Time Horizons Research Articles

Analyzing the Impact of Monetory Policy Shocks on Output, Inflation and Exchange Rate Dynamics of Pakistan

The major objective of this study is to analyse that how monetary policy of Pakistan had impact on output, inflation and exchange rate fluctuations during Global Financial Crisis 2008-2009 in Pakistan. Quarterly data was used for this purpose. Data spans from 1990Q1 to 2022Q4. Time varying parameter – vector auto regression model (TVP-VAR) of Jouchi Nakajima (2011) has been used for analysis. Through this model we selected the time horizons such as 2008, 2009 and 2015 mainly to study the impact of monetary policy on different selected variables. Our empirical finding indicates that monetary policy shock plays an important role in the economic dynamics of Pakistan. The impulse response of inflation is negative to tight monetary policy shock (εi↑→ p), this is in accordance with the standard economic theory. In case of Pakistan, after 1990 there is declining trend in volatility of output and a considerable reduction in business cycle fluctuations. Policy rate is more volatile during 1998. After 2000 there is decline in volatility of policy rate but in 2008-09 there is rise in interest rate because of Global Financial Crises in 2007 and 2008. The response of policy rate to inflation shock is in rising trend in 2007 and 2008. The inflation is increasing as a response to positive output shocks after 12th quarter this rising trend is also evident during 2008 where the impact of output on inflation is long term.

Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques

In the evolving field of Intelligent Transportation Systems (ITSs), accurate and reliable traffic prediction is essential in enhancing management and planning capabilities. Accurately predicting traffic conditions over both short-term and long-term intervals is vital for the practical application of ITS. The integration of deep learning into traffic prediction has proven crucial in advancing traffic prediction beyond traditional approaches, particularly in analyzing and forecasting complex traffic scenarios. Despite these advancements, the existing methods are unable to effectively handle both short-term and long-term traffic patterns given their complex nature, revealing a need for more comprehensive forecasting solutions. To address this need, we propose a new approach named the Short-Term and Long-Term Integrated Transformer (SLIT). SLIT is a Transformer-based encoder–decoder architecture, designed for the effective prediction of both short-term and long-term travel time durations. The architecture integrates the Enhanced Data Preprocessing (EDP) with the Short-Term and Long-Term Integrated Encoder–Decoder (SLIED). This harmonious combination enables SLIT to effectively capture the complexities of traffic data over varying time horizons. Extensive evaluations on a large-scale real-world traffic dataset demonstrate the excellence of SLIT compared with existing competitive methods in both short- and long-term travel time predictions across various metrics. SLIT exhibits significant improvements in prediction results, particularly in short-term forecasting. Remarkable improvements are observed in SLIT, with enhancements of up to 9.67% in terms of all evaluation metrics across various time horizons. Furthermore, SLIT demonstrates the capability to analyze traffic patterns across various road complexities, proving its adaptability and effectiveness in diverse traffic scenarios with improvements of up to 10.83% in different road conditions. The results of this study highlight the high potential of SLIT in significantly enhancing traffic prediction within ITS.

Enabling active visitor management: local, short-term occupancy prediction at a touristic point of interest

After the temporary shock of the Covid-19 pandemic, the rapid recovery and resumed growth of the tourism sectors accelerates unsustainable tourism, resulting in local (over-)crowding, environmental damage, increased emissions, and diminished tourism acceptance. Addressing these challenges requires an active visitor management system at points of interest (POI), which requires local and timely POI-specific occupancy predictions to predict and mitigate crowding. Therefore, we present a new approach to measure visitor movement at an open-spaced, and freely accessible POI and evaluate the prediction performance of multiple occupancy and visitor count machine learning prediction models. We analyze multiple case combinations regarding spatial granularity, time granularity, and prediction time horizons. With an analysis of the SHAP values we determine the influence of the most important features on the prediction and extract transferable knowledge for similar regions lacking visitor movement data. The results underline that POI-specific prediction is achievable with a moderate relation for occupancy prediction and a strong relation for visitor count prediction. Across all cases, XGBoost and Random Forest outperform other models, with prediction accuracy increasing as the prediction time horizon shortens. For effective active visitor management, combining multiple models with different spatial aggregations and prediction time horizons provides the best information basis to identify appropriate steering measures. This innovative application of digital technologies facilitates information exchange between destination management organizations and tourists, promoting sustainable destination development and enhancing tourism experience.

