Behavior Of Time Series Research Articles

Predicting Apple Stock Price Based on ARIMA Model

ARIMA stands for Auto-Regressive Integrated Moving Average and is a popular statistical model to forecast time-series. It has since been used to predict the behaviour of time series based on historical data, and a wide range of applications in which it is useful like stock market forecasting, economic growth analysis, etc. While the ARIMA model has significant benefits, it is a model with limitations, particularly when the underlying data are polluted by noise or present non-linear patterns. Long-term forecasting with ARIMA is less accurate because it is a linear model which does not always accurately reflect the complexity or immediate changes found in most real-world financial data. Our focus will be on Apple stock price data from 2014 to 2024 (working days for simplicity), which we analyze using ARIMA model, and we compare with other analysis models. In this analysis, we would like to point out two things why ARIMA models are not suitable for longer-term market predictions and demonstrate how poorly it fits on nonlinear data.

Monitoring of networked RTK reference stations for coordinate reference system realization and maintenance – case study of the Czech Republic

Abstract Networked Real-Time Kinematic (NRTK) is the most widely used GNSS (Global Navigation Satellite Systems) positioning technique in surveying practice due to its speed, operability and sufficient accuracy. Positioning using NRTK methods in the European Terrestrial Reference System ETRS89 obligatory coordinate system is tied to the positioning of reference stations of the RTK network. Monitoring of GNSS reference stations provides both the determination of the position of individual stations in the obligatory coordinate system and the monitoring of daily position deviations of individual stations. The application to four networks of GNSS reference stations in the Czech Republic has demonstrated the accuracy of the positioning of stations from the daily solution at the level of 1.2 mm in the horizontal components and 3.8 mm in the vertical component. This accuracy is quite suitable for NRTK methods that determine position with centimetre accuracy. At the same time, it allows to detect the behaviour of time series of coordinates of individual stations – sudden jumps, linear and periodic trends. The accuracy achieved also allows tracking geokinematic movements.

Object oriented data analysis of surface motion time series in peatland landscapes

Abstract Peatlands account for 10% of UK land area, 80% of which are degraded to some degree, emitting carbon at a similar magnitude to oil refineries or landfill sites. A lack of tools for rapid and reliable assessment of peatland condition has limited monitoring of vast areas of peatland and prevented targeting areas urgently needing action to halt further degradation. Measured using interferometric synthetic aperture radar (InSAR), peatland surface motion is highly indicative of peatland condition, largely driven by the eco-hydrological change in the peatland causing swelling and shrinking of the peat substrate. The computational intensity of recent methods using InSAR time series to capture the annual functional structure of peatland surface motion becomes increasingly challenging as the sample size increases. Instead, we utilize the behaviour of the entire peatland surface motion time series using object oriented data analysis to assess peatland condition. Bayesian cluster analysis based on the functional structure of the surface motion time series finds areas indicative of soft/wet peatlands, drier/shrubby peatlands, and thin/modified peatlands. The posterior distribution of the assigned peatland types enables the scale of peatland degradation to be assessed, which will guide future cost-effective decisions for peatland restoration.

Modified multiscale Renyi distribution entropy for short-term heart rate variability analysis.

Multiscale sample entropy (MSE) is a prevalent complexity metric to characterize a time series and has been extensively applied to the physiological signal analysis. However, for a short-term time series, the likelihood of identifying comparable subsequences decreases, leading to higher variability in the Sample Entropy (SampEn) calculation. Additionally, as the scale factor increases in the MSE calculation, the coarse-graining process further shortens the time series. Consequently, each newly generated time series at a larger scale consists of fewer data points, potentially resulting in unreliable or undefined entropy values, particularly at higher scales. To overcome the shortcoming, a modified multiscale Renyi distribution entropy (MMRDis) was proposed in our present work. The MMRDis method uses a moving-averaging procedure to acquire a family of time series, each of which quantify the dynamic behaviors of the short-term time series over the multiple temporal scales. Then, MMRDis is constructed for the original and the coarse-grained time series. The MMRDis method demonstrated superior computational stability on simulated Gaussian white and 1/f noise time series, effectively avoiding undefined measurements in short-term time series. Analysis of short-term heart rate variability (HRV) signals from healthy elderly individuals, healthy young people, and subjects with congestive heart failure and atrial fibrillation revealed that MMRDis complexity measurement values decreased with aging and disease. Additionally, MMRDis exhibited better distinction capability for short-term HRV physiological/pathological signals compared to several recently proposed complexity metrics. MMRDis was a promising measurement for screening cardiovascular condition within a short time.

