Time Series Models Research Articles

Online learning of order flow and market impact with Bayesian change-point detection methods

Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.

Evaluating the Impact of Inflation and Exchange Rates on the Net Active Value (NAV) of Conventional Mutual Funds in Indonesia

The growing development in the financial industry and the current high inflation rate are a great worry for investment prospects, especially mutual funds. Conventional mutual funds in Indonesia have experienced significant growth in the capital market. In the last 10 years, the number of domestic mutual funds has continued to increase. One measure of investment performance in mutual funds is proxied by the Net Asset Value (NAV) of mutual funds. Therefore, the objective of this study is to evaluate the influence of macroeconomics variables on the NAV of conventional mutual funds in Indonesia. In this study, using commercial bank data in Indonesia, we estimate an empirical model of the effect of inflation and exchange rates on the NAV of conventional mutual funds in Indonesia during the period 2012–2022. We employ a time series model using the Ordinary Least Square (OLS). The main findings show that while inflation has no effect on the NAV of conventional mutual funds, the exchange rate has a significant effect on it because there are many instruments in conventional mutual funds that have invested abroad due to changes in exchange rates that appear with dominant results that do not affect the growth of the NAV of conventional mutual funds. Keywords: macroeconomics, net active value (NAV), mutual funds

BITCOIN PRICE PREDICTION BY USING ARIMA

Cryptocurrency markets have emerged as a dynamic and intriguing domain, with Bitcoin at the forefront, captivating the attention of investors, researchers, and enthusiasts alike. The volatile nature of Bitcoin prices presents both opportunities and challenges for market participants seeking to understand and anticipate its movements. In this study, we delve into the realm of time series analysis to explore the feasibility of predicting The research journey begins with meticulous data preprocessing steps to ensure the quality and integrity of the input data. Leveraging Python libraries such as pandas and NumPy, we cleanse and format the historical Bitcoin price data, laying the foundation for subsequent analysis. Key preprocessing tasks include handling missing values, normalization, and addressing any anomalies or outliers that may distort the underlying patterns. With the data prepared, our attention turns to assessing the stationarity of the Bitcoin price time series—a fundamental prerequisite for applying classical time series models. Through visual inspection and statistical tests such as the Augmented Dickey-Fuller (ADF) test, we ascertain the presence of trends or seasonality that could influence the modelling process. To mitigate such effects, we employ techniques such as differencing and transformations, including the Box-Cox transformation, to stabilize the variance of the data. Armed with a stationary time series, we embark on the core of our analysis: modelling Bitcoin prices using Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. These models, renowned for their versatility and effectiveness in capturing temporal dependencies, offer a sophisticated framework for forecasting time series data. Guided by the principles of parsimony and model selection criteria such as the Akaike Information Criterion (AIC), we systematically explore the parameter space to identify the optimal specifications for our models. The efficacy of the chosen models is rigorously evaluated through diagnostic checks, encompassing residual analysis, model fit statistics, and out-of-sample validation. Insights gleaned from these assessments inform our confidence in the models' predictive capabilities and guide our interpretation of the forecasted outcomes. Finally, armed with a validated model, we turn our gaze to the future, employing it to generate forecasts of Bitcoin prices for forthcoming periods. Visualizations juxtaposing predicted prices against observed values provide a compelling narrative of the model's performance and offer stakeholders valuable insights into potential market trends and dynamics. In summary, this research contributes to the burgeoning field of cryptocurrency analytics by showcasing the application of time-tested statistical methodologies to forecast Bitcoin prices. By leveraging the power of ARIMA and SARIMAX models, we illuminate the intricate patterns underlying Bitcoin's price dynamics, empowering market participants with actionable intelligence for informed decision-making in an ever-evolving landscape. Keywords Cryptocurrency Markets, Bitcoin Price Prediction, Time Series Analysis, Python Programming, Data Preprocessing, Pandas, NumPy, Stationarity Testing, Augmented Dickey-Fuller (ADF) Test, Box-Cox Transformation, ARIMA Model, SARIMAX Model, Model Selection, Akaike Information Criterion (AIC), Diagnostic Checks, Residual Analysis, Out-of-Sample Validation, Forecasting, Visualization, Market Trends, Decision-Making, Cryptocurrency Analytics

Precision in Insurance Forecasting: Enhancing Potential with Ensemble and Combination Models based on the Adaptive Neuro-Fuzzy Inference System in the Egyptian Insurance Industry

ABSTRACT Enhancing the precision of retention ratio predictions holds profound significance for insurance industry decision-makers and those vested in advancing insurance services. Precision helps insurance companies navigate inflationary pressures and evaluate underwriting profitability, enabling reliable prognoses of future underwriting gains. As far as we know, although there have been multiple attempts to construct a predictive model for retention ratio, none of these attempts have used combining models or studied the Egyptian market. Therefore, this study contributes significantly to this developing field by providing combining models, which combined statistical time series models such as Exponential Smoothing (ES), and Autoregressive Integrated Moving Average (ARIMA), with Adaptive Neuro-Fuzzy Inference System (ANFIS). Two different types of combinations are employed with these models. Furthermore, the study introduces three ensemble models designed for the purpose of predicting the retention ratio within the Egyptian insurance market. Dataset was carefully gathered from the EFSA’s annual reports, focused on the property-liability insurance sector within the Egyptian insurance market and covers the time period from 1989 to 2021. Next, the proposed models are assessed employing well-established statistical assessment metrics, namely, Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE), R Square (R2), and Root Mean Square Error (RMSE). The results show that combining and ensemble methods improve predicted accuracy. A multi-linear regression-based ensemble model that combines ARIMA, ES, and ANFIS models outperforms both single and combined models in robustness. The article concludes that the insurance industry can greatly benefit from modern predictive methods to make sound decisions.

Estimation of Rice Plant Coverage Using Sentinel-2 Based on UAV-Observed Data

Vegetation coverage is a crucial parameter in agriculture, as it offers essential insight into crop growth and health conditions. The spatial resolution of spaceborne sensors is limited, hindering the precise measurement of vegetation coverage. Consequently, fine-resolution ground observation data are indispensable for establishing correlations between remotely sensed reflectance and plant coverage. We estimated rice plant coverage per pixel using time-series Sentinel-2 Multispectral Instrument (MSI) data, enabling the monitoring of rice growth conditions over a wide area. Coverage was calculated using unmanned aerial vehicle (UAV) data with a spatial resolution of 3 cm with the spectral unmixing method. Coverage maps were generated every 2–3 weeks throughout the rice-growing season. Subsequently, crop growth was estimated at 10 m resolution through multiple linear regression utilizing Sentinel-2 MSI reflectance data and coverage maps. In this process, a geometric registration of MSI and UAV data was conducted to improve their spatial agreement. The coefficients of determination (R2) of the multiple linear regression models were 0.92 and 0.94 for the Level-1C and Level-2A products of Sentinel-2 MSI, respectively. The root mean square errors of estimated rice plant coverage were 10.77% and 9.34%, respectively. This study highlights the promise of satellite time-series models for accurate estimation of rice plant coverage.

Automatic early warning of rockbursts from microseismic events by learning the feature-augmented point cloud representation

This study proposes an automatic early warning system for rockbursts in deeply buried tunnels using microseismic data. A novel machine learning model is introduced, which is the first to treats the microseismic event data as a high-dimensional point cloud. The model utilizes a fast approximated convolution (FAC) method to effectively and efficiently learn features from the raw event data with small model sizes. The model is trained and tested using microseismic data from the Hanjiang-To-Weihe water diversion project. Two competing models, a time series model and a traditional threshold model, are also constructed for comparison. Results show that the proposed model achieves reliable early warnings for 90.1% of rockbursts in the tunnel. This study presents a new and feasible rockburst prediction method that can be used independently or as a supplement to current assessment and management approaches for both tunnel boring machines (TBMs) and drilling- and blasting-excavated tunnels.

Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression from Age-Related Changes in OCT scans

To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard. Retrospective cohort study. Glaucomatous and healthy eyes with ≥5 reliable peripapillary OCT (Spectralis, Heidelberg Engineering) circle scans were included. A weakly supervised time-series learning model, called Noise Positive-Unlabeled (Noise-PU) DL was developed to classify whether sequences of OCT B-scans showed glaucoma progression. The model used two learning schemes, one to identify age-related changes by differentiating test sequences from glaucoma vs. healthy eyes, and the other to identify test-retest variability based on scrambled OCTs of glaucoma eyes. Both models' bases were convolutional neural networks (CNN) and long short-term memory (LSTM) networks which were combined to form a CNN-LSTM model. Model features were combined and jointly trained to identify glaucoma progression, accounting for age-related loss. The DL model's outcomes were compared with ordinary least squares (OLS) regression of retinal nerve fiber layer (RNFL) thickness over time, matched for specificity. The hit ratio was used as a proxy for sensitivity. 8,785 follow-up sequences of 5 consecutive OCT tests from 3253 eyes (1859 subjects) were included in the study. Mean follow-up time was 3.5±1.6 years. In the test sample, the hit ratios of the DL and OLS methods were 0.498 (95%CI:0.470-0.526) and 0.284 (95%CI:0.258-0.309) respectively (P<0.001) when the specificities were equalized to 95%. A DL model was able to identify longitudinal glaucomatous structural changes in OCT B-scans using a surrogate reference standard for progression.

Non-fatal suicide behaviours across phases in the COVID-19 pandemic: a population-based study in a Catalan cohort

The COVID-19 pandemic has been extensively discussed in the context of its effect on mental health. Although global suicide rates have remained stable during the pandemic, the specific effect on non-fatal suicide behaviours during and after the pandemic remains underexplored. This study aims to investigate patterns of non-fatal suicide behaviours before, during, and after the pandemic. In this cohort study, we used data from all hospitals in Catalonia, Spain, collected through the Catalan Suicide Risk Code, which is a specifically designed suicide attempt surveillance protocol, involving a face-to-face, in-depth psychiatric evaluation, after a Catalan resident presents any suicide risk behaviour in any public health-care setting. This evaluation centralises data from suicide registries across the territory. We included non-fatal suicide behaviours, meaning suicidal ideation or attempts that did not result in death, and excluded self-harm behaviours not judged to be linked with suicidal ideation. We considered three periods: the pre-confinement period (Jan 1, 2018, to the enforcement of the lockdown in Spain on March 14, 2020); the confinement period (March 14, 2020, to the end of lockdown on June 21, 2020); and the post-confinement period (June 21, 2020, to Dec 31, 2022). We used Bayesian structural time series models to assess the effect of pandemic phases on non-fatal suicide behaviours, and we ran stratified analyses by sex and age to identify distinct patterns among demographic cohorts. We obtained 26 482 records from Jan 1, 2018, to Dec 31, 2022. The mean age was 37·94 years (SD 18·07), and the sample included 17 584 (66·4%) women and 8898 (33·6%) men. Data on ethnicity were not collected. Temporal trends showed a mild increase in non-fatal suicide behaviours from Jan 1, 2018, to March 13, 2020; a reduction during the confinement period; and a subsequent rise after confinement. Bayesian models suggested a significant causal effect of lockdown easing, resulting in a 50·77% increase in non-fatal suicide behaviours (95% credible interval [CrI] 26·62-76·58; p<0·0001). Stratified analyses indicated that the easing of lockdown resulted in a significant increase in non-fatal suicide behaviours among women (25·92%; 6·71-44·72; p=0·011) and among individuals aged 18 years and younger (72·75%; 38·81-108·11; p<0·0001). This study provides a comprehensive examination of non-fatal suicide behaviours in Catalonia, Spain, emphasising the dynamics of different COVID-19 pandemic phases. The initial reduction during strict lockdown aligns with Joiner's Interpersonal Theory of Suicide, whereas the post-confinement rise reflects complex factors, including social isolation and economic challenges. Sex-specific and age-specific analyses underscore distinct vulnerabilities, emphasising the need for targeted preventive strategies. Centro de Investigación Biomédica en Red de Salud Mental annual budget of G21, Agència de Gestió d'Ajuts Universitaris i de Recerca of the Generalitat de Catalunya. For the Catalan and Spanish translations of the abstract see Supplementary Materials section.

Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China.

Due to rapid expansion in the global economy and industrialization, PM2.5 (particles smaller than 2.5µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM2.5 levels. In this paper, ambient PM2.5 concentrations along with meteorological data are forecasted using time series models, including random forest (RF), prophet forecasting model (PFM), and autoregressive integrated moving average (ARIMA) in Anhui province, China. The results indicate that the RF model outperformed the PFM and ARIMA in the prediction of PM2.5 concentrations, with cross-validation coefficients of determination R2, RMSE, and MAE values of 0.83, 10.39µg/m3, and 6.83µg/m3, respectively. PFM achieved the average results (R2 = 0.71, RMSE = 13.90µg/m3, and MAE = 9.05µg/m3), while the predicted results by ARIMA are comparatively poorer (R2 = 0.64, RMSE = 15.85µg/m3, and MAE = 10.59µg/m3) than RF and PFM. These findings reveal that the RF model is the most effective method for predicting PM2.5 and can be applied to other regions for new findings.

Achieving optimal trade-off for student dropout prediction with multi-objective reinforcement learning

Student dropout prediction (SDP) in educational research has gained prominence for its role in analyzing student learning behaviors through time series models. Traditional methods often focus singularly on either prediction accuracy or earliness, leading to sub-optimal interventions for at-risk students. This issue underlines the necessity for methods that effectively manage the trade-off between accuracy and earliness. Recognizing the limitations of existing methods, this study introduces a novel approach leveraging multi-objective reinforcement learning (MORL) to optimize the trade-off between prediction accuracy and earliness in SDP tasks. By framing SDP as a partial sequence classification problem, we model it through a multiple-objective Markov decision process (MOMDP), incorporating a vectorized reward function that maintains the distinctiveness of each objective, thereby preventing information loss and enabling more nuanced optimization strategies. Furthermore, we introduce an advanced envelope Q-learning technique to foster a comprehensive exploration of the solution space, aiming to identify Pareto-optimal strategies that accommodate a broader spectrum of preferences. The efficacy of our model has been rigorously validated through comprehensive evaluations on real-world MOOC datasets. These evaluations have demonstrated our model’s superiority, outperforming existing methods in achieving optimal trade-off between accuracy and earliness, thus marking a significant advancement in the field of SDP.

Assessing the Differentiated Impacts of COVID-19 on the Immigration Flows to Europe

The immediate effects of COVID-19 on mortality, fertility, and internal and international migration have been widely studied. Particularly, immigration to high-income countries declined in 2020. However, the persistence of these declines and the extent to which they have impacted different migration flows are yet to be established. Drawing on immigration flows from Eurostat and Autoregressive Integrated Moving Average (ARIMA) time-series models, we assess the impact of COVID-19 on different immigration streams to seven European countries. We forecast counterfactual levels of immigration in 2020 and 2021 assuming no pandemic, and compare these estimates with actual immigration counts. We use regression modeling to explore the role of immigrants’ origin, distance, stringency measures, and gross domestic product (GDP) trends at origins and destinations as potential driving forces of changes in immigration during COVID-19. Our results show that, while there was a general decline in immigration during 2020, inflows returned to expected levels in 2021, except for Spain. However, drops in immigration flows from countries outside the Schengen Area to Europe persisted in 2021. Immigrants’ origin emerged as the main factor modulating immigration changes during the pandemic, and to a lesser extent stringency measures and GDP trends in destination countries. Contextual factors at origin seem to have been less important.

날씨 변수를 고려한 전력 수요량 예측구간 시계열 분석 연구

Annual electricity demand is steadily increasing and the importance of forecasting the electricity demand is emerging since the dependence on domestic electricity is also increasing. The purpose of this study is analysis of the electricity demand considering seasonality and weather variables, for which we study time series modelling and forecasting analysis. ARIMA, SARIMA, ARIMAX, SARIMAX models are handled with optimally selected orders. Out-of-sample forecasting analysis is carried out to evaluate predicted values and their errors such as RMSE, MAE, MAPE and MSLE. The errors by four models are compared. As the optimal model, SARIMAX(2,1,1) (2,1,2,12) model with CDD, HDD and insolation has been chosen. For strict statistical analysis of prediction intervals, normal distribution, laplace distribution and bootstrap method are adopted to construct 80%, 95% prediction intervals. Coverage probability (CP), average length (AL) and mean interval score (MIS) of the prediction intervals by three approximations are evaluated. This study contributes with novelty in that it provides three approaches of prediction intervals along with their comparison. The results of this study are expected to be of practical help in the efficient operation of electricity supply system.

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

Optimizing Air Handling Unit Induction Motor Frequency Predictions: Evaluating and Advancing Forecasting Techniques with a Modified Chen's Fuzzy Time Series Model

Long Short-Term Memory Parameter Optimization Based on Improved Sparrow Search Algorithm for Molten Iron Quality Prediction

Blast furnace (BF) ironmaking is a key process in iron and steel production. Because BF ironmaking is a dynamic time series process, it is more appropriate to use a recurrent neural network for modeling. The long short-term memory (LSTM) network is commonly used to model time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necessary to study parameter optimization for the LSTM model. The sparrow search algorithm (SSA) holds advantages over traditional optimization algorithms in several aspects, such as no need for prior knowledge, fewer parameters, fast convergence, and high scalability. However, the algorithm still faces some challenges, such as the tendency to become trapped in the local optimum and the imbalance between global search ability and local search ability. Therefore, on the basis of SSA, this study examined the Levy flight strategy, sine search strategy, and step size factor adjustment strategy to improve it. This algorithm, improved by three strategies, is called the improved sparrow search algorithm (ISSA). Then, the ISSA-LSTM model was established. Furthermore, considering the limitations of SSA in dealing with multi-objective problems, the fast non-dominated sorting genetic algorithm (NSGAII) was introduced, and the ISSA-NSGAII model was established. Finally, experimental validation was performed using real blast furnace operation data, which demonstrated the proposed algorithm’s superiority in parameter optimization for the LSTM model and prediction for real industrial data.

Assessment and Forecasting of Energy Efficiency in Economic Sectors in Poland

The material developed focuses on the analysis of energy efficiency trends in Poland, utilising ODEX indicators for the sectors of industry, transport, households and in general, from 2011 to 2021. The objective of the study is to assess the progress made in energy efficiency and to forecast future trends in these sectors. The methods employed are based on statistical modelling of time series, taking into account sector-specific energy consumption dynamics. The following techniques were employed: linear regression, cluster analysis to identify patterns of change, statistical hypothesis testing for energy efficiency and simplified autoregressive models. The results demonstrated significant improvements in energy efficiency in the industrial sector, stability of the ODEX indicator in the transport sector and gradual improvements in households and overall. The prediction indicates an upward trend in the ODEX indicator in the short term, suggesting an increase in energy demand. However, it also predicts a decline in the long term, which may indicate the effectiveness of future energy efficiency strategies and investments. Consequently, the necessity for continued efforts to increase energy efficiency and further research into the factors influencing energy efficiency in different economic sectors is emphasised.

Interpretable Recurrent Variational State-Space Model for Fault Detection of Complex Systems Based on Multisensory Signals

It is necessary to develop a health monitoring system (HMS) for complex systems to improve safety and reliability and prevent potential failures. Time-series signals are collected from multiple sensors installed on the equipment that can reflect the health condition of them. In this study, a novel interpretable recurrent variational state-space model (IRVSSM) is proposed for time-series modeling and anomaly detection. To be specific, the deterministic hidden state of a recursive neural network is used to capture the latent structure of sensor data, while the stochastic latent variables of a nonlinear deep state-space model capture the diversity of sensor data. Temporal dependencies are modeled through a nonlinear transition matrix; an automatic relevance determination network is introduced to selectively emphasize important sensor data. Experimental results demonstrate that the proposed algorithm effectively captures vital information within the sensor data and provides accurate and reliable fault diagnosis during the steady-state phase of liquid rocket engine operation.

Causal structure learning for high-dimensional non-stationary time series

Learning the causal structure of high-dimensional non-stationary time series can help in understanding the data generation mechanism, which is a crucial task in machine learning. However, current causal discovery methods for high-dimensional non-stationary time series face several challenges, including the inability to effectively capture non-stationarity, failure to ensure acyclicity of causal graphs, and reliance on subjective threshold definitions, leading to suboptimal algorithm performance. To address these challenges, we introduce a novel Causal Structure Learning model for High-dimensional Non-stationary Time Series (CSL-HNTS). Firstly, this model presents a graph neural network to model the non-stationarity of time series. Secondly, it introduces a novel Directed Acyclic Graph (DAG) sampling method that transforms the space of DAGs into a continuous space, enabling the search for causal graphs within this continuous space to ensure acyclicity. Finally, the model proposes an automatic threshold definition method, without prior knowledge, to convert the weighted adjacency matrix into the Boolean adjacency matrix of the causal graph, thereby avoiding time-consuming postprocessing steps. The proposed approach is validated using simulation datasets and two real datasets, and is benchmarked against current state-of-the-art methods and ablation experiments. The results demonstrate a significant improvement over existing methods, highlighting the efficacy of the proposed model.

Time-varying double-peaked emission lines following the sudden ignition of the dormant galactic nucleus AT2017bcc

Abstract We present a pan-chromatic study of AT2017bcc, a nuclear transient that was discovered in 2017 within the skymap of a reported burst-like gravitational wave candidate, G274296. It was initially classified as a superluminous supernova, and then reclassified as a candidate tidal disruption event. Its optical light curve has since shown ongoing variability with a structure function consistent with that of an active galactic nucleus, however earlier data shows no variability for at least 10 years prior to the outburst in 2017. The spectrum shows complex profiles in the broad Balmer lines: a central component with a broad blue wing, and a boxy component with time-variable blue and red shoulders. The Hα emission profile is well modelled using a circular accretion disc component, and a blue-shifted double Gaussian which may indicate a partially obscured outflow. Weak narrow lines, together with the previously flat light curve, suggest that this object represents a dormant galactic nucleus which has recently been re-activated. Our time-series modelling of the Balmer lines suggests that this is connected to a disturbance in the disc morphology, and we speculate this could involve a sudden violent event such as a tidal disruption event involving the central supermassive black hole, though this cannot be confirmed, and given an estimated black hole mass of ≳ 107–108 M⊙ instabilities in an existing disc may be more likely. Although we find that the redshifts of AT2017bcc (z = 0.13) and G274296 (z &amp;gt; 0.42) are inconsistent, this event adds to the growing diversity of both nuclear transients and multi-messenger contaminants.

Assessing and forecasting collective urban heat exposure with smart city digital twins

Due to population growth, climate change, and the urban heat island effect, heat exposure is becoming an important issue faced by urban built environments. Heat exposure assessment is a prerequisite for mitigation measures to reduce the impact of heat exposure. However, there is limited research on urban heat exposure assessment approaches that provides fine-scale spatiotemporal heat exposure information, integrated with meteorological status and human collective exposure as they move about in cities, to enable proactive heat exposure mitigation measures. Smart city digital twins (SCDTs) provide a new potential avenue for addressing this gap, enabling fine spatiotemporal scales, human-infrastructure interaction modeling, and predictive and decision support capabilities. This study aims to develop and test an SCDT for collective urban heat exposure assessment and forecasting. Meteorological sensors and computer vision techniques were implemented in Columbus, Georgia, to acquire temperature, humidity, and passersby count data. These data were then integrated into a collective temperature humidity index. A time-series prediction model and a crowd simulation were employed to predict future short-term heat exposures based on the data accumulated by this SCDT and to support heat exposure mitigation efforts. The results demonstrate the potential of SCDT to enhance public safety by providing city officials with a tool for discovering, predicting, and, ultimately, mitigating community exposure to extreme heat.

Time series analysis and applications in data analysis, forecasting and prediction

Time series analysis is an essential field in data analysis, particularly within forecasting and prediction domains. Researching and building time series models play a crucial role in understanding and predicting the temporal dynamics of various phenomena. In mathematics, time series data is defined as data points indexed in chronological order and have a consistent time interval between consecutive observations. This can include data such as daily stock prices, annual national income, quarterly company revenue, and more. The advantage of time series data is that it can capture the state of a variable over time. In contrast, the world is constantly changing, and phenomena rarely remain static they typically exhibit variations over time. Therefore, time series data has highly practical applications and is used in various fields, including statistics, econometrics, financial mathematics, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, telecommunications, and signal processing. ARIMA, which stands for Auto Regressive Integrated Moving Average, is a widely used time series forecasting method in data science. It is a popular model for analyzing and predicting time-dependent data points. ARIMA combines autoregression, differencing, and moving averages to capture different aspects of time series data. In this paper, we study ARIMA, which is a significant model for analyzing and predicting time series data.