Series Approach Research Articles

Trend Analysis on Prices of Sunflower in Andhra Pradesh- A Time Series Approach

The present study made an attempt to forecast the sunflower prices in Adoni, Kurnool and Yemmiganur markets of Andhra Pradesh by using the secondary time series data for a period of 147 months with an interval ranging from Jan 2011 to Apr 2023. For this study various Growth models (linear, Cubic, Quadratic, Exponential and power models) and time series models (ARIMA and GARCH) and Hybrid Times series models were applied. On consideration of the selected diagnostic criterion like R2, RMSE and MAPE, it resulted that Hybrid time series models outperformed among the selected models for all the three selected markets. Finally, the prices of sunflower in Adoni, Kurnool and Yemmiganur market were forecasted by ARIMA(1,1,1)-GARCH(1,1), ARIMA(2,1,1)-GARCH(1,1) and ARIMA(2,1,2)-GARCH(1,1) models respectively and prices of for the month of April 2024 were estimated as 5527.7 (Rs. /Q.), 5819.70(Rs. /Q.) and 5395.04 (Rs. /Q.).. KEYWORDS :Price, Model, Sunflower, Forecast.

The Influence of Loan to Deposit Ratio (LDR) and Operating Costs on Operating Income (BOPO) on Return on Assets (ROA)

This study examines the influence of LDR and BOPO on ROA at a bank in Bandung. Return on Assets (ROA) highlights the Company's ability to produce revenue from its operations by efficiently using its assets. Factors affecting Return on Assets (ROA) encompass the Loan to Deposit Ratio (LDR) and the Operating Expenses to Operating Income Ratio (BOPO), all of which impact ROA. The study's population consisted of four years of financial records from a bank in Bandung, encompassing all four quarters. The sample was selected using the time series approach, consisting of 16 financial reports from 2018 to 2021. The applied data testing method was multiple linear regression, utilizing a significance level of 5% alpha. The findings indicated that LDR did not exert a significant influence on ROA. Despite LDR exhibiting a positive coefficient, the significance value is above the conventional threshold, suggesting that the impact of LDR on ROA is minimal and statistically insignificant. Conversely, BOPO substantially impacts ROA, with a negative coefficient and a minimal significance value. The rise in BOPO, signifying diminished operational efficiency, adversely affects ROA, suggesting that elevated operating costs diminish the bank's profitability. Subsequent research indicated that the regression model accounts for most of the variation in ROA, demonstrating a robust association between LDR and BOPO with ROA. It is advised that the Company's management concentrate on optimizing operating cost management to enhance ROA. Implementable strategies encompass conducting frequent audits to identify and mitigate waste and enhancing the efficiency of operational procedures. Despite LDR not demonstrating a significant impact, it is crucial to monitor this ratio to ensure the bank's liquidity and financial stability. This study also advocates for additional research to investigate other variables that may influence ROA, along with a comprehensive examination of operational elements to enhance profitability.

Venturing the Golden Indonesia 2045: Are Electricity and Broadband Development for Five Indonesian Priority Industries Impactful?

This study aims to examine the impact of electricity and broadband development on the progress of five priority industries in Indonesia. Initial findings indicate a mismatch between policy expectations and their impacts. This research collected secondary data from 2010 to 2019, using a time series approach and bootstrapping 1000 times to strengthen and cover the weaknesses of the method and the short time span. The results reveal that the electronics sector has the greatest beneficial impact both partially and simultaneously from all external variables. Additionally, four industries, except the automotive sector, show the same impact. This report emphasizes the necessity of synergy between digitalization and electrification, asserting the inseparable integration of policies. The automotive sector has experienced significant growth over the past decade, but it has not been directly impacted by these policies. Conversely, the food industry is affected and has emerged as the largest output producer, while other sectors are considered to have growth potential, indicating the need for alternative infrastructure initiatives. This study is expected to provide insights to all stakeholders regarding the impact of government policies and how to maximize business benefits amid global challenges.

Random cohort effects and smooth structures for mortality modelling and forecasting: A mixed-effects Gaussian process time series approach

The issue of population ageing has become a critical challenge for many developed nations in recent decades, and urgent action is needed to address this concern. Despite numerous efforts have been made to tackle the risks associated with increased longevity, the problem remains unresolved. The Cairns-Blake-Dowd (CBD) model, widely recognised as a leading approach for mortality modelling at older ages, incorporates cohort effects parameters into its parsimonious design. This article proposes a new mixed-effects Gaussian process time series approach that not only considers random cohort effects but also ensures smoothness across age ranges. By adopting Gaussian process, this novelty allows the proposed method naturally accounts for all uncertainties of the estimated parameters without requiring any pre-specified constraints and provides more accurate results. Through two empirical applications using male and female mortality data, we demonstrate the exceptional capabilities of our approach, which outperforms the CBD models with cohorts in short-, mid-, and long-term forecasting of mortality rates from various developed countries. Our proposed approach offers a significant improvement in forecast accuracy and shows a valuable contribution to the field of cohort mortality modelling.

Integration of Landsat-8 OLI/TIRS and Sentinel-1A PolSAR for analyzing land surface temperature and its anomalies linked to ENSO in Surakarta, Indonesia

The increase in urban buildings leads to degraded vegetated areas, resulting in higher surface radiation and air temperatures. The rise of land surface temperature (LST) is also influenced by land cover changes and global climate related to the ENSO (El Nino-Southern Oscillation) phenomena. This study is the first to combine active and passive sensors to analyze LST anomalies linked to ENSO-related land cover change on a time series approach. Conducted from August 2018 to 2023 in an urban area dominated by buildings, we used Python programming to extract LST from the Landsat-8 OLI/TIRS passive sensor with a Mono-window algorithm. Meanwhile, the land cover classification was performed by Sentinel-1A active sensor imagery using polarimetric decomposition with unsupervised Wishart. The LST and land cover results were equalized to 30 m spatial resolution for regression and anomaly analysis based on reported ENSO phenomena. The results revealed that land cover type significantly affected LST variation during the study period, proven by the significance value of each land cover type being less than 0.05 and showing a positive correlation. However, the correlation is low, meaning that land cover change is not the dominant factor causing LST change. The low correlation caused by El Nino and La Nina, contributed more to the change in LST during the study period. The integrated method can overcome the weakness of passive sensors in penetrating clouds, contribute to a broader knowledge of the factors causing LST changes, and provide effective early mitigation strategies against the threat of future climate change crises.

Make Some ‘Noise’ Tonight—Overnight Return as Investor Sentiment Index: Indian Evidence

This research, following Aboody et al., 2018, Journal of Financial and Quantitative Analysis, 53(2), 485–505, and using 612,066 firm-day data daily stock returns, examines whether overnight return is a reliable proxy for firm-specific investor sentiment in India. The study also probes into the impact of the COVID-19 crisis on the relevance of overnight returns for Indian stock investors. The time series approach pursued in the study provides evidence of short-term overnight-return persistence, where the impact is more on the firms with complex valuation and arbitrage structures. Moreover, short-term overnight-return persistence is higher during the COVID-19 pandemic period, highlighting its significance as an indicator of firm-specific investor sentiment in the face of market volatility. In contrast to earlier results of transient sentiment-driven mispricing, we demonstrate that overnight returns in India do not reverse over time. We found that even during the height of the epidemic, overnight returns were only partially indicative of firm-specific investor sentiment in emerging markets such as India.

A Gaussian Process Regression and Wavelet Transform Time Series approaches to modeling Influenza A

The global spread of Influenza A viruses is worsening economic and social challenges. Various mechanistic models have been developed to understand the virus’s spread and evaluate intervention effectiveness. This study aimed to model the temporal dynamics of Influenza A using Gaussian Process Regression (GPR) and wavelet transform approaches. The study employed Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and Wavelet Power Spectrum to analyze time-series data from 2009 to 2023. The GPR model, known for its non-parametric Bayesian nature, effectively captured non-linear trends in the Influenza A data, while wavelet transforms provided insights into frequency and time-localized characteristics. The integration of GPR with DWT denoising techniques demonstrated superior performance in forecasting Influenza A cases compared to traditional models like Auto Regressive Integrated Moving Averages (ARIMA) and Exponential Smoothing (ETS) using Holt–Winter method. The study identified significant anomalies in Influenza A cases, corresponding to known pandemic events and seasonal variations. These findings highlight the effectiveness of combining wavelet transform analysis with GPR in understanding and predicting infectious disease patterns, offering valuable insights for public health planning and intervention strategies. The research recommends extending this approach to other respiratory viruses to assess its broader applicability.

Optimization of MR-ARFI for Human Transcranial Focused Ultrasound.

Magnetic resonance acoustic radiation force imaging (MR-ARFI) is an exceptionally promising technique to non-invasively confirm targeting accuracy and estimate exposure of low-intensity transcranial focused ultrasound stimulation. MR-ARFI uses magnetic field motion encoding gradients to visualize the MR phase changes generated by microscopic displacements at the ultrasound focus. Implementing MR-ARFI in the human central nervous system has been hindered by 1) phase distortion caused by subject motion, and 2) insufficient signal-to-noise ratio at low (<1.0 MPa) ultrasound pressures. The purpose of this study was to optimize human MR-ARFI to allow reduced ultrasound exposure while at the same time being robust to bulk and physiological motion. We demonstrate that a time series of single-shot spiral acquisitions, while triggering ultrasound on and off in blocks, provides ARFI maps that with correction are largely immune to bulk and pulsatile brain motion. Furthermore, the time series approach allows for a reduction in ultrasound exposure per slice while improving motion robustness with reduced scan time. The focused ultrasound beam can be visualized in an 80 second scan with our protocol, enabling iteration for image-guided targeting. We demonstrated robust ARFI signals at the expected target in 4 participants. Our results provide persuasive proof-of-principle that MR-ARFI can be used as a tool to guide ultrasound-based precision neural circuit therapeutics.

Urban transformation of lakes and its impacts on a lake series: A case study of the yele mallappa shetty lake series Bengaluru, India.

The interconnected systems of lakes of Bengaluru, India, experience tremendous stress due to unsustainable urbanisation. Traditional lake-centric research approach struggles to capture and address this issue due to the cascaded nature of impacts in interconnected lake systems. Hence, this study assesses the impact of urbanisation on a lake series scale, focusing on the Yele Mallappa Shetty Lake series (YMSLS). Land use land cover change analysis (1993-2023) showed that in 1993, the YMSLS catchment area was dominated by open spaces (53.4%) and agriculture land and vegetation (35%), with built-up areas covering only 7.2%. By 2023, the built-up area has expanded to 34.6% and became the dominant land use. This rapid urbanisation has led to increased water surface area from wastewater influx, causing significant weed growth and water pollution in the lakes. Lake water quality assessment showed that faecal coliform, total coliform, dissolved oxygen, and biochemical oxygen demand levels exceeded standard limits with spatio-temporal variations driven by interconnectivity. Pollutant flushing during monsoon reduced pollution load in upstream lakes, but high wastewater inflow made downstream lakes, particularly the end lake, highly polluted but perennial. Increased water availability expanded the user base of the end lake, but traditional uses have declined and coping strategies of users, such as wastewater lift irrigation from the lake, became unsustainable. The study indicates that spatial interdependence and cascading effects necessitate a lake series approach for assessing the impacts of urbanisation on interconnected lake system, which is crucial for enhancing water security and preparedness for future extreme events.

Abstract 4136550: In-hospital cardiac arrest can be detected by Innovative smart healthcare syste

Background: Predicting in-hospital cardiac arrest (IHCA) is crucial for preventing patient fatalities. However, it remains challenging due to the irregularities in time series data across various features. Previous studies relied on traditional medical scoring systems, but their accuracy was limited as the models were overly simplistic. Recent research using machine learning methods has shown improved results. In our work, we employ deep learning and time series approaches, which are considered the most powerful models for addressing complex problems in recent years. Method/Research Design: The cohort study took place in a hospital in Taiwan from January 2016 to August 2022, involving 252,000 patients in general wards. Input data encompassed basic information, vital signs, Glasgow Coma Scale scores, medication, etc., to predict IHCA occurrences within 24 hours. Given the immediacy of IHCA events, we utilized left alignment to handle time series. Evaluation metrics included the area under the receiver operating characteristic curve (AUROC). Results: We compared three types of settings. The first was based on scoring systems, where the national early warning system (NEWS) achieved an AUROC of 0.756, and the modified early warning score (MEWS) reached 0.653. In the next setting, based on the data summary within a given time interval, the machine learning models XGBoost and deep learning model TabNet achieved AUROCs of 0.863 and 0.839, respectively. This indicates that machine learning outperforms traditional medical methods. The final setting involved time series analysis, where XGBoost and the Gate Recurrent Unit (GRU) network achieved 0.848 and 0.904, respectively, surpassing all previous settings. Conclusion: Our study demonstrates that learning-based methods outperform rule-based methods in predicting IHCA events. Machine learning algorithms offer more possibilities and have the potential to address complex problems effectively. Moreover, considering time series data rather than summarizing within fixed intervals preserves information and leads to better scores. Leveraging the time series deep learning method, GRU shows promising potential in significantly reducing the risk of IHCA events.

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

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

An efficient Volterra series approach for identifying nonlinear system vibration responses to an excitation

This paper focuses on the issue of nonlinear vibration responses identification of nonlinear systems. An efficient algorithm is presented, in which the nonlinear vibration system under studied is decomposed into a multiple-input/single-output (MISO) linear system with a series of power characterized inputs based on Volterra series, and the nonlinear output responses of different orders are identified by taking power spectra operation to the input and output data, revealing the contributions of each order nonlinearity to the output of the system. Compared to the existing method, the input signal of the system required in the presented approach need not remain unchanged in waveform and adjustable in magnitude. To verify the approach, a classic Duffing–Van der Pol oscillator was simulated, obtaining results very close to the fourth order Runge–Kutta method. Finally, an experiment analysis was carried out, in which the vibration transmission properties of a bolt connection were tested when the bolt was tight and loose, respectively. The results proved that the approach is effective in identifying nonlinear vibration frequency responses.

Exploring cardiovascular involvement in IgG4-related disease: a case series approach with cardiovascular magnetic resonance

BackgroundIgG4-related disease (IgG4-RD) is a relapsing–remitting, fibroinflammatory, multisystem disorder. Cardiovascular involvement from IgG4-RD has not been systematically characterised. In this study, we sought to evaluate consecutive patients with IgG4-RD using...

Time Series approach to map areas of Agricultural Plastic Waste generation

Abstract. The escalating presence of plastics in agriculture has raised concerns regarding Agricultural Plastic Waste (APW). Hitherto, a lack of comprehensive plasticulture data impedes effective waste management strategies, potentially resulting in plastic pollution and contributing to microplastic formation. APW locations and quantities are pivotal for territorial planning and the formulation of public policies on waste management and land use. Remote detection of agri-plastics has garnered increased consideration, particularly in mapping plastic-mulched farmlands (PMFs) dispersed in extensive regions. This study investigates whether the use of satellite image time series can map PMFs accurately. We assessed pixel-based classification using a 16-day composite time series of Sentinel-2 imagery (S2-16D) obtained from the Brazil Data Cube project. The Plastic-Mulched Landcover Index (PMLI) was joined into S2-16D bands because it is an index focused on PMF detection. Four classifiers (RF, MLP, L-TAE, and TCNN) were compared through agreed classification metrics. The most promising outcome showed an overall accuracy of 100% employing L-TAE but with visible noise in the map. The time series enhanced accuracy while minimizing background confusion, offering a viable solution for PMF detection. The PMF map presented herein represents an initial stride toward fostering circularity in plasticulture throughout South America.

The Effect of Diesel Vehicle Regulation on Air Quality in Seoul: Evidence from Seoul’s Low Emission Zone

This study investigates the effect of the low emission zone (LEZ), designed to restrict old diesel vehicles, on air quality in Seoul, Republic of Korea, using the regression discontinuity in time (RDiT) approach. While previous studies have examined LEZ impacts using traditional econometric models such as time series and panel data approaches, our research uniquely integrates high-frequency daily weather data to better control for confounding environmental variables and captures time-of-day effects on pollutant concentrations. Our findings reveal that the LEZ policy effectively reduced NO2 and SO2 concentrations by 4.7% and 11.6%, respectively. Notably, during daytime hours, when traffic is heaviest, NO2, SO2, and PM10 concentrations decreased by 7.1%, 14.8%, and 13.6%, respectively. These results suggest that the observed improvements can be attributed not only to reduced diesel vehicle registrations but also to significant declines in overall traffic volume.

A selective diesel vehicle ban’s impact on NO2 concentrations in Munich: a quasi-experimental study

Abstract Background The European Union’s current NO2 concentration limit of 40 μg/m³ is regularly exceeded in Munich, Germany. This limit is likely to be reduced towards the WHO recommended target of 10 μg/m³. In response, the city enacted a selective ban on older diesel vehicles, specifically those categorized as Euro 4 or older, within the current low-emission zone in February 2023. Our study examined how Munich’s selective ban on diesel vehicles impacted NO2 levels, concentrating on the six-month period after its introduction. Methods We employed a synthetic control approach in the primary analysis and a controlled interrupted time series approach in the secondary analysis. These quasi-experimental methods establish a ‘counterfactual’ no-intervention scenario, allowing for comparison with the actual observed scenario and estimation of the impact of the intervention. Historical data from monitoring stations within and outside the low-emission zone from 2014 to 2022 were utilized as controls, while potential confounders were considered. Results NO2 concentrations within Munich’s low-emission zone decreased across the study period. Effects of the selective diesel vehicle ban at monitoring stations within the low-emission zone were minimal, with wide confidence intervals indicating uncertainty. At Landshuter Allee, the average intervention effect was -2.67 μg/m3 (95%-CI = [-12.72; 7.38]), at Stachus it was -2.74 μg/m3 (95%-CI = [-9.91; 4.42]) and at Lothstrasse, it was -1.03 μg/m3 (95%-CI = [-7.75; 5.69]). The secondary analysis confirms these findings. Conclusions Munich’s selective diesel vehicle ban had limited effects in reducing NO2 concentrations. Factors such as its narrow focus on Euro 4 diesel vehicles, numerous exemptions, and unclear enforcement may explain this outcome. Comprehensive strategies and continuous monitoring and evaluation are crucial for effectively addressing urban air pollution and protecting public health. Key messages • Munich’s selective diesel vehicle ban had minimal effects on reducing NO2 levels, possibly due to its narrow scope, many exemptions, and unclear enforcement. • Current guidelines and court rulings signal the need for more effective solutions beyond selective bans, emphasizing the importance of well-designed interventions and rigorous evaluation.

Dynamic Analysis and FPGA Implementation of Fractional-Order Hopfield Networks with Memristive Synapse

Memristors have become important components in artificial synapses due to their ability to emulate the information transmission and memory functions of biological synapses. Unlike their biological counterparts, which adjust synaptic weights, memristor-based artificial synapses operate by altering conductance or resistance, making them useful for enhancing the processing capacity and storage capabilities of neural networks. When integrated into systems like Hopfield neural networks, memristors enable the study of complex dynamic behaviors, such as chaos and multistability. Moreover, fractional calculus is significant for their ability to model memory effects, enabling more accurate simulations of complex systems. Fractional-order Hopfield networks, in particular, exhibit chaotic and multistable behaviors not found in integer-order models. By combining memristors with fractional-order Hopfield neural networks, these systems offer the possibility of investigating different dynamic phenomena in artificial neural networks. This study investigates the dynamical behavior of a fractional-order Hopfield neural network (HNN) incorporating a memristor with a piecewise segment function in one of its synapses, highlighting the impact of fractional-order derivatives and memristive synapses on the stability, robustness, and dynamic complexity of the system. Using a network of four neurons as a case study, it is demonstrated that the memristive fractional-order HNN exhibits multistability, coexisting chaotic attractors, and coexisting limit cycles. Through spectral entropy analysis, the regions in the initial condition space that display varying degrees of complexity are mapped, highlighting those areas where the chaotic series approach a pseudo-random sequence of numbers. Finally, the proposed fractional-order memristive HNN is implemented on a Field-Programmable Gate Array (FPGA), demonstrating the feasibility of real-time hardware realization.

Full wavefield modeling and dispersion characteristics of underwater MASW

The underwater multichannel analysis of surface wave (UMASW) is becoming an essential tool for surveying subbottom shear wave velocity. Current practice is limited to interpretation based on the fundamental mode. This paper investigates the full dynamic response of the underwater-multilayered structure under two different types of sources (impact and explosion). Fundamental solutions in the transformed domain, after applying both Fourier transform and Fourier–Bessel transform, are derived utilizing the global stiffness matrix method, analogous to a 1-D finite element approach. Solutions in the physical domain are then obtained using the fast and accurate Fourier series and Fourier–Bessel series approach proposed in this paper. The most attractive feature of this novel approach is that the expansion coefficients (or Love numbers) can be pre-calculated, saved, and repeatedly used for other field points. Through numerical analyses, we quantitatively investigate the effect of the water depth and source/receiver types/locations on different wave features (i.e., Scholte, fast-guided, and acoustic modes) from different perspectives (i.e., dispersion curve, Green’s function, frequency-velocity spectrum (FVS), and waveform). We find that (1) unlike the impact source, stronger acoustic waves can be produced by the explosive source, which sometimes causes difficulty in identifying the Scholte waves. (2) The acoustic and interface-guided waves exhibit distinct behaviors depending on the source and receiver locations. Scholte waves and fast-guided waves are weakened when the source and receivers are elevated far from the water/soil interface. Moreover, (3) the response of the Scholte wave can be enhanced by muting the direct acoustic waves. However, (4) the fast-guided wave may also arise in underwater surveys when the Vs of the underlying half-space is much higher than those of the upper layers, exerting a significant influence on the shallow water scenarios and posing challenges for correct mode identification. With the ability to model the entire wavefield (or FVS) that takes into account all propagation modes and the actual survey configuration, inversion can be performed by fitting the entire FVS. This approach eliminates the need to pick dispersion curves and provides a more accurate and stronger model constraint.

Evaluating the Effectiveness of Macroeconomic Determinants on the Performance of the Dhaka Stock Exchange: A Time Series Approach

The present study examines the causal relationships between selected macroeconomic factors and the Dhaka Stock Exchange Broad Index (DSEX) performance from January 2014 to December 2023. Time series econometric techniques include Dickey-Fuller (ADF) and Phillips-Perron (PP) tests to verify stationarity, Granger causality to investigate short-term dynamics, and Johansen's co-integration test to evaluate long-term relationships. The results show that while there is no long-term cointegration, there are significant short-term relationships between macroeconomic variables and the DSEX broad index. GDP growth and unemployment significantly impact stock performance, with two-way causality. While interest rates and the money supply have little impact on the index, inflation forecasts trends in inflation even if it doesn't directly influence it. Overall, the DSEX broad index shows how important it is to Bangladesh's macroeconomic dynamics by accurately predicting changes in GDP growth, unemployment, and inflation. The researcher provides unique insights into the mechanics of the Bangladeshi stock market, filling a vacuum in the literature on emerging countries. It analyzes how macroeconomic conditions affect the DSEX index over time, including the COVID-19 pandemic. The study investigates the influence of several macroeconomic variables on DSEX Index performance, providing significant information for Bangladeshi investors and policymakers looking to increase profits, inform policy choices, and promote economic stability. To have a better knowledge of stock market dynamics, future studies should include other macroeconomic aspects and regional markets.

Literature Review on Stochastic Modeling of Wet and Dry Spells

With increasing concerns about climate change and its profound implications for water resources, stochastic modeling of wet and dry spells has emerged as a critical domain within hydrology and climatology, providing insight into the temporal patterns of precipitation and drought occurrences. This review of the literature synthesizes recent advances in the modeling of wet and dry spells using stochastic methods, focusing on publications from 2000 to 2024. The review examines various modeling approaches, including Markov chain models, mixed probability models, non-homogeneous models, and time series approaches. Key findings indicate that Markov chain models are effective in simulating the sequential occurrence of wet and dry spells, with higher-order variants addressing issues of overdispersion. Mixed probability models excel at representing heterogeneity and extremes in precipitation data, especially in regions with distinct wet and dry seasons. Non-homogeneous models are valuable for understanding the temporal dynamics and irregularities of dry spells, revealing significant shifts in their frequency and duration over time. These can be complemented by time-series methods to highlight long-term trends and seasonal variations. The review underscores the importance of regional specificity in modeling approaches to accurately capture local climatic and geographical variability. Recommendations for future research include the integration of hybrid models that combine the strengths of various approaches to improve predictive precision, focusing on the impacts of climate change, and conducting cross-regional comparisons to generalize findings and enhance the robustness of the models.