Persistent Behavior in Energetic Neutral Atom Time Series from IBEX

We investigate the long-term persistence of the time series of energetic neutral atom (ENA) fluxes recorded by the Interstellar Boundary Explorer (IBEX). ENAs provide global information about the outer heliosphere and its interactions with the very local interstellar medium. To avoid the IBEX Ribbon, here, we focus our analysis solely on the polar regions N60°–N90° and S60°–S90°. Each year, IBEX takes two measurements of every pixel in the sky. We make use of the whole set of 14 yr of IBEX data and adhere to the correct time order for the construction of the flux time series. We examine in detail both the trend and the fluctuations of these time series. Using modern methods of time series analysis and persistence characterization, we show that the time series (i) trend is influenced by the solar cycle, (ii) persistence can be established independently of the presence of this trend, and (iii) statistical properties of the fluctuations differs between north and south, pointing to the existence of anisotropy and thus a north–south asymmetry.

An analytical approach for identifying trend-seasonal components and detecting unexpected behaviour in psychological time-series.

The recent advances in technological capabilities have led to a massive production of time-series data and remarkable progress in longitudinal designs and analyses within psychological research. However, implementing time-series analysis can be challenging due to the various characteristics and complexities involved, as well as the need for statistical expertise. This paper introduces a statistical pipeline on time-series analysis for studying the changes in a single process over time at either a population or individual level, both retrospectively and prospectively. This is achieved through systemization and extension of existing modelling and inference techniques. This analytical approach enables practitioners not only to track but also to model and evaluate emerging trends and apparent seasonality. It also allows for the detection of unexpected events, quantifying their deviations from baseline and forecasting future values. Given that other discernible population- and individual-level changes in psychological and behavioural processes have not yet emerged, continued surveillance is warranted. A near real-time monitoring tool of time-series data could guide community psychological responses across multiple ecological levels, making it a valuable resource for field practitioners and psychologists. An empirical study is conducted to illustrate the implementation of the introduced analytical pipeline in practice and to demonstrate its capabilities.

Exploring Price Patterns of Vegetables with Recurrence Quantification Analysis

This study investigates the time-series behavior of vegetable prices in the Central Market of Thessaloniki, Greece, using Recurrence Plot (RP) analysis and Recurrence Quantification Analysis (RQA), which considers non-linearities and does not necessitate stationarity of time series. The period of study was 1999–2016 for practical and research reasons. In the present work, we focus on vegetables available throughout the year, exploring the dynamics and interrelationships between their prices to avoid missing data. The study applies RP visual inspection classification, a clustering based on RQA parameters, and a classification based on the RQA analysis graphs with epochs for the first time. The aim of the paper was to investigate the grouping of products based on their price dynamical behavior. The results show that the formed groups present similarities related to their use as dishes and their way of cultivation, which apparently affect the price dynamics. The results offer insights into market behaviors, helping to inform better management strategies and policymaking and offer a possibility to predict variability of prices. This information can interest government policies in various directions, such as what products to develop for greater stability, identity for fluctuating prices, etc. In future work, a larger dataset including missing data could be included, as well as a machine-learning algorithm to classify the products based on the RQA with epochs graphs.

Implementation of the ARIMA model for prediction of economic variables: evidence from the health sector in Brazil

In recent decades, quantitative models for forecasting economic crises have garnered significant interest from researchers, policymakers, and public and private institutions. Identifying the most appropriate models for predicting economic time series behaviors during crises is a pressing challenge. Effective techniques can be instrumental in forestalling financial irregularities, thus empowering institutions to deploy remedial actions and swiftly minimizing further economic setbacks. Contemporary literature introduces various forecasting models, such as the autoregressive integrated moving average (ARIMA) model. Recognized for its statistical alignment with numerous linear models, the ARIMA model has demonstrated its efficacy across various domains. This paper delves into applying the ARIMA model to predict five critical economic time series that substantially influenced Brazil’s public and private healthcare sectors throughout the economic crisis between 2000 and 2020. These time series encompassed the variables (i) the gross domestic product—GDP, (ii) the Extended National Consumer Price Index—IPCA, (iii) the unemployment rate, (iv) the total number of health plan beneficiaries, and (v) total number of individual health plan beneficiaries. Importantly, this study provides a comprehensive outline of the ARIMA implementation process, underscoring that precise forecasting is pivotal for managers aiming to curtail financial anomalies and avert resource shortages. The findings highlight the ARIMA model’s (1, 0, 2), (2, 2, 1), (0, 1, 2), (1, 1, 2), and (2, 2, 1) viability in accurately forecasting health-related time series, exceeding 95% accuracy for economic variables analyzed. These results have significant practical implications for healthcare managers and decision-makers. By offering accurate forecasts of critical economic metrics, such as the unemployment rate and the transition of beneficiaries between public and private health systems during economic downturns, this research provides valuable insights for strategic planning within the healthcare sector.

