Past Values Research Articles

Seasonal Autoregressive Integrated Moving Average (SARIMA) for Melon (Cucumis melo) Yield from 2011 to 2020 Based on Planting Date Period

The main goal of time series modelling is to collect and analyze past values to develop appropriate models that describe the inherent structure and characteristics of the series about the planting date of melon. The data used in this study are the melon yield based on twenty-nine days of planting date in March from the first to the last planting date of March 2011 to 2020, to evaluate the prediction performances of the models, two indices: the root mean squared error (RMSE) and mean absolute percentage error (MAPE) were used to compare the forecasting capabilities of the SARIMA models, Excel was used first for preparing as CSV file and then changed to time series data to make the dataset of yield and R version 4.0.2 (the R Development Core Team) was used to perform ETS and SARIMA models For planting date time series based on yield, seasonal autoregressive integral moving average models (SARIMA) were constructed. The Student's t-test was one of the statistical tests used to evaluate the effectiveness and validity of the SARIMA models Through the use of SARIMA models, it is feasible to create synthetic records that maintain the statistical properties of the historical record. Finally, the results can be applied to different planting dates based on yield. Based on the Bayesian Information Criteria (BIC) values and the overall highest R2 values of 0.94, the optimal SARIMA (0,1,1) model was chosen. No limitations were found. It is the first time that this mathematical method was used to analyze time series data for 10 years of planting date of melon and it could be useful for agronomists and horticulturists to choose the best model.

Two-Step Medical Inflation Forecasts: Econometric Performance and Related Issues

Abstract This paper examines medical inflation forecasting based on a two-step method proposed by Gilbert (2019), whereby the medical inflation rate is forecast via the sum of two terms: a broad inflation published forecast and a historical average of the inflation gap—this being the difference between medical inflation and broad inflation. In a simple forecasting experiment, the two-step method compares favorably to the one-step method of forecasting medical inflation based on its past values alone. Stationarity tests applied to the inflation gap mostly support stationarity, with a possible historical break. The econometric results generally support the use of the two-step method, with a limited historical window for inflation gap averaging, consistent with Gilbert (2019).

Predictive Data Analysis: Leveraging RNN and LSTM Techniques for Time Series Dataset

Recurrent neural networks (RNNs) and long short-term memory (LSTM) models have demonstrated tremendous effectiveness in modeling time series data due to the ability they possess to capture temporal dependencies. Stock market data is an example of time series data. Stock price data is very volatile and dynamic therefore one of the most turbulent areas to invest in might be the stock market. The choice to buy or sell stocks is heavily influenced by external circumstances and statistical analysis of previous stock performance. Stock price index forecasting has been a major area of research for many years and many machine learning and deep learning methods have been proposed to simplify this hard process, but little success has been discovered so far.In this paper, the application of RNNs and LSTMs on a stock price dataset to predict future values is explored. We start with Recurrent neural networks (RNNs) to predict the values but one of the major challenges with it is the "vanishing gradient" problem, which makes it difficult for the network to learn long-term dependencies in the data. To overcome this long short-term memory (LSTM) was used which eliminates the vanishing gradient problem. Data were normalized and divided into time steps to determine the relationship between past values and future values to make accurate predictions. The outcomes show that the MLS LSTM method significantly outperforms existing algorithms, achieving a prediction accuracy of 98.1% on the training data set and a 91.97% accuracy on the testing data set.

Prediction of physical realizations of the coordinated universal time with gated recurrent unit.