Global Warming Potentials of CO2 Uptake, CH4 Emissions, and Albedo Changes in a Restored Mangrove Ecosystem

AbstractRestoration of mangrove forests has garnered increasing global prominence as a nature‐based solution for carbon (C) sequestration. However, it was unclear whether the radiation forcing induced by methane (CH4) emissions and albedo changes during mangrove restoration processes can offset the cooling effect resulting from the net carbon dioxide (CO2) uptake. In this study, we measured the CO2, CH4, and albedo during 2020–2022 using an open‐path eddy covariance system in an 8‐year restored mangrove ecosystem afforested in Zhejiang Province, China. Their integrated global warming potentials (GWPs) were calculated to assess the climatic impact of mangrove restoration. The results showed that the restored mangroves functioned as a CO2 sink and a CH4 source, with annual values of −656.75 to −465.41 and 5.54 to 9.07 g C m−2 yr−1, respectively. The albedo varied slightly with a range of 0.11–0.13. The integrated GWPs of CO2, CH4, and albedo were −1,354.00 and −1,875.70 g CO2‐eq m−2 yr−1 over the 20‐ and 100‐year time horizons, respectively. The negative values indicated that the mangrove restoration had a net cooling effect, mainly due to CO2 uptake. The warming effects caused by CH4 emissions and albedo changes had the potential to partially offset CO2 uptake by 12.55%–36.51% and 0.08%–0.42%, respectively. Random forest analysis showed that photosynthetically active radiation (PAR) was the dominant driver on integrated GWPs with feature importance values of 0.34. Our results revealed that the cooling benefit of 8‐year restored mangroves remained significant, even when it was partially offset by CH4 emissions and albedo changes.

Explainable hypoglycemia prediction models through dynamic structured grammatical evolution

Effective blood glucose management is crucial for people with diabetes to avoid acute complications. Predicting extreme values accurately and in a timely manner is of vital importance to them. People with diabetes are particularly concerned about suffering a hypoglycemia (low value) event and, moreover, that the event will be prolonged in time. It is crucial to predict hyperglycemia (high value) and hypoglycemia events that may cause health damages in the short term and potential permanent damages in the long term. This paper describes our research on predicting hypoglycemia events at 30, 60, 90, and 120 minutes using machine learning methods. We propose using structured Grammatical Evolution and dynamic structured Grammatical Evolution to produce interpretable mathematical expressions that predict a hypoglycemia event. Our proposal generates white-box models induced by a grammar based on if-then-else conditions using blood glucose, heart rate, number of steps, and burned calories as the inputs for the machine learning technique. We apply these techniques to create three types of models: individualized, cluster, and population-based. They all are then compared with the predictions of eleven machine learning techniques. We apply these techniques to a dataset of 24 real patients of the Hospital Universitario Principe de Asturias, Madrid, Spain. The resulting models, presented as if-then-else statements that incorporate numeric, relational, and logical operations between variables and constants, are inherently interpretable. The True Positive Rate and True Negative Rate metrics are above 0.90 for 30-minute predictions, 0.80 for 60 min, and 0.70 for 90 min and 120 min for the three types of models. Individualized models exhibit the best metrics, while cluster and population-based models perform similarly. Structured and dynamic structured grammatical evolution techniques perform similarly for all forecasting horizons. Regarding the comparison of different machine learning techniques, on the shorter forecasting horizons, our proposals have a high probability of winning, a probability that diminishes on the longer time horizons. Structured grammatical evolution provides advanced forecasting models that facilitate model explanation, modification, and retesting, offering flexibility for refining solutions post-creation and a deeper understanding of blood glucose behavior. These models have been integrated into the glUCModel application, designed to serve people with diabetes.

MRI-Derived AD Signature of Cortical Thinning and Plasma P-Tau217 for Predicting Alzheimer Dementia Among Community-Dwelling Older Adults.

