data that were used were of the Mexican stock exchange. The method finds the best prediction error. This method consists of the aggregation of the responses of the ENN with type-2 and type-3 fuzzy systems. In this case, the systems consist of five inputs and one output. Each input is made up of two membership functions and there are 32 possible fuzzy if-then rules. The simulation results show that the approach with type-2 and type-3 fuzzy systems provides a good prediction of the Mexican stock exchange. Statistical tests of the comparison of type-1, type-2, and type-3 fuzzy systems are also presented.

MCGnet with multi-level gated unit transformations: A time series prediction-based model for unknown pattern abnormality detection

The task of anomaly detection in data patterns remains challenging due to the diverse of fault patterns, which often render existing models ineffective when encountered with unknown or unseen data patterns in the training data. Furthermore, insufficient samples, the presence of chaotic behavior, unbalanced anomaly patterns are potential limiting factors in the practical application. While prediction error-based approaches attempt to address these challenges, the discriminability of abnormal patterns in existing scenarios is not yet sufficient, thus limiting the ability to identify abnormal phenomena. To overcome these limitations, we proposed a novel model called MCGnet. The backbone architecture of MCGnet employed a novel design of continuous multi-layer circular convolution transformations. This design enhances the expressive capability of the model by expanding the differences among data patterns. Additionally, we introduced in parallel the newly proposed temporal-frequency gated Gaussian unit, which generates richer feature representations. Subsequently, the obtained feature representations were fed into an explicit causal network to learn complex patterns of data along temporal dependencies. The MCGnet not only solves high-precision time series prediction problems but also significantly improves the discriminability between normal and abnormal patterns. Since the proposed model is accurately modeled using only a small amount of normal data, it bypasses the limiting factors such as insufficient fault samples and sample imbalance, and is able to effectively deal with various unknown data patterns. The accuracy of the proposed method was verified through simulation data (Duffing system) and its potential for engineering applications was demonstrated through real-world data (rotor system).

Application of Time Series Prediction Models in Chinese Electricity Consumption Analysis

Study focuses on the application of time series prediction models in analyzing electricity consumption in China, aiming to promote the intelligentization and sustainable development of the country's power system. The ARIMA model was chosen for this research, incorporating historical electricity data to forecast future consumption. Through a series of steps including data preprocessing, model construction, and prediction analysis, the results indicate a general growth trend in electricity consumption, influenced by seasonal and cyclical factors. By plotting periodic decomposition charts, the study visually reveals the components and patterns of electricity data, providing scientific decision-making support for the planning, scheduling, and management of the electricity industry.

Rethinking real-time risk prediction from multi-step time series forecasting on highway car-following scenarios

Driving risk prediction emerges as a pivotal technology within the driving safety domain, facilitating the formulation of targeted driving intervention strategies to enhance driving safety. The driving safety undergoes continuous evolution in response to the complexities of the traffic environment, representing a dynamic and ongoing serialization process. The evolutionary trend of this sequence offers valuable information pertinent to driving safety research. However, existing research on driving risk prediction has primarily concentrated on forecasting a single index, such as the driving safety level or the extreme value within a specified future timeframe. This approach often neglects the intrinsic properties that characterize the temporal evolution of driving safety. Leveraging the high-D natural driving dataset, this study employs the multi-step time series forecasting methodology to predict the risk evolution sequence throughout the car-following process, elucidates the benefits of the multi-step time series forecasting approach, and contrasts the predictive efficacy on driving safety levels across various temporal windows. The empirical findings demonstrate that the time series prediction model proficiently captures essential dynamics such as risk evolution trends, amplitudes, and turning points. Consequently, it provides predictions that are significantly more robust and comprehensive than those obtained from a single risk index. The TsLeNet proposed in this study integrates a 2D convolutional network architecture with a dual attention mechanism, adeptly capturing and synthesizing multiple features across time steps. This integration significantly enhances the prediction precision at each temporal interval. Comparative analyses with other mainstream models reveal that TsLeNet achieves the best performance in terms of prediction accuracy and efficiency. Concurrently, this research undertakes a comprehensive analysis of the temporal distribution of errors, the impact pattern of features on risk sequence, and the applicability of interaction features among surrounding vehicles. The adoption of multi-step time series forecasting approach not only offers a novel perspective for analyzing and exploring driving safety, but also furnishes the design and development of targeted driving intervention systems.