Coordinated Universal Time (UTC), produced by the Bureau International des Poids et Mesures (BIPM), is the official worldwide time reference. Given that there is no physical signal associated with UTC, physical realizations of the UTC, called UTC(k), are very important for demanding applications such as global navigation satellite systems, communication networks, and national defense and security, among others. Therefore, the prediction of the time differences UTC-UTC(k) is important to maintain the accuracy and stability of the UTC(k) timescales. In this paper, we report for the first time the use of a deep learning (DL) technique called Gated Recurrent Unit (GRU) to predict a sequence of H futures values of the time differences UTC-UTC(k) for ten different UTC(k) timescales. UTC-UTC(k) time differences published on the monthly Circular T document of the BIPM are used as training samples. We utilize a multiple-input, multiple-output prediction strategy. After a training process where about 300 past values of the difference UTC-UTC(k) are used, H (H = 6) values of the Circular T can be predicted using p (typically p = 6) past values. The model has been tested with data from ten different UTC(k) timescales. When comparing GRU results with other standard DL algorithms, we found that the GRU approximation has a good performance in predicting UTC(k) timescales. According to our results, the GRU error in predicting UTC-UTC(k) values is typically 1ns. The frequency instability of the UTC(k) timescale is the main limitation in reducing the GRU error in the time difference prediction.

Bioenergy potential from agricultural by-product in 2030: An AI-based spatial analysis and climate change scenarios in a Chinese region

Addressing the pressing need for sustainable bioenergy sources amid the growing climate crisis, this study investigates potential crop bioenergy output in the Jianghan Plain, Hubei, China. The research leverages Backpropagation Artificial Neural Network models to predict the accumulated net primary productivity (NPP) of various crops, i.e., single rice, double rice, maize, and wheat, in Jianghan Plain. The models highlight a strong predictive performance, with the SE model achieving an R2 of 0.83 and wheat model an R2 of 0.90, implying these models can be utilized reliably for future NPP forecasting. Our evaluation of past and future NPP values reveals a negative correlation between the increase in NPP values and observed temperature trends. This emphasizes the significant impact of temperature on crop bioenergy potential. For future NPP predictions, scenario (S3a) under Temperature Control strategies at medium-effort mitigation yields a favourable outcome for all crops, particularly SE. Additionally, the results suggest that aggressive decarbonization and global warming mitigation strategies are vital for enhancing NPP values. However, the study also highlights potential trade-offs and stresses the necessity for region-specific climate reconciliation strategies; for instance, midland counties benefit from temperature control, whereas the sideline favours CO2 mitigation. The study further investigates the spatial distribution of potential bioenergy, with the southwestern part of the plain holding more bioenergy potential, providing an essential reference for future energy planning. By 2030, in Jianghan Plain, when fully integrated with reasonable climate responses, there could be substantial bioenergy potential from crop residues reaching (1.2–1.9) × 109 MJ, which is 11% less than the current.

Capital flight from BRICS nations: Does every cloud have a silver lining?

This study investigates the potential determinants of capital flight from BRICS countries. We use the residual method to estimate capital flight and employ GMM testing approach. We find a surge in the volume of capital flight from BRICS especially in the aftermath of global financial crisis. The empirical results suggest that, past values of the capital flight, real GDP growth rates, exchange rate depreciation, unemployment rate, business confidence index and financial stability indicator as significant factors causing resident capital outflows from BRICS nations. These findings call for an effective policy framework by the fiscal and monetary authorities.

Factors Influencing Stock Price Prediction: Demographics, Mental State, Company Origin, and Past Performance

This study investigates the interplay between socio-demographic factors, mental state, and stock price prediction behavior among a sample of 150 individuals in Ulaanbaatar, Mongolia. Participants were tasked with completing incomplete graphs, varying in context in three survey groups, allowing for an exploration of how age, gender, and past value perceptions, and contextual things influence prediction patterns. The research aims to understand the cognitive biases underpinning investment decisions, particularly in a context of limited financial literacy. By analyzing completed graphs, the study identifies distinct decision-making tendencies across age and gender groups. Additionally, it examines whether the type of given context of the graph impacts prediction behavior in a certain way. While emotional bias was found to be less influential, the study reveals significant differences in predictions based on contextual differences, also the study revealed certain age and gender group tendencies bias proneness for certain biases. These findings contribute to a deeper understanding of investor psychology and provide insights for financial advisors seeking to tailor strategies based on client demographics.