Structural brain MRI and blood-based phosphorylated tau (p-tau) measures are among the least invasive and least expensive Alzheimer's disease (AD) biomarkers to date. The extent to which these biomarkers may outperform one another in predicting future Alzheimer dementia diagnosis is poorly understood, however. This study investigated 2 specific AD biomarkers, i.e., a cortical thickness signature of AD (AD-CT) and plasma p-tau217, for predicting Alzheimer dementia. Data came from community-dwelling older participants of the Religious Orders Study or the Rush Memory and Aging Project. AD-CT was obtained from 3T MRI scans using a magnetization-prepared rapid acquisition gradient echo sequence and by averaging thickness from previously identified cortical regions implicated in AD. Plasma p-tau217 was quantified using an immunoassay developed by Lilly Research Laboratories on the MSD platform. Both MRI scans and blood specimens were collected at the same visits, and subsequent diagnoses of Alzheimer dementia were determined through annual detailed clinical evaluations. Cox proportional hazards models examined the associations of the 2 biomarkers with incident Alzheimer dementia, and prediction accuracy was assessed using c-statistics. A total of 198 older adults, on average 84 years of age, were included. Over a mean follow-up of 4 years, 60 (30%) individuals developed Alzheimer dementia. AD-CT (hazard ratio: 1.71, 95% CI 1.26-2.31) and separately plasma p-tau217 (hazard ratio: 2.57, 95% CI 1.83-3.61) were associated with incident Alzheimer dementia. The c-statistic for prediction accuracy was consistently higher for plasma p-tau217 (between 0.74 and 0.81) than AD-CT (between 0.70 and 0.75) across a range of time horizons. Furthermore, with both biomarkers included in the same model, there was only modest improvement in the c-statistic due to AD-CT. Plasma p-tau217 outperforms an imaging-based cortical thickness signature of AD in predicting future Alzheimer dementia diagnosis. Furthermore, the AD cortical thickness signature adds little to the prediction accuracy above and beyond plasma p-tau217.

Improving algorithmic trading consistency via human alignment and imitation learning

Research on algorithmic trading using reinforcement learning has become increasingly popular in recent years. Although most of the current reinforcement learning methods are employed to train the agent for some kind of modeling or data problem, it is worthwhile to explore in aligning agents with human behavior in applications as crucial as financial trading. Achieving such consistency by incorporating human expert experience into agent behavior is a key for potential improvements in this field. Imitation learning learns directly from examples of humans or other agents performing tasks. However, using imitation learning alone suffers from the problem of transitionally fitting expert example data. By combining the advantages of imitation learning and the Advantage Actor–Critic method, the Human Alignment Advantage Actor–Critic (HA3C) algorithm is proposed, to enhance single-asset trading strategy. First, by adding daily and weekly frequency trading data as input features to TimesNet, which is specifically designed to extract correlated temporal patterns from time-series data, it can capture both short-term and long-term features, thus capturing time-series features more comprehensively. Second, an expert action labeling method is proposed to train a strategy prediction network through supervised learning of behavior imitation. Third, a pre-trained strategy network is transferred to balance the exploration and exploitation of the agent’s behavior. Imitation learning techniques leverage finance-specific knowledge to enhance algorithmic trading consistency. This approach enables algorithms to mimic and adapt human decision-making patterns in finance, ultimately improving overall performance. This paper introduces a novel return-based function that efficiently balances short-term and long-term returns over flexible time horizons. It considers the maximum return from different positions and uses flexible time windows to capture trends while maximizing returns. Finally, evaluation on six commonly used datasets, such as DJI and SP500, demonstrates the advantages of the proposed HA3C algorithm compared with other classical and reinforcement learning-based strategies. Notably, on the HSI dataset, the HA3C strategy significantly outperforms other methods, achieving an impressive cumulative return of 681.55% and a Sharpe ratio of 5.07. These results show the superior performance of the HA3C algorithm in enhancing stock trading strategies and its potential to impact algorithmic trading consistency through aligning agent behavior with human expertise.

