Time Series Research Articles

Impact of Environmental Regulation and Sustainable Development on Economic Growth

Access to stable, low-cost, and environmentally friendly power has the potential to raise a country's human development index (HDI). However, there are unforeseen effects to economic growth, such as environmental damage caused by overconsumption of energy. Major changes to the world's ecology have resulted from the growing reliance on non-renewable energy sources. This research delves into the interplay between various energy sources, GDP growth, environmental legislation, sustainable development, and the HDI. The time series data used in this research spans the years 2000–2020. The degree of correlation between two variables may be determined using the Granger causality test. The correlations among the variables, both long-term and short-term, may be determined using the Course Mistake Alteration model. In order to meet the growing need for energy, policymakers should support and promote these renewable liveliness bases as a feasible alternative to traditional energy sources, taking into account the expected results. To reach the goal of reducing carbon emissions by 2030 set by South Asian countries, current environmental laws need to be tightened.

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.

Improving density peak clustering on multi-dimensional time series: rediscover and subdivide

Improving density peak clustering on multi-dimensional time series: rediscover and subdivide

Study On Mathematical Model For Forecasting of Novel Coronavirus in India

This paper reveals that the new coronavirus (Covid-19) is the biggest challenge for the whole world. The World Health Organization (WHO) has declared it an epidemic. The data was collected from 209 different individual WHO situation reports for COVID-19 in India. First, prognostic models were compared based on minimum AIC, MAPE, and MAE. Then the best forecasting models were used on the epidemiologic data of India to predict the epidemiologic pattern of prevalence. New and Total Covid-19 Deaths and Incidence ARIMA and SARIMA have been found suitable and predicted for September 1, 2020. Also, the prophet model is used for accuracy and prediction of the total number of coronavirus cases in India. The predicted values are checked against the past observed values so that the two values are very close. Using such time series models we can forecast for the next 15-20 days and plan accordingly. This kind of projection helps in planning for the future.

Score filtering for contextualized noise suppression of Poisson‐distributed geophysical signals

AbstractGeophysical and remote sensing products that rely on Poisson‐distributed measurement signals, such as cosmic‐ray neutron sensing (CRNS) and gamma spectrometry, often face challenges due to inherent Poisson noise. Common techniques to enhance signal stability include data aggregation or smoothing (e.g., moving averages and interpolation). However, these methods typically reduce the ability to resolve detailed temporal (stationary data) and spatial (mobile data) features. In this study, we introduced a method for contextual noise suppression tailored to Poisson‐distributed data, utilizing a discrete score attribution system. This score filter evaluates each observation against eight different criteria to assess its consistency with surrounding values, assigning a score between 0 (very unlikely) and 8 (very likely) to indicate whether the observation is likely to act as noise. These scores can then be used to flag or remove data points based on user‐defined thresholds. We tested the score filter's effectiveness on both stationary and mobile CRNS data, as well as on gamma‐ray spectrometry and electromagnetic induction (EMI) recordings. In our examples, the score filter consistently outperformed established filters, for example Savitzky–Golay and Kalman, in direct competition when applied to CRNS time series data. Additionally, the score filter substantially reduced Poisson noise in mobile CRNS, gamma‐ray spectrometry and EMI data. The scoring system also provides a context‐sensitive evaluation of individual observations or aggregates, assessing their conformity within the dataset. Given its general applicability, customizable criteria and very low computational demands, the proposed filter is easy to implement and holds promise as a valuable tool for denoising geophysical data and applications in other fields.

Time Series Analysis of Dry Chilli Price Trends in Byadagi Market, Haveri District, Karnataka, India

Spices are conventional aromatic vegetables mainly utilized for flavoring of food. Among these, chilli (capsicum annuum), is one of most important spice used around the world. The present study examined dry chillies price behaviour in the Byadagi market of Haveri District, Karnataka. The study was based on secondary data from 2000-2022 collected in Agmarketnet.in. Time series analysis involves a complex mix of four main components: trend (Tt), seasonal (St), cyclical (Ct), and irregular (It) variations. The result revealed that there was a significant increasing trend in prices of dry chillies from 2000-2022. The Multiplicative model of TCSI was used in this study, the polynomial regression was also used for the trend analysis and found the Third-degree model best fit. The highest seasonal index was found in January (112.44) while the lowest seasonal index in the month of July (93.28) the analysis of seasonal indices in the price of dry chilli would help to understand the status of dry chilli marketing in the Byadagi market. The dry chilli prices exhibit no oscillatory movement and no definite periodicity during the study period.

Underlying factors for gold price prediction based on ARIMA models

Abstract. The purpose of this study is to explore the application of ARIMA model and deep learning technology in time series diagram prediction and the comparison of their effects. First, this paper analyzes the time series data in detail and models them using the ARIMA model. The ARIMA model effectively captures the seasonal and trending characteristics of the data through autoregressive, moving average, and differential steps. At the same time, the method of introducing deep learning models based on recurrent neural networks (RNNs), especially long short-term memory networks (LSTMs), to deal with the nonlinear characteristics and long-term dependency of time series data. By comparing and analyzing the performance of the ARIMA model and the deep learning model in terms of prediction accuracy, computational efficiency and model generalization ability, finding that the deep learning model has higher prediction accuracy when processing complex time series data. In addition, this study also uses time series diagrams to visually display the prediction results, which further verifies the advantages of deep learning models in capturing dynamic changes in data. This study provides a new perspective and method for time series data analysis and prediction, and has important practical significance for finance, meteorology, energy and other fields.

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.

Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis

Abstract. Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.

Permutation Entropy: An Ordinal Pattern-Based Resilience Indicator for Industrial Equipment

In a highly dynamic and complex environment where risks and uncertainties are inevitable, the ability of a system to quickly recover from disturbances and maintain optimal performance is crucial for ensuring operational continuity and efficiency. In this context, resilience has become an increasingly important topic in the field of engineering and the management of productive systems. However, there is no single quantitative indicator of resilience that allows for the measurement of this characteristic in a productive system. This study proposes the use of permutation entropy of ordinal patterns in time series as an indicator of resilience in industrial equipment and systems. Based on the definition of resilience, the developed method enables precise and efficient assessment of a system’s ability to withstand and recover from disturbances. The methodology includes the identification of ordinal patterns and their analysis through the calculation of a permutation entropy indicator to characterize the dynamics of industrial systems. Case studies are presented and the results are compared with other resilience models existing in the literature, aiming to demonstrate the effectiveness of the proposed approach. The results are promising and highlight a highly applicable and simple indicator for resilience in industrial systems.

Comprehensive comparative analysis of reconstructed sea level datasets in the China Seas: insights from tide gauge and satellite altimetry

At present, there are many reconstructed datasets at the global scale. To test the applicability of these datasets in the China seas, the study comprehensively analyzes the reliability and accuracy of reconstructed sea level datasets in capturing nuanced temporal patterns of sea level changes in the China Seas. This study applied analysis methods or indicators such as time series, Taylor plots, correlation coefficients, growth rates, and standard deviations. Ocean Data Assimilations (ODAs) outperform Tide Gauge Reconstructions (TGRs) in terms of correlation with measured data in the nearshore, while TGRs exhibit superior capability in capturing oceanic sea level variability. Although the ODAs and TGRs both suffer from the underestimation of sea level variability in China as well as in neighboring seas, the TGRs perform better than the former. ODAs show inconsistency in reflecting the rate of sea level rise, but they, particularly the China Ocean Reanalysis (CORA), demonstrate a better correlation with satellite altimetry datasets. Meanwhile, both of them can reflect the Pacific Decadal Oscillation (PDO) well. TGRs, relying on oceanic tide gauge stations, suffer from poor correlation with tide gauge stations due to limited coverage. Reconstruction discrepancies are attributed to methodological differences and data assimilation techniques. Future studies should explore alternative variables like sea surface temperature and so on to enhance sea-level reconstruction, especially in regions with sparse tide gauge coverage.

Turbulence statistics estimation across a step change in roughness via interpretable network-based modelling

Abstract This study proposes a data-driven methodology to complement existing time-series measurement tools for turbulent flows. Specifically, a cluster-based transition network model is employed for the estimation of velocity time traces and their corresponding statistics. The method is tested on a laboratory-modelled turbulent boundary layer over a step change in surface roughness, where velocity time series are recorded for training and validation purposes via Laser Doppler Anemometry. Results show that our approach can estimate velocity and momentum flux statistics within experimental uncertainty over a rough surface through an unsupervised approach, and across the step change in roughness through a semi-supervised variant. The friction velocity across the domain is also estimated with 10\% relative error compared to the measured value. The proposed methodology is interpretable robust against the main methodological parameters. A reliable data-driven framework is hence provided that can be integrated within existing laboratory setups to supplement or partially replace measurement systems, as well as to reduce wind tunnel running times.

Review of Biomass Gasification and Pyrolysis Process Based on Long Short-Term Memory Network

Abstract. With the global energy crisis become escalated day by day, finding an effective way of exploring the renewable energy has become an important direction as its widely resources, comparatively low environment impact. This paper has concluded the biomass pyrolysis and gasification process based on long- and short-term memory (LSTM) method, including the LSTM principle, current application, faced challenges and future development. LSTM as a kind of special recurrent neural network, which could effectively solve the gradient vanish and explosion problem in time-series data. Research shows that, LSTM has an excellent performance in terms of reaction condition and optimization, which could be able to capture the relationship between complex variables, the pyrolysis efficiency could be improved, and prediction accuracy could over 90%. During the data preprocessing and model training stage, it is vital to make sure the quality of data, including data normalisation, outlier handling and time series splitting, etc. Through these steps, a better prediction accuracy could be achieved by LSTM. At the same time, the real application cases of LSTM used in biomass pyrolysis process have been discussed, it is shown that the combination of LSTM and other machine learning techniques could obviously improve the system stability and product quality. In addition, this paper has also looked forward to the enhancement of model explainability and the prospect of uncertainty quantification, which is helpful to improve the LSTM application and practical value.

Maximum-likelihood estimation of the Po-MDDRCINAR(p) model with analysis of a COVID-19 data

Integer-valued data are frequently encountered in time series studies. A pth-order mixed dependence-driven random coefficient integer-valued autoregressive time series model (Po-MDDRCINAR(p)) in view of binomial and negative binomial operators, where the innovation sequence follows a Poisson distribution, is investigated to provide meaningful theoretical explanations. Strict stationary and ergodicity of the model are demonstrated. Furthermore, the conditional least-squares and conditional maximum-likelihood methods are adopted to estimate the parameters, where the asymptotic characterization of the estimators is derived. Finite-sample properties of the conditional maximum-likelihood estimator are examined in relation to the widely used conditional least-squares estimator. The conclusion is that, if the Poisson assumption of the innovation sequence can be justified, conditional maximum-likelihood method performs better in terms of MADE and MSE. Finally, the practical performance of the model is illustrated by a set of COVID-19 data of suspected cases in China with a comparison with relevant models that exist so far in the literature.

Time Series Perspective on the Sustainability of the South African Food and Beverage Sector

This study aims to quantify and graphically illustrate the negative lingering effect that the COVID-19 pandemic had on the sales in South African Rands (ZAR) of the food and beverage sector using the time series seasonal autoregressive integrated moving average with exogenous components (SARIMAX) intervention model. The SARIMAX2, 1, 00, 1, 212 intervention model provided the best fit, supported by the lowest values of the model selection and error metrics (Akaike’s information criterion, Bayesian information criterion, and root mean square error). The total estimated loss of sales in the 52 months during the intervention period (March 2020 to June 2024) amounts to ZAR 130,579 million. The most affected months were April 2020 and May 2020 with estimated losses of ZAR 7719 million and ZAR 7633 million, respectively. The findings of this study align with the Statistics South Africa® statistical report based on empirical estimation without any model fitting, thus highlighting the effectiveness of the SARIMAX intervention model in quantifying the effects of the pandemic. The lingering negative impact of the COVID-19 pandemic still continues to threaten the sustainability of the South African food and beverage sector, violating the United Nations’ Sustainable Development Goal, Number 2, which is to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture.

Exploring the Relationship Between Time Series of Sentinel-1 Interferometric Coherence Data and Wild Edible Mushroom Yields in Mediterranean Forests

Edible wild mushrooms constitute a valuable marketable non-wood forest product with high relevance worldwide. There is growing interest in developing tools for estimation of mushroom yields and to evaluate the effects that global change may have on them. Remote sensing is a powerful technology for characterization of forest structure and condition, both essential factors in triggering mushroom production, together with meteo-climatic factors. In this work, we explore options to apply synthetic aperture radar (SAR) data from C-band Sentinel-1 to characterize, at the plot level, wild mushroom productive forests in the Mediterranean region, which provide saprotroph and ectomycorrhizal mushrooms. Seventeen permanent plots with mushroom yield data collected weekly during the productive season are characterized with dense time series of Sentinel-1 backscatter intensity (VV and VH polarizations) and 6-day interval interferometric VV coherence during the 2018–2021 period. Weekly-regularized series of SAR data are decomposed with a LOESS approach into trend, seasonality, and remainder. Trends are explored with the Theil-Sen test, and periodicity is characterized by the Discrete Fast Fourier transform. Seasonal patterns of SAR time-series are described and related to mycorrhizal and saprotroph guilds separately. Our results indicate that time series of interferometric coherence show cyclic patterns which might be related with annual mushroom yields and may constitute an indicator of triggering factors in mushroom production, whereas backscatter intensity is strongly correlated with precipitation, making noisy signals without a clear interpretable pattern. Exploring the potential of remotely sensed data for prediction and quantification of mushroom yields contributes to improve our understanding of fungal biological cycles and opens new ways to develop tools that improve its sustainable, efficient, and effective management.

Conditional parametric bootstrap in GLARMA models

Due to situations where asymptotic results are not valid or when we do not know the true distributions of the data set, the bootstrap technique stands out as an effective alternative to make inferences about model parameters. In this context, we introduce the conditional parametric bootstrap, applied to time series data, specifically using the Generalized Linear Autoregressive Moving Average (GLARMA) model. Our interest is to use the procedure to correct possible biases and construct confidence intervals and hypothesis testing for the parameters of the GLARMA model. We conducted simulation studies to evaluate the performance of the procedure in finite samples. The results show that the proposed technique is promising and can be applied in practical situations. An example is the analysis of the monthly number of chronic obstructive pulmonary disease (COPD) cases, which illustrates the potential of the proposed approach in a real situation.

Method of bed exit intention based on the internal pressure features in array air spring mattress

With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment.

Making it on the breadline – improving food security on the Anangu Pitjantjatjara Yankunytjatjara Lands, Central Australia

BackgroundThis longitudinal case study describes the efforts and impacts of community-controlled service organisations on the Anangu Pitjantjatjara Yankunytjatjara (APY) Lands in Central Australia to tackle food security since the 1980s, with a focus on the last decade, particularly during a year of concerted action from mid-2018.MethodsThe co-designed study comprised an interrupted time series with controls. Availability, affordability, accessibility and sales of foods in the community retail stores on the APY Lands were monitored regularly from 2014 to mid-2022, including by local research teams. Store nutrition policy was updated early 2018. For a year from mid-2018, of the eight communities with stores: (i) two were the focus for concerted intervention, including support from a locally based project officer to help implement the policy and action 105 community requests for nutrition activities (ii) three received usual support to implement the policy; and (iii) three were subject to ‘business as usual’. From mid-2019, all communities/stores received usual service, from 2020 with some restrictions related to the COVID-19 pandemic. Results were compared over time, across different community/store groups and with controls.ResultsIn the 12 months from mid-2018, all food security metrics improved most in the two focus communities. Impacts were less marked in the communities without additional support to implement the revised nutrition policy, and even less apparent, although more varied, in the other three communities/stores. Dietary intake improved only in the two focus communities.In all communities from early 2020 most gains eroded due to impacts of the COVID-19 pandemic and other external stressors. Food security metrics, including price of healthy food, appeared more resilient in the focus communities, although diet quality worsened. At all times assessed, healthy diets were unaffordable for welfare-dependant households.ConclusionsThis co-designed study demonstrates the effectiveness of community-led approaches, confirming that it is possible to improve food security and diet in remote Aboriginal communities. However, sustained action and monitoring, dedicated resources and employment of local people are critical for success. Results also highlight that low incomes are a major barrier to food security.

Examining the Impact of COVID-19 on Early Intervention Service Provision for Military Families: A Brief Report

Understanding the extent to which the COVID-19 pandemic disrupted birth-to-three early intervention (EI) service provision is critical as these services play an essential role in ensuring positive long-term developmental outcomes for children with disabilities, including those who are military dependents. The present study leveraged an interrupted time series approach to compare monthly service provision rates before and after the onset of the pandemic for a subset of children served by the Army Medical Department’s Educational and Developmental Intervention Services EI program. Analysis identified 77% and 93% immediate step decreases in EI referral and enrollment rates, respectively, following the onset of the pandemic in March 2020; however, rates returned to, or exceeded, prepandemic levels by late 2020. There were limited differences by race or ethnicity. Additional research is necessary to explore the mechanisms underlying these changes and to understand the long-term impact of missed referrals and enrollments on military children.