Time Series Analysis Approach Research Articles

Impact of Average Atmospheric Temperature on Rainbow Trout Production in South Africa: A Time Series Analysis Approach

Climate change has various effects on the global food supply and the environment as a whole. Similarly, the production of fish like Rainbow Trout is affected by variation in the atmospheric temperature higher than 21oC, leading to fluctuations in yield. Therefore, this study sought to investigate the impact of average atmospheric temperature on Rainbow Trout production in South Africa using time series data spanning from 2000 to 2022. The unit root test, Johannsen cointegration test, and the Vector Error Correction model were employed to analyse the data. The results show that the change in average atmospheric temperature affects the production of Rainbow Trout. The correlation between average temperature and Rainbow Trout production is positive in the short-run and long-run. I Prolonged exposure to high temperature may lead to fish stress, migration and increased susceptibility to diseases which ultimately affects the economic viability of the sector in both the short-run and the long-run. In addition, the overall change in the atmospheric temperature has a negative effect on the production of the fish highlighting the need for climate change intervention to sustain fish production. The study recommends implementing strategies that reduce the impact of temperature changes on fish production to support both economic growth and food security.

Forecasting the Volatility of Tuna Fish Prices in North Sumatra using the ARCH Method in the Period January - April 2024

Tuna (Euthynnus affinis) is one of the most important fisheries commodities in Indonesia with significant economic value, especially in its contribution to fisheries export revenue. However, the price of tuna experiences significant fluctuations that can affect local and national economic stability. This study analyzes the daily price fluctuations of tuna in the North Sumatra market from January 1, 2024 to April 29, 2024 using a time series analysis approach. Daily price data were collected and analyzed to identify existing price patterns and volatility. The Autoregressive Conditional Heteroskedasticity (ARCH) model was selected to address the heteroscedasticity in the data, which suggests that the volatility of tuna prices can be well predicted based on past price behavior. The analysis steps include identifying the optimal ARCH model using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), as well as testing parameter significance and normality assumptions to validate the model fit. The results show that the ARMA (1,0,0) model is the optimal one to model the price volatility of yellow tuna with the MAPE obtained of 2.382. compared to the ARMA-ARCH method with the MAPE value obtained of 2,747. Because it still contains heteroskedasticity effects, even though the results are good, the prediction results do not closely match the original data. The model is effective in improving price forecasting accuracy, which is important to support decision-making in risk management and economic planning in the fisheries sector. The findings contribute to understanding the dynamics of the yellowtail market and optimizing strategies for fisheries management.

Time Series Analysis to Estimate the Volume of Drinking Water Consumption in the City of Meoqui, Chihuahua, Mexico

Water is a vital resource for sustaining life and for numerous processes within the transformation industry. It is a finite resource, albeit one that can be renewed, and thus sustainable management is imperative. To achieve this objective, it is necessary to have the appropriate tools to assist with the planning policies for its management. This paper presents a time series analysis approach to measure and predict the pattern of water consumption by humans throughout subsectors (domestic, commercial, public sector, education, industry, and raw water) and total water consumption in Meoqui, Chihuahua, Mexico with data from 2011 to 2023, applying calibration model techniques to measure uncertainty in the forecasting. The municipality of Meoqui encompasses an area of 342 km2. The climate is semi-arid, with an average annual rainfall of 272 mm and average temperatures of 26.4 °C in summer and 9.7 °C in winter. The municipal seat, which has a population of 23,140, is supplied with water from ten wells, with an average consumption of 20 ± 579 m3 per user. The consumption of the general population indicates the existence of a seasonal autoregressive integrated moving average (SARIMA) (0,1,2)(0,0,2)12 model. (Sen’s Slope = 682.7, p < 0.001). The domestic sector exhibited the highest overall consumption, with a total volume of 17,169,009 m3 (13 ± 93). A SARIMA (2,1,0)(2,0,0)12 model was estimated, with a Sen’s slope of 221.65 and a p-value of less than 0.001. The second-largest consumer of total water was the “raw water” sector, which consumed 5,124,795 (30,146 ± 35,841) m3 and exhibited an SARIMA (0,1,1)(2,0,0)12 model with no statistically significant trend. The resulting models will facilitate the company’s ability to define water resource management strategies in a sustainable manner, in alignment with projected consumption trends.

Auto-Scaling Techniques in Cloud Computing: Issues and Research Directions.

In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in improving cost efficiency, performance, and energy consumption in cloud services. We then discuss various strategies employed in auto-scaling, ranging from threshold-based rules and queuing theory to sophisticated machine learning and time series analysis approaches. After that, we explore the critical issues in auto-scaling practices and review several studies that demonstrate how these challenges can be addressed. We then conclude by offering insights into several promising research directions, emphasizing the development of predictive scaling mechanisms and the integration of advanced machine learning techniques to achieve more effective and efficient auto-scaling solutions.

Development, Calibration and Validation of Time Series Analysis and Artificial Neural Network Joint Model for Urban Noise Prediction

Noise in large urban areas, which is mainly generated by road traffic and by the human activities carried out nearby and inside the area under study, is a relevant problem. The continuous exposure to high noise levels, in fact, can lead to several problems, largely documented in the scientific literature. The analysis and forecasting of the noise level in a given area are, then, fundamental for control and prevention, especially when field measurements present peculiar trends and slopes, which can be modeled with a Time Series Analysis approach. In this paper, a hybrid model is presented for the analysis and the forecasting of noise time series in urban areas: this technique is based on the application of a deterministic decomposition model followed in cascade by a predictor of the forecasting errors based on an artificial neural network. Two variants of the hybrid model have been implemented and presented. The time series used to calibrate and validate the model is composed of sound pressure level measurements detected on a busy road near the commercial port of an Italian city. The proposed hybrid model has been calibrated on a part of the entire time series and validated on the remaining part. Residuals and error analysis, together with a detailed statistical description of the simulated noise levels and error metrics describe in detail the method’s performances and its limitations.

Modeling epidemic dynamics using Graph Attention based Spatial Temporal networks.

The COVID-19 pandemic and influenza outbreaks have underscored the critical need for predictive models that can effectively integrate spatial and temporal dynamics to enable accurate epidemic forecasting. Traditional time-series analysis approaches have fallen short in capturing the intricate interplay between these factors. Recent advancements have witnessed the incorporation of graph neural networks and machine learning techniques to bridge this gap, enhancing predictive accuracy and providing novel insights into disease spread mechanisms. Notable endeavors include leveraging human mobility data, employing transfer learning, and integrating advanced models such as Transformers and Graph Convolutional Networks (GCNs) to improve forecasting performance across diverse geographies for both influenza and COVID-19. However, these models often face challenges related to data quality, model transferability, and potential overfitting, highlighting the necessity for more adaptable and robust approaches. This paper introduces the Graph Attention-based Spatial Temporal (GAST) model, which employs graph attention networks (GATs) to overcome these limitations by providing a nuanced understanding of epidemic dynamics through a sophisticated spatio-temporal analysis framework. Our contributions include the development and validation of the GAST model, demonstrating its superior forecasting capabilities for influenza and COVID-19 spread, with a particular focus on short-term, daily predictions. The model's application to both influenza and COVID-19 datasets showcases its versatility and potential to inform public health interventions across a range of infectious diseases.

Agricultural Production and Agricultural Employment Rate in South Africa: Time Series Analysis Approach

South Africa's agricultural sector, despite facing high unemployment, is acknowledged for creating job opportunities. However, there is concern that the sector's adoption of technology-intensive methods may not necessarily lead to increased employment. The study aims to analyze the intricate relationship between agricultural output and employment in South Africa using time series analysis. This research employed a Vector Autoregressive (VAR) model to evaluate the link between agricultural production and the rate of agricultural employment in South Africa spanning from 1990 to 2022. The findings indicate that both variables passed levels and became stationarity at the first difference when employing ADF, and a long-term equilibrium relationship between the variables was observed using Johansen cointegration test. Over the short term, there was a significant positive correlation among agricultural production and the agricultural employment rate evidenced from ECT coefficient of 0.139 value. The results of the Granger causality tests indicated unidirectional relationship that agricultural employment Granger-causes agricultural production, signifying that agricultural employment can be used to predict the growth of agricultural production. Study recommended that policies which promote injection of funds to improve production in the agriculture sector needs to be prioritized to maintain and improve employment opportunities.

A Smart Approach For Budget Deficits PredictionUnder Economic Shocks

Historically, the main behavior of fiscal policy is to distribute resources, income, and expenditures, which are interconnected functions of economic stability. Recently, the scope of public sector economics has expanded beyond budgetary components in parallel with the development of public finance. While the budget reached the economic division form of budget items based on program and performance theories, Budget monitoring, and financial risk management are currently challenging, particularly in the face of monetary policy uncertainty. Financial institutions are crucially concerned with the stability of public finance in low-income countries (LICs) as it contributes to improved investor confidence and fiscal decision-making. Hence, economists investigated uncertainty shocks and contributed to managing financial risks with global and energy uncertainty indices. Furthermore, the maturity of digital transformation and artificial intelligence financial applications catalyzed scholars to examine its contributions in the fiscal distress prediction field. Hence, this research aims to integrate artificial intelligence into financial performance analysis to bridge the gap in budget forecasting. The study was aimed at proposing an Economics Division Uncertainty approach (EDUA), which combined (ARIMA) and (LSTM) models for time series analysis of Nuclear Material Authority expenditures over the previous five years divided into quarterly periods, to achieve efficiency in spending. The (ARIMA) model’s (ADF) results showed that uncertainty indicators are highly significant. The best (p-value) in the first and second differences in (ARIMA) models is (0.0001) for petroleum items, (0.0001) for solar price rates, (0.001) for the US exchange rate, and (0.003) for electricity price rates, when compared to (EDU_LSTM). Both models have similar accuracy rates, with the best being (EDU_ARIMA) (solar price 97%, USD exchange rate 84%). The second study proposed a composite model of four machine-learning tools to enhance financial performance during financial distress. The study collected (12) indicators from general financial literature and corporate studies, utilizing the (XGBOOST, Random Forest, KNN, and Naïve Bayes) models. Comparing the accuracy results for each model presented different accuracy results in the deep learning models over five years of data. The best accuracy score was for Random Forest at (69%), XGBOOT at (68%), and KNN at (68%). We recommended explainable AI as future research to interpret the budget deficit during the fiscal year period. Keywords: Economic Shocks, Uncertainty, Budget Reliability, Financial Distress Prediction, Artificial Intelligence.

Changes in pCO2 and climate paced by grand orbital cycles in the late Cenozoic

As one of the most important greenhouse gases, CO2 is considered a major controlling factor of Earth's climate over geological timescales. However, the origins of quasi-periodic fluctuations in pCO2 on a million-year timescale remain unclear. Here, we used published datasets of atmospheric pCO2, oxygen isotopes of benthic foraminifera (δ18Obenthic) and global mean sea-level (GMSL) from 23 Ma to the present to explore the pacing of pCO2 changes and concomitant climatic effects using multiple time series analysis approaches. Our results indicate that the evolution of late Cenozoic pCO2 and climate was paced by the grand orbital cycles, in particular the ∼4.5 Myr and ∼2.4 Myr eccentricity cycles, and ∼1.3 Myr obliquity cycle. Periodic occurrence of cold conditions was associated with low climate seasonality during the minima of ∼4.5 Myr and ∼2.4 Myr eccentricity cycles. We suggest that cooler conditions are associated with decreased atmospheric pCO2 as a result of higher organic carbon burial due to lower metabolic rate of heterotrophic bacteria and more organic carbon export to the deep ocean. Furthermore, the buildup of glaciers during the minima of grand eccentricity cycles might lower pCO2 via increased ice cover and enhanced dust fluxes. In contrast, high seasonal climate may lead to an opposite effect on atmospheric pCO2 during the maxima of the grand eccentricity cycles. Moreover, we found a distinct shift in the dominant signal from eccentricity to obliquity cycles recorded in the pCO2, δ18Obenthic and GMSL datasets at ∼13 Ma, a time when perennial sea ice occurred in the Arctic and significant ice growth shown in Antarctica. We suggest that the change in the type and distribution of the ice sheets would shift glacial response to orbital forcing and hence mediated global climate and pCO2. Our analysis reveals a clear synchrony among atmospheric pCO2, climate change, and the grand orbital cycles in the late Cenozoic.

EXPLORING CEEMDAN DECOMPOSITION FOR IMPROVED FINANCIAL MARKET FORECASTING: A CASE STUDY ON DOW JONES INDEX

This study presents an innovative financial time series analysis approach by integrating Complete Ensemble Empirical Mode Decomposition (CEEMDAN) with the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) model. The primary contribution of the research lies in significantly enhancing the predictive accuracy and understanding of the dynamics governing major stock indices. CEEMDAN adaptively decomposes complex financial time series into intrinsic mode functions (IMFs), a technique that has yet to be extensively utilized in this domain. IMFs are combined with ARIMAX's predictive proficiency, which accounts for the influence of historical trends and external factors. Our study showcases an R² of 0,93, aligning with some of the high-performing models in the literature. However, the unique strength of our model lies in its lag-free predicting of the DJIA, effectively mirroring its volatility and major movements with high fidelity, making it highly practical for financial applications.

The relationship between finance and growth in Germany and Luxembourg: A timeseries analysis approach

The present paper examines the economic growth and financial development relationship in two of the European Union’s founding countries and Eurozone members, namely Germany and Luxembourg, by taking into consideration a large period, from 1970 to 2019. We motivate the choice of these specific two countries based on the similar rate of GDP per capita growth over the years, and also on the relevance of the financial sector in the total economy. Five different measures of financial development are employed to address the depth and efficiency of the financial sector. We apply Granger causality tests, using the cointegration and Vector Error-Correction (VEC) methodology. The empirical analysis indicates consistent results, as both in the case of Germany and Luxembourg it can be established Granger causality for the economic growth – financial development nexus. There is also evidence of bi-directional relationship.

Analysis and future projections of the electricity demands of the Jordanian household sector using artificial neural networks

Purpose The continuous increase of energy demands is a critical worldwide matter. Jordan’s household sector accounts for 44% of overall electricity usage annually. This study aims to use artificial neural networks (ANNs) to assess and forecast electricity usage and demands in Jordan’s residential sector. Design/methodology/approach Four parameters are evaluated throughout the analysis, namely, population (P), income level (IL), electricity unit price (E$) and fuel unit price (F$). Data on electricity usage and independent factors are gathered from government and literature sources from 1985 to 2020. Several networks are analyzed and optimized for the ANN in terms of root mean square error, mean absolute percentage error and coefficient of determination (R2). Findings The predictions of this model are validated and compared with literature-reported models. The results of this investigation showed that the electricity demand of the Jordanian household sector is mainly driven by the population and the fuel price. Finally, time series analysis approach is incorporated to forecast the electricity demands in Jordan’s residential sector for the next decade. Originality/value The paper provides useful recommendations and suggestions for the decision-makers in the country for dynamic planning for future resource policies in the household sector.

Analysis of Asset Management Ratio and Solvency Management Ratio

Profit is total income after deducting company costs or what can be called net profit. Profits generated by a company can be a benchmark for a company's success. To gain large profits, a company must be able to manage assets effectively and efficiently, because this can show that the company has good financial performance. One of the ratios that investors most often consider is the total assets turnover ratio. However, often a high asset management ratio does not indicate the company's effectiveness in managing high assets, but because inventory levels are low. A company is said to be insolvent when its debt position is greater than the total assets owned by the company. To find out whether it is solvable or not, it can be calculated using the debt to equity ratio. PT FKS Food Sejahtera Tbk (IDX: AISA) is a food producing company headquartered in Jakarta, Indonesia. This company was founded in 1990, with the main products being various types of ready-made food. The purpose of this research is to analyze asset management ratio and solvency management ratio PT FKS Food Sejahtera Tbk. The type of research in this research uses qualitative descriptive research. The subject of this research is PT FKS Food Sejahtera Tbk whose financial report data is from 2015 to 2021. The object of this research is asset management ratio and solvency management at PT FKS Food Sejahtera Tbk. The analysis method in this research uses a time series analysis approach. The results of this research show that the average fixed asset turnover of PT FKS Food Sejahtera Tbk is 1.15 times. The average total asset turnover of PT FKS Food Sejahtera Tbk is 0.7 times. The average debt ratio at PT FKS Food Sejahtera Tbk from 2015 to 2017 has a value of 1.38 which becomes unsafe in 2017, 2018, and 2019. The multiple that received the highest profit was in 2019 with a multiple of 12.15.

Refined composite multiscale dynamic causality diagram: a novel time-series analysis approach and its application in fault diagnosis of rotating machinery

Entropy-based feature extraction methods have been widely used in the fault diagnosis of rotating machinery, but the entropy-based methods still have the defects of poor noise robustness, weak feature extraction, and low computational efficiency. To solve this problem, this article proposes a fault diagnosis method based on refined composite multiscale dynamic causal diagram (RCMSDCD) and local receptive field extreme learning machine (LRFELM). First, a novel feature extraction method, named dynamic causal diagram (DCD), is proposed to comprehensively quantify static and dynamic complexity. DCD is obtained by combining generalized inverse fractional order entropy with complexity–entropy causal plane. Then, combined with the coarse-graining process, DCD is extended to a multiscale analysis called RCMSDCD to complement the feature description at cross-time scales. Third, RCMSDCD features are input into LRFELM classifier for fault recognition of rotating machinery. The effectiveness of the proposed RCMSDCD-LRFELM method is verified by the Paderborn University bearing test and real wind turbine gear signals. The results show that this method has the highest classification accuracy of 100% with high computational efficiency, good stability, and strong generalization ability.

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection.

Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. These approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and introduce mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach

ATTRIBUTION ANALYSIS OF CLIMATE CHANGE AND HUMAN ACTIVITIES TO WATER VOLUME VARIATION IN LARGE LAKES

Abstract. Lakes are important components of the Earth's surface water bodies, and their fluctuating water levels and changing water volumes can have an important impact on ecological processes and landscape patterns within a watershed. Climate change and human activities are the main drivers of water volume changes in lakes. We applied a long time series analysis approach using classical statistics and geographic information system (GIS) technics to quantitatively attribute water volume variations in Lake Victoria from 2000 to 2019. We examined several drivers of lake water volume variations: meteorological factors including regional climate change (precipitation, evaporation) and global climate change (El Niño/Southern Oscillation events), and anthropogenic factors including socio-economic development and land use/cover change. The results show that regional climate change has a greater impact on lake water volume variations than global climate change; among them, the correlation coefficient between precipitation and lake water volume variations is the largest (0.410), with an impact lag of 1 month. For anthropogenic factors, variations in lake water volume are more pronounced in areas with rapid economic development and high population growth, with correlation coefficients greater than 0.440. Different types of land use/cover have varying impacts on Lake Victoria's water volume variations. Overall, the influence of anthropogenic factors is greater than that of meteorological factors. This study results not only provides a scientific basis for exploring the hydrological changes and their drivers in the world's large transboundary lakes, but also be great scientific significance to rational use of water resources and sustainable regional development.

On the price dynamics of non-fungible tokens: The 'Bored Apes' case.

We analyse the pattern of daily price of a collection of artistic non-fungible tokens, namely, the "Bored Ape Yacht Club" (BAYC) collectibles, over the first year of their life, from May 2021 to May 2022. Taking a time-series analysis approach, we consider the daily average price, and other variants of daily price index, derived from hedonic regression model. Aesthetic features of the collectibles do matter. At the same time, the price series emerge to be non-stationary, integrated of order 1, with their first difference exhibiting heteroscedasticity and autoregressive variance. Models of ARCH/GARCH class are appropriate to describe the dynamics. Though the price series of BAYC collectibles and their daily movements share many characteristics with the series of financial assets, they do not appear to be related to financial variables from both the crypto- and the real (i.e., not crypto) world.

Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach

A popular self-normalization (SN) approach in time series analysis uses the variance of a partial sum as a self-normalizer. This is known to be sensitive to irregularities such as persistent autocorrelation, heteroskedasticity, unit roots and outliers. We propose a novel SN approach based on the adjusted-range of a partial sum, which is robust to these aforementioned irregularities. We develop an adjusted-range based Kolmogorov–Smirnov type test for structural breaks for both univariate and multivariate time series, and consider testing parameter constancy in a time series regression setting. Our approach can rectify the well-known power decrease issue associated with existing self-normalized KS tests without having to use backward and forward summations as in Shao and Zhang (2010), and can alleviate the “better size but less power” phenomenon when the existing SN approaches (Shao, 2010; Zhang et al., 2011; Wang and Shao, 2022) are used. Moreover, our proposed tests can cater for more general alternatives. Monte Carlo simulations and empirical studies demonstrate the merits of our approach.

Emotional states predict cellular immune system activity under conditions of life as it is lived: A multivariate time-series analysis approach.

The relationship between emotional states and immune system activity is characterized by bidirectional influences; however, limited information is available regarding the temporal dynamics of these effects. The goal of this investigation was to examine how these psychoimmunological interdependencies unfold over time under conditions of "life as it is lived". For this purpose, three healthy women collected their entire urine over a period of approximately two months at 12-h intervals (8 am-8 pm, 8 pm-8 am), resulting in a total of 112 to 126 consecutive measurements per subject. In addition, among other regular psychological assessments, the subjects completed the EWL-60-S, an emotional state questionnaire, each morning and evening. To assess the extent of T-helper type 1 immune activation, the neopterin per creatinine concentration was measured in the urine samples using high-pressure liquid chromatography. The dynamic relationships between the time series of the six emotional states (performance-related activity, general inactivity, extraversion/introversion, general feeling of comfort, emotional irritation, anxiety/depressiveness) and urinary neopterin levels were estimated in vector-autoregressive models and evaluated using Granger-causality tests, impulse-response functions and forecast error variance decompositions. The findings showed that emotional states explained up to 20% of the variance of urinary neopterin per creatinine levels, whereby most of the effects occurred within a period of approximately three days. Across all subjects, increases in anxiety/depressiveness and extraversion led to increases in neopterin levels, while a general feeling of comfort led to decreases in neopterin. These results emphasize the importance of the interdependencies between emotional states and immune system activity and showcase the potential that intensive longitudinal study designs offer for psychoneuroimmunology.