Interior-point policy optimization based multi-agent deep reinforcement learning method for secure home energy management under various uncertainties

Improving the efficiency of home energy management (HEM) is of great significance in reducing resource waste and prompting renewable energy consumption. With the development of advanced HEM systems and internet-of-things technology, more residents are willing to participate in the electricity market through a demand response mechanism, which can not only reduce the electricity bill but also stabilize the operation of the main grid through peak shaving and valley filling. However, the uncertainties from household appliance parameters, user behaviors, penetrated renewable energy, and electricity prices render the HEM problem a non-trivial task. Although previous deep reinforcement learning (DRL) methods have no requirement for system dynamics due to their model-free and data-driven merits, the unrestricted action space may violate the physical constraints of various devices, and few works have concentrated on this area before. To solve the above challenges, this paper proposes a novel interior-point policy optimization-based multi-agent DRL algorithm to optimize the home energy scheduling procedure while guaranteeing safety during its operation. Besides, a time-series prediction model based on a non-stationary Transformer neural network is proposed to predict the future trend of solar generation and electricity price, which can provide the agents with abundant information to make better decisions. The superior performance of our proposed predictive-control coupled method is demonstrated through extensive numerical experiments, which achieves more precise prediction results, near-zero constraint violations, and better trade-offs between cost and safety than several benchmark methods.

Hybrid deep learning approach for rock tunnel deformation prediction based on spatio-temporal patterns

The ability to predict tunnel deformation holds great significance for ensuring the reliability, safety, and sustainability of tunnel structures. However, existing deformation prediction models often simplify or overlook the impact of spatial characteristics on deformation by treating it as a time series prediction issue. This study utilizes monitoring data from the Grand Canyon Tunnel and introduces an effective data-driven method for predicting tunnel deformation based on the spatio-temporal characteristics of the historical deformation of adjacent sections. The proposed model, a combination of graph attention network (GAT) and bidirectional long and short-term memory network (Bi-LSTM), is equipped with robust spatio-temporal predictive capabilities. Additionally, the study explores other possible spatial connections and the scalability of the model. The results indicate that the proposed model outperforms other deep learning models, achieving favorable root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) values of 0.34 mm, 0.23 mm, and 0.94, respectively. The graph structure based on intuitive spatial connections proves more suitable for meeting the challenges of predicting deformation. Integrating GAT-LSTM with transfer learning technology, remains stable performance when extended to other tunnels with limited data.

Maschinelles Lernen als modernes Werkzeug für Additive Fertigung

Abstract In modern manufacturing systems, quality monitoring is crucial for efficient and cost-effective production. Conventional systems rely on thresholds and process windows, but machine learning (ML) techniques promise greater accuracy and efficiency. However, pre-processing the data is still timeconsuming. This paper presents an approach to visually verify two Variational Autoencoders (VAEs) using contextual information such as print job numbers and timestamps, with the aim of predicting time series data from image data to optimize additive manufacturing processes in time. The approach focuses on the Digital Light Processing (DLP) printing process and emphasizes the importance of accurate data pre-processing and contextual visualization. The approach utilizes VAE-generated latent spaces to improve prediction accuracy in additive manufacturing and implement quality monitoring without thresholds and process windows.

Collective dynamics and long-range order in thermal neuristor networks

In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed “thermal neuristors.” These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.

Corporate bankruptcy prediction based on the Adaboost algorithm for optimisation of long and short-term memory networks

In this study, a Long Short-Term Memory (LSTM) network was used to model and predict time series data, providing an innovative approach to corporate bankruptcy prediction. Subsequently, the predictions and real labels of the LSTM models were used to train the Adaboost algorithm to improve the accuracy and robustness of the models. Eventually, multiple trained LSTM models are combined into a more robust integrated model by adjusting the weights. It is worth mentioning that the integrated model achieves 95.57% prediction accuracy on the training set and 94.39% prediction accuracy on the test set, which indicates that the model has good prediction effect and generalisation ability. The method proposed in this study is of great significance, firstly, by combining LSTM and Adaboost algorithm, we not only improve the accurate prediction ability of corporate bankruptcy, but also enhance the ability of identifying anomalies. Second, by combining multiple LSTM models and adjusting the weights to form a more powerful integrated model, we effectively improve the overall prediction performance. This approach can provide financial institutions, investors, and government regulators with a more reliable and accurate tool for assessing corporate bankruptcy risk, which can help identify potential risks and take appropriate measures in a timely manner.