Periodic Transformer Encoder for Multi-Horizon Travel Time Prediction

In the domain of Intelligent Transportation Systems (ITS), ensuring reliable travel time predictions is crucial for enhancing the efficiency of transportation management systems and supporting long-term planning. Recent advancements in deep learning have demonstrated the ability to effectively leverage large datasets for accurate travel time predictions. These innovations are particularly vital as they address both short-term and long-term travel demands, which are essential for effective traffic management and scheduled routing planning. Despite advances in deep learning applications for traffic analysis, the dynamic nature of traffic patterns frequently challenges the forecasting capabilities of existing models, especially when forecasting both immediate and future traffic conditions across various time horizons. Additionally, the area of long-term travel time forecasting still remains not fully explored in current research due to these complexities. In response to these challenges, this study introduces the Periodic Transformer Encoder (PTE). PTE is a Transformer-based model designed to enhance traffic time predictions by effectively capturing temporal dependencies across various horizons. Utilizing attention mechanisms, PTE learns from long-range periodic traffic data for handling both short-term and long-term fluctuations. Furthermore, PTE employs a streamlined encoder-only architecture that eliminates the need for a traditional decoder, thus significantly simplifying the model’s structure and reducing its computational demands. This architecture enhances both the training efficiency and the performance of direct travel time predictions. With these enhancements, PTE effectively tackles the challenges presented by dynamic traffic patterns, significantly improving prediction performance across multiple time horizons. Comprehensive evaluations on an extensive real-world traffic dataset demonstrate PTE’s superior performance in predicting travel times over multiple horizons compared to existing methods. PTE is notably effective in adapting to high-variability road segments and peak traffic hours. These results prove PTE’s effectiveness and robustness across diverse traffic environments, indicating its significant contribution to advancing traffic prediction capabilities within ITS.

Deep Survival Models Can Improve Long-Term Mortality Risk Estimates from Chest Radiographs

Deep learning has recently demonstrated the ability to predict long-term patient risk and its stratification when trained on imaging data such as chest radiographs. However, existing methods formulate estimating patient risk as a binary classification, typically ignoring or limiting the use of temporal information, and not accounting for the loss of patient follow-up, which reduces the fidelity of estimation and limits the prediction to a certain time horizon. In this paper, we demonstrate that deep survival and time-to-event prediction models can outperform binary classifiers at predicting mortality and risk of adverse health events. In our study, deep survival models were trained to predict risk scores from chest radiographs and patient demographic information in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial (25,433 patient data points used in this paper) for 2-, 5-, and 10-year time horizons. Binary classification models that predict mortality at these time horizons were built as baselines. Compared to the considered alternative, deep survival models improve the Brier score (5-year: 0.0455 [95% CI, 0.0427–0.0482] vs. 0.0555 [95% CI, (0.0535–0.0575)], p < 0.05) and expected calibration error (ECE) (5-year: 0.0110 [95% CI, 0.0080–0.0141] vs. 0.0747 [95% CI, 0.0718–0.0776], p < 0.05) for those fixed time horizons and are able to generate predictions for any time horizon, without the need to retrain the models. Our study suggests that deep survival analysis tools can outperform binary classification in terms of both discriminative performance and calibration, offering a potentially plausible solution for forecasting risk in clinical practice.

Climate change risks to mine closure

Robust planning for mine closure is crucial for minimising negative environmental impacts and ensuring a positive post-mining legacy. Even though climate is an important factor in the design and management of most post-mine landscape features, general understanding of the multi-facetted nature of climate change risks to mine closure outcomes is limited. An approach is presented to mapping the climate change risk while elucidating the nature of its uncertainty. The approach is demonstrated using five mine closure planning and design considerations, namely: pit lake hydrology, landform stability, flooding, revegetation, and acid and metalliferous drainage. The risk to each of these considerations is quantified with a representative climate index, namely: aridity index, rainfall erosivity, annual maximum 1-day precipitation, extreme drought frequency, and drying period duration. Change in climate indices are derived relative to baseline conditions for two future 30-year periods (2040–2069, and 2070–2099) from an 11-member ensemble of dynamically downscaled simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) for two emission scenarios (Representative Concentration Pathways 4.5 and 8.5). The approach is demonstrated for mines within Queensland, Australia, with future change and uncertainty in indices mapped spatially. Results show that annual maximum 1-day precipitation and extreme drought frequency is expected to become more extreme into the future irrespective of future time period, emission scenario or geographic region; whilst the expected direction of change varies spatially for the aridity index, rainfall erosivity and drying period duration. Most indices suggest that the greatest changes will occur in the Mount Isa mining region, whereas the Weipa mining region projections are highly uncertain. The research demonstrates the multiple facets of climate change risks that need consideration in mine closure planning and design. It provides new insights into the variabilities among indices, across mining regions, over ensemble members and between time horizons that need to be factored in by mine closure stakeholders.