An Integrated Blockchain Based Real Time Stock Price Prediction Model by CNN, Bi LSTM and AM

The continuous growth of blockchain has brought revolutionary impact on different areas with characteristics like decentralization, trust-worthy and tampering-proof. In the financial field, forecasting stock market had remained a challenging task for researchers. A novel integration of convolution and LSTM along with attention mechanism is proposed for prediction of stock price for the last hour of a day based it’s performance in six hours for making prediction in intraday trades. The proposed prediction model uses 4 different stocks of Apple, Facebook, Nike and Uber for data input. The input data is given to the combination of many convolutional and LSTM layers for predicting future value of stocks in short term. Furthermore, the proposed model combines the obtained data with LSTM layers output vectors enhanced for final prediction. The prediction is based on past value of stocks. Performance evaluation is done based on RMSE and computational time. The average values obtained were 0.091 and 11.57 seconds respectively, showing it’s superiority from benchmark models. The obtained data is stored on blockchain for enhanced security. The model can further be tuned for predicting other markets and will be more efficient than market specific markets.

Uniform in number of neighbors consistency and weak convergence of $ k $NN empirical conditional processes and $ k $NN conditional $ U $-processes involving functional mixing data

<abstract><p>$ U $-statistics represent a fundamental class of statistics arising from modeling quantities of interest defined by multi-subject responses. $ U $-statistics generalize the empirical mean of a random variable $ X $ to sums over every $ m $-tuple of distinct observations of $ X $. Stute [182] introduced a class of so-called conditional $ U $-statistics, which may be viewed as a generalization of the Nadaraya-Watson estimates of a regression function. Stute proved their strong pointwise consistency to: $ r^{(m)}(\varphi, \mathbf{t}): = \mathbb{E}[\varphi(Y_{1}, \ldots, Y_{m})|(X_{1}, \ldots, X_{m}) = \mathbf{t}], \; \mbox{for}\; \mathbf{ t}\in \mathcal{X}^{m}. $ In this paper, we are mainly interested in the study of the $ k $NN conditional $ U $-processes in a functional mixing data framework. More precisely, we investigate the weak convergence of the conditional empirical process indexed by a suitable class of functions and of the $ k $NN conditional $ U $-processes when the explicative variable is functional. We treat the uniform central limit theorem in both cases when the class of functions is bounded or unbounded satisfying some moment conditions. The second main contribution of this study is the establishment of a sharp almost complete Uniform consistency in the Number of Neighbors of the constructed estimator. Such a result allows the number of neighbors to vary within a complete range for which the estimator is consistent. Consequently, it represents an interesting guideline in practice to select the optimal bandwidth in nonparametric functional data analysis. These results are proved under some standard structural conditions on the Vapnik-Chervonenkis classes of functions and some mild conditions on the model. The theoretical results established in this paper are (or will be) key tools for further functional data analysis developments. Potential applications include the set indexed conditional <italic>U</italic>-statistics, Kendall rank correlation coefficient, the discrimination problems and the time series prediction from a continuous set of past values.</p></abstract>

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

<p>This study aims to apply advanced machine-learning models and hybrid approaches to improve the forecasting accuracy of the US Consumer Price Index (CPI). The study examined the performance of LSTM, MARS, XGBoost, LSTM-MARS, and LSTM-XGBoost models using a large time-series data from January 1974 to October 2023. The data were combined with key economic indicators of the US, and the hyperparameters of the forecasting models were optimized using genetic algorithm and Bayesian optimization methods. According to the VAR model results, variables such as past values of CPI, oil prices (OP), and gross domestic product (GDP) have strong and significant effects on CPI. In particular, the LSTM-XGBoost model provided superior accuracy in CPI forecasts compared with other models and was found to perform the best by establishing strong relationships with variables such as the federal funds rate (FFER) and GDP. These results suggest that hybrid approaches can significantly improve economic forecasts and provide valuable insights for policymakers, investors, and market analysts.</p>

Forced convection past a circular cylinder in a planar Poiseuille flow

In this work the transfer of a passive scalar (temperature) from a circular cylinder (radius R) in a planar Poiseuille flow (height 2h) is investigated with numerical simulations. The study varies the channel half-height to cylinder radius ϵ=h/R, the inertial parameter Λ=(UcR/ν)(h/R)2 and the Péclet number Pe