Time series features and fuzzy memberships combination for time series classification

Time series classification is an increasingly attractive field with the appearance of new problems in an expanding digitalized world. Most of the proposals in the state-of-the-art have focused just on improving the results’ performance, leaving interpretability on a secondary level. The available interpretable proposals do not provide competitive results, which is an issue to be addressed. This paper introduces a new fuzzy feature-based time series classification method, which joins the ability of time series features to capture essential information about the time series with Fuzzy logic. This proposal allows the fuzzy-based approach to incorporate global information about the behavior of time series in the membership calculation with the aim of improving the performance and interpretability of the results by using an interpretable classifier. The proposed method has been evaluated over the 112 state-of-the-art time series classification datasets from the UCR repository, and the results obtained show a better performance. Furthermore, the combination of time series features and fuzzy memberships has also increased the interpretability of final models.

Temporal assessment of Gross alpha emissions from the petroleum industry in Tenerife, Canary Islands (2001–2022)

A ca. 76% decrease in gross alpha activity levels, measured in surface aerosols collected in the city of Santa Cruz de Tenerife (Spain), has been explained in the present study in connection with the reduction of activities, and eventual closure, of an oil refinery in the city. Gross Alpha in surface aerosols, collected at weekly intervals over a period of 22 years (2001–2022), was used for the analysis. The dynamic behaviour of the gross alpha time series was studied using statistical wavelet, multifractal analysis, empirical decomposition method, multivariate analysis, principal component, and cluster analyses approaches. This was performed to separate the impact of other sources of alpha emitting radionuclides influencing the gross alpha levels at this site. These in-depth analyses revealed a noteworthy shift in the dynamic behaviour of the gross alpha levels following the refinery's closure in 2013. This analysis also attributed fluctuations and trends in the gross alpha levels to factors such as the 2008 global economic crisis and the refinery's gradual reduction of activity leading up to its closure. The mixed-model approach, incorporating multivariate regression and autoregressive integrated moving average methods, explained approximately 84% of the variance of the gross alpha levels. Finally, this work underscored the marked reduction in alpha activity levels following the refinery's closure, alongside the decline of other pollutants (CO, SO2, NO, NO2, Benzene, Toluene and Xylene) linked to the primary industrial activity in the municipality of Santa Cruz de Tenerife.

Singular Spectrum Analysis of Exoplanetary Transits

Transit photometry is currently the most efficient and sensitive method for detecting extrasolar planets (exoplanets) and a large majority of confirmed exoplanets have been detected with this method. The substantial success of space-based missions such as NASA’s Kepler/K2 and Transiting Exoplanet Survey Satellite has generated a large and diverse sample of confirmed and candidate exoplanets. Singular spectrum analysis (SSA) provides a useful tool for studying photometric time series and exoplanetary transits. SSA is a technique for decomposing a time series into a sum of its main components, where each component is a separate time series that incorporates specific information from the behavior of the initial time series. SSA can be implemented for extracting important information (such as main trends and signals) from the photometry data or reducing the noise factors. The detectability and accurate characterization of an exoplanetary transit signal is principally determined by its signal-to-noise ratio (S/N). Stellar variability of the host star, small planet to star radius ratio, background noises from other sources in the field of observations and instrumental noise can cause lower S/Ns and consequently, more complexities or inaccuracies in the modeling of the transit signals, which in turn leads to the inaccurate inference of the astrophysical parameters of the planetary object. Therefore, implementing SSA leads to a more accurate characterization of exoplanetary transits and is also capable of detecting transits with low S/Ns (S/N < 10). In this paper, after discussing the principles and properties of SSA, we investigate its applications for studying photometric transit data and detecting low S/N exoplanet candidates.

Forecasting the waste production hierarchical time series with correlation structure

AbstractContinuous increase in society’s prosperity causes overwhelming growth of the produced municipal solid waste. Circular economy initiatives help to solve this problem by creating closed production cycles, where the produced waste is recycled, or its energy is recovered. An embedment of such principles requires implementation of new waste management strategies. However, these novel strategies must be based on the accurate forecasts of future waste flows. Municipal solid waste production data demonstrate behavior of hierarchical time series. Among all possible approaches to hierarchical times series forecasting, this article is focused on the reconciliation of the base waste generation forecasts. The novel method, that is based on the game-theoretically optimal reconciliation of hierarchical time series, is presented. The modified approach enables to incorporate interdependencies between time series using correlation matrix and to obtain the forecasts corresponding to the unique solution of the optimization problem. The potential of the proposed abstract approach is demonstrated on the waste production data of paper, plastics (both primarily sorted by households), and mixed municipal solid waste from the Czech Republic.

Persistent Behavior in Solar Energetic Particle Time Series

We investigate the long-term persistence of solar energetic particle (SEP) time series by means of four different methods: Hurst rescaled range R/S analysis, detrended fluctuation analysis, centered moving average analysis, and the fluctuation of natural time under the time reversal method. For these analyses, we use data sets from the Integrated Science Investigation of the Sun instrument suite on board NASA's Parker Solar Probe. Background systematic noise is modeled using cross-correlation analysis between different SEP energy channels and subtracted from the original data. The use of these four methods for deriving the time-series persistence allows us to (i) differentiate between quiet- and active-Sun periods based on the values of the corresponding self-similarity exponents alone; (ii) identify the onset of an ongoing activity well before it reaches its maximum SEP flux; (iii) reveal an interesting fine structure when activity is observed; and (iv) provide, for the first time, an estimate of the maximum SEP flux of a future storm based on the entropy change of natural time under time reversal.

Correlation analysis of InSAR displacement data and inspection data towards multiple bridge monitoring in transportation network

This paper presents a study on multiple bridge monitoring in a wide area of transportation networks using satellite Synthetic Aperture Radar (SAR) technology. SAR has been expected to have potential to analyze displacements of multiple large-scale structures in wide areas and in long-term without installing any sensors on them. This study shows quantitative analysis of InSAR (interferometric SAR) data on multiple bridges regarding to structural condition using their periodic inspection data. The target bridges are seventeen bridges located at a region in Japan, Ibaraki prefecture. They are the bridges that has lengths over 100 m selected from ones operated by the rural-road office of MLIT, the transport ministry of Japan, where periodic inspection data are all opened and available. Their displacements are then obtained using Sentile1 satellite SAR data. The time period for the analysis is seven years from 2016 to 2022, with time interval of 12 days. Using those data, line-of-sight (LoS) displacements of each bridge are derived as a time-series data through interferometric processing. The LoS displacement time-series data are then analyzed for each bridge by principal component analysis (PCA) to extract significant components of time-series deformation behavior. The extracted principle components, air temperature, and inspection records are compared and analyzed. As a result, it is found that a stable correlation between the principle component and air temperature could be one of explanatory factors of bridge conditions. This may indicate the bridges having stable correlation between LoS displacements and air temperature are in a nominal and undamaged condition although further investigation to more bridges is required. Moreover, visualizations of time-series InSAR bridge deflection and extracted feature components and its pattern analysis in multiple bridges are shown to have high potential to contribute on multiple bridge operations in whole transportation network.

Analyzing dynamic patterns of information flow between bitcoin and economic uncertainty in light of public sentiments: A statistical behavior approach

Modeling and analyzing interrelationships within the Bitcoin market, as a prominent cryptocurrency, leads to understanding hidden structures, effective management and informed decision-making. Regarding this matter, numerous studies have analyzed the time-varying spillover patterns in this ecosystem. Although spillover network analysis can elucidate the nature and strength of correlations, it may not be adept at handling the conditional interdependencies within intricate non-linear and dynamic essential behaviors of financial time series. This research tries to address the mentioned challenges by presenting a novel analytical model to investigate the dynamic communication patterns among Bitcoin, United States Economic Policy Uncertainty (US EPU) and public sentiments. Following this objective, rather than directly exploring the effect of original data series on each other, the approach decomposes them into sequences of meaningful statistical behaviors, at different lag-lead horizons. Subsequently, considering the significance of conditional dependencies, we extract and analyze the rules and patterns of information flow among the observed behaviors. The findings not only unveil a distinct flow pattern compared to the spillover network, but also offer valuable insights into dynamic interactions and dominant behaviors under various scenarios. One observation suggests that as the historical range of predictors increases in predicting future changes, their effectiveness or reliability decreases, while their number simultaneously increases. Moreover, the trend slope of Bitcoin functions as a notable behavior in propagating information, directly influencing both economic uncertainty and investor sentiment. The proposed model enhances the understanding of interaction between financial time series and provides useful perspectives for analysis and risk management.

Dynamics and wave analysis in longitudinal motion of elastic bars or fluids

We look at the bifurcation analysis, chaotic behavior, multistability, and sensitivity analysis of the van der Waals equation, which is used in science and engineering to study the dynamics of various structures. The variant van der Waals equation covers one-dimensional longitudinal isothermal motion in elastic bars or fluids, which is the main focus of this research. The Galilean transformation transfers the given model into a planar dynamical system. The power series methodology produces exact wave solutions. In addition, after accounting for the perturbation component, sensitivity analysis for various initial value problems is used to investigate quasi-periodic, chaotic, and time series behavior. Numerical simulation results show that influencing viscosity and the coefficient of interfacial capillarity influence the dynamical factors of the examined model. We are observing what happens with viscosity and the interface coefficients to the wave solution. In some cases, for different parameter values, we present plots of both one-dimensional and two-dimensional graphical representations of individual solutions.

Connectedness with commodities in emerging markets: ESG leaders vs. conventional indexes

This study investigates the dynamic connectedness of ESG Leaders and conventional equity indexes with commodities in emerging markets. Using data for emerging markets stocks from three regions (Asia, Europe, and Latin America), we compare the spillover effects from commodities to ESG and conventional indexes. Most of the transmission of shocks occurs between equity indexes, whereas spillover from commodities is limited. While there are similarities in the behavior of ESG Leaders and conventional index time series, slightly higher transmission is documented from commodities to conventional indexes compared to the ESG Leaders. Regional differences in connectedness are observed. However, from a portfolio management perspective, no notable differences can be seen between investing in either the ESG Leaders or conventional emerging market indexes when included in portfolios with commodities.

Comparing Influences of Solar Wind, ULF Waves, and Substorms on 20 eV–2 MeV Electron Flux (RBSP) Using ARMAX Models

AbstractElectron fluxes (20 eV–2 MeV, RBSP‐A satellite) show reasonable simple correlation with a variety of parameters (solar wind, IMF, substorms, ultralow frequency (ULF) waves, geomagnetic indices) over L‐shells 2–6. Removing correlation‐inflating common cycles and trends (using autoregressive and moving average terms in an ARMAX analysis) results in a 10 times reduction in apparent association between drivers and electron flux, although many are still statistically significant (p &lt; 0.05). Corrected influences are highest in the 20 eV–1 keV and 1–2 MeV electrons, more modest in the midrange (2–40 keV). Solar wind velocity and pressure (but not number density), IMF magnitude (with lower influence of Bz), SME (a substorm measure), a ULF wave index, and geomagnetic indices Kp and SymH all show statistically significant associations with electron flux in the corrected individual ARMAX analyses. We postulate that only pressure, ULF waves, and substorms are direct drivers of electron flux and compare their influences in a combined analysis. SME is the strongest influence of these three, mainly in the eV and MeV electrons. ULF is most influential on the MeV electrons. Pressure shows a smaller positive influence and some indication of either magnetopause shadowing or simply compression on the eV electrons. While strictly predictive models may improve forecasting ability by including indirect driver and proxy parameters, and while these models may be made more parsimonious by choosing not to explicitly model time series behavior, our present analyses include time series variables in order to draw valid conclusions about the physical influences of exogenous parameters.

Methodological proposal to determine explosive tourism growth: a warning for unsustainable development?

ABSTRACT Sustainability is a crucial aspect of tourism growth and development. This paper suggests the adaptation of a methodology originally designed for analysing explosive behaviour in financial market time series to assess the explosive behaviour of demand pressure in tourist destinations. The proposed methodology could function as an early warning system for potential issues such as over-tourism by identifying further unsustainable growth in destinations. The methodology is exemplified by examining the long-term evolution of tourism pressure in both Spain as a whole, and in Barcelona, which is a destination frequently scrutinised in the literature and media due to perceived over-tourism. The findings suggest explosive behaviour in tourism pressure, especially in the case of Barcelona, from which valuable lessons can be learned and meaningful conclusions drawn.

On the impact of institutional change: Rights reassignment and career length

AbstractRottenberg argued that the reassignment of negotiating rights from owners to players, that would accompany free agency in professional sports, would have little impact on locational or market outcomes. Empirical investigations into such reassignment have produced mixed results. The present paper examines the impact such reassignment had on professional sports athletes' career length. By examining the univariate time series and panel data behavior of Major League Baseball players' average tenure and retention rates, we find that the increased negotiating power associated with the advent of free agency had the impact of shortening average player career length.