A time-varying analysis between economic uncertainty and tourism development in Singapore.

Tourism development (TO) is seen as a viable solution to address economic policy uncertainty (EPU) risks. However, previous studies have largely ignored the relationship between short, medium, and long term by decomposing TO and EPU index at different time-frequency scales, especially in Singapore. In this study, the Wavelet tools analysis and a rolling window algorithm are employed to re-visit the causal relationship between EPU, industrial production index (IPI), government revenue (GR), and tourism development (TO) in Singapore from January 2003 to February 2022. The findings revealed the heterogeneous effects of EPU on TO at different time horizons in terms of importance and magnitude over time. A rise in EPU results in a decline in TO at the low frequencies, indicating that EPU has a detrimental effect on TO over the short term. Conversely, in the long term, an increase in TO results in a decrease in EPU. Furthermore, the outcome also indicated that there is a uni-directional causality running from TO to EPU, GR and IPI. Expressly, we confirm that the negative co-movement is more pronounced in the aftermath of the COVID-19 crisis, particularly for EPU, and GR at low-medium frequencies throughout the research period. The findings provide tourism policymakers with insight to develop strategic plans for tourism development that consider the effects of economic policy uncertainty. By understanding how uncertainty impacts tourism, governments can tailor development strategies to mitigate risks and capitalize on opportunities.

When, at what speed, and how? Resilient transformation of the Vesdre river basin (Belgium) following the 2021 floods

BackgroundDual crises happen when an acute shock unfolds in the context of a creeping crisis. The July 2021 floods in the Vesdre river basin (Wallonia, Belgium) is a typical case of such dual crises in the context of climate change. This study is based on 16 semi-structured interviews (conducted in Spring 2023) with 10 mayors, 4 representatives of the Public Service of Wallonia, 1 person working for the federal government, and 4 project managers, coupled with a document analysis (n = 13). It investigates the temporal strategies that connect short and long-term considerations in the aftermath of this disaster (timing, futuring, pacing, cyclical adaptation, and determining time horizons), at two different governmental levels: river basin and municipal level.ResultsIn general, the window of opportunity to improve disaster resilience has been seized. Several studies were initiated by the Walloon region that shape the idea of an ideal future for the river basin and give recommendations for how to reach it. Unfortunately, those recommendations still come late compared to the temporal reality of the reconstruction process. Municipalities wish to strengthen disaster resilience as soon as possible, but they have to prioritize certain actions over others because of limited resources. The recommendations are considered flexible enough to adapt strategies to future contexts, but no monitoring and evaluation system for doing so has been implemented so far. In addition, clear policy agendas with transformational goals are scarce, and they diverge between the river basin and the municipalities. All these temporal strategies are shaped by elements of the institutional policy arrangement: resources, which affect them all, as well as actors, power, and formal rules, which affect some. These policy dimensions notably slow down the implementation of disaster resilience strategies and limit the determination of consensual time horizons.ConclusionsThe temporal strategies are passively shaped by the policy arrangement dimensions to a greater extent than actively chosen by the stakeholders. A structural transformation of the institutional policy arrangement is therefore needed to enable more coherent temporal strategies between different governance levels and to facilitate the consideration of long-term resilience during the recovery process from disasters.

Solar power forecasting using domain knowledge

Integrating renewable energy into the existing energy market is crucial. Solar power forecasting is essential since it depends on weather parameters and must integrate with the central grid to use the produced solar power effectively. Contemporary studies indicate that machine learning has the potential to predict the future generation of solar energy based on past data. This research demonstrates a broad range of solar power forecasting, combining the one-year time series solar power generation data, solar panel physical features, and weather information with the help of machine learning and deep learning tools with domain knowledge. The dataset is curated and preprocessed. We propose a deep learning ensemble model based on BI-LSTM. The proposed model can forecast well regardless of geographical position and is able to predict both short-term and long-term time horizons. We compared the results of the proposed model with the existing dataset and multiple standard deep learning models and found that our model produced better performance than traditional models. We also validated our model using different solar plants in Durgapur, India. For long-term forecasting, our model also outperformed the base model.

Analysing the performance of a health innovation ecosystem in the COVID-19 crisis: complexity and chaos theory perspective

BackgroundThis research delves into the complexity management of collaborative networks and interorganizational systems in the health innovation ecosystem on the basis of a best practice in the coronavirus disease 2019 (COVID-19) crisis. The objective is to offer specific solutions and guidelines to stakeholders in the health innovation ecosystem to control the chaos resulting from unexpected events along the ecosystem development and evolution path.MethodsFor this purpose, the performance of the Health Innovation Ecosystem in Iran (the Every Home is a Health Base plan) has been examined through a detailed and in-depth analysis of events and actions taken using documents, reports and interviews with experts. The practical application of chaos and complex adaptive system features (adaptation, time horizons, edge of chaos, sensitivity to initial conditions, state space and strange attractors) is introduced to identify and manage the transition from a state where the health innovation ecosystem is on the edge of chaos and prone to failure. Data were collected through studying documents, reports and interviews with experts, and then analysed using qualitative content analysis techniques, open and axial coding and metaphors derived from complexity and chaos theories.ResultsThe findings indicate that to understand and embrace the complexity of the health innovation ecosystem throughout its development and evolution and manage and lead it through the edge of chaos towards successful interorganizational systems performance, it is necessary to use gap analysis to achieve consensus, establish a highly interactive governance structure with key stakeholders of the ecosystem, maintain flexibility to control bifurcations (butterfly effect), prevent transforming emergency solutions into standard routines and ensure the sustainability of the ecosystem against future threats by long-term financial security.ConclusionsThis research provides insights into the dynamics of complex health systems and offers strategies for promoting successful innovation through collaborative networks and interorganizational systems in the development and evolution of the health innovation ecosystem. By embracing complexity and chaos, healthcare professionals, policy-makers and researchers can collaboratively address complex challenges and improve outcomes in health network activities. The conclusion section provides guidelines for successfully managing the complexity of the ecosystem and offers suggestions for further research.

Would really long-only climate-transition strategies in commodities bring lower market risk for sustainable markets in the long run? The Islamic sustainable market versus the global sustainability leaders

By allocating investments towards commodities that align with climate-transition goals, environmentally conscious commodity investment strategies serve to promote and support sustainable markets, channeling capital towards sectors that prioritize environmental sustainability. Through the application of a quantile causality test, which examines the relationship between commodity-based strategies with a climate-transition focus and eco-friendly markets, over the period spanning from May 1, 2013, to May 25, 2023, our findings reveal a bi-directional causality relationship between different themes of sustainable markets and long-only climate-transition strategies in the commodity market across various market conditions. Furthermore, employing a quantile time-frequency connectedness approach allows us to discern that long-only climate-transition strategies in the commodity market exhibit lower long-run total connectedness with responsible and conscious markets compared to the short term. Consequently, these results suggest that transition-oriented strategies for commodities in a climate-conscious world not only mitigate market risk for regenerative markets in the long run but also indicate that different types of global sustainability leaders demonstrate a stronger connectedness with climate-transition strategies in commodities when compared to the Islamic sustainable market across a majority of quantiles and time horizons. In light of these findings, policymakers are urged to prioritize the long-term dimensions of climate-transition strategies in commodity markets by implementing new emission standards and environmental benchmarks. Additionally, the design and implementation of similar long-only climate-transition strategies in other markets would further enhance the long-term effectiveness of climate-conscious markets and foster stronger connections with responsible markets. our study underscores the significance of integrating climate-transition strategies into commodity markets and highlights their role in promoting sustainable and environmentally conscious investment practices. By directing investments towards climate-aligned commodities, policymakers and market participants can contribute to the long-term sustainability of global markets while fostering stronger connections between sustainable markets and climate-transition strategies in commodities.

RISK AND RETURN ANALYSIS OF SIP V/S LUMP SUM INVESTMENT IN MUTUAL FUND SCHEME (EQUITY & HYBRID & ELSS)

This paper conducts a comprehensive risk and return analysis comparing systematic investment plan (SIP) and lump sum investment approaches in various categories of mutual fund schemes, including equity, hybrid, and Equity Linked Savings Schemes (ELSS). The study examines historical data to evaluate the performance, volatility, and risk-adjusted returns of both SIP and lump sum investments over different time horizons. Key metrics such as annualized returns, standard deviation, Sharpe ratio, and maximum drawdown are employed to provide insights into the comparative risk and return profiles of these investment strategies. The findings contribute to a better understanding of the trade-offs between SIP and lump sum investments in different mutual fund categories, aiding investors in making informed decisions based on their risk tolerance, investment objectives, and time horizon

Considering the development of organic farming in Poland at different territorial aggregation levels

The aim of this paper is to show and assess how the level of data aggregation can change the way the development of organic agriculture in Poland in 2014–2021 is perceived. A composite indicator of eco-farming at different levels of territorial aggregation and in different time horizons has been built taking into account sub-components and individual indicators. The performance of organic agriculture with the initial effects of the pandemic (for 2020–2021) was also quantified. The results showed that, in populationally larger territorial units (NUTS-1, NUTS-2, NUTS-3), organic farming characteristics are less variable, whereas the same characteristics show greater variability in smaller territorial units (LAU-1, LAU-2). This reflects the strong regional character of eco-agriculture. It can be observed especially in northern and south-eastern Poland. Moreover, although the highest performance of organic farming was observed in 2021, the effects of the pandemic crisis were highly heterogeneous by location. The results may be crucial for farmers and policymakers in planning sustainable agricultural strategies and building resilient organic regions.

Uniform asymptotics for a risk model with constant force of interest and a random number of delayed claims

Consider an insurance risk model with constant force of interest under the assumption that an immediate claim is accompanied by a random number of delayed claims. In the presence of heavy tails on claim distributions and dependence structures among modelling components, we study the uniform asymptotics for an insurer's ruin-related quantities, where the uniformity holds for all time horizons varying in a relevant interval.

Fair Funding Decisions: Consistency of the Time Horizons Used in the Calculation of Quality-Adjusted Life Years for Therapies for Very Rare Diseases by the National Institute for Health and Care Excellence in England.

The National Institute for Health and Care Excellence (NICE) in England uses quality-adjusted life years (QALYs) to assess the cost-effectiveness of treatments. A QALY is a measure that combines the size of the clinical benefit of a treatment with the time the patient benefits from it, i.e., the time horizon. We wanted to know how consistently QALY gains are calculated at NICE. Therefore, we have analysed information on the time horizons used for the QALY calculations of the concluded evaluations conducted under the Highly Specialised Technologies programme for treatments of very rare diseases at NICE. For treatments with final guidance published by December 2023 (n = 29), a time horizon of median 97.5 years (range: 35 to 125 years) was used to calculate the QALY gains. For most QALY calculations, the accepted time horizon was longer than either the expected treatment duration or the estimated life expectancy. In contrast, for the only technology with a final negative funding decision, i.e., afamelanotide for treating the lifelong chronic disease erythropoietic protoporphyria, a time horizon that was shorter than the expected treatment duration was used. The fairness and consistency of the evaluation process of treatments for very rare diseases at NICE should be reviewed.

Forecasting the Exceedances of PM2.5 in an Urban Area

Particular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, lung cancer, etc. Accurate air-quality forecasting on a regional scale enables local agencies to design and apply appropriate policies (e.g., meet specific emissions limitations) to tackle the problem of air pollution. Under this framework, low-cost sensors have recently emerged as a valuable tool, facilitating the spatiotemporal monitoring of air pollution on a local scale. In this study, we present a deep learning approach (long short-term memory, LSTM) to forecast the intra-day air pollution exceedances across urban and suburban areas. The PM2.5 data used in this study were collected from 12 well-calibrated low-cost sensors (Purple Air) located in the greater area of the Municipality of Thermi in Thessaloniki, Greece. The LSTM-based methodology implements PM2.5 data as well as auxiliary data, meteorological variables from the Copernicus Atmosphere Monitoring Service (CAMS), which is operated by ECMWF, and time variables related to local emissions to enhance the air pollution forecasting performance. The accuracy of the model forecasts reported adequate results, revealing a correlation coefficient between the measured PM2.5 and the LSTM forecast data ranging between 0.67 and 0.94 for all time horizons, with a decreasing trend as the time horizon increases. Regarding air pollution exceedances, the LSTM forecasting system can correctly capture more than 70.0% of the air pollution exceedance events in the study region. The latter findings highlight the model’s capabilities to correctly detect possible WHO threshold exceedances and provide valuable information regarding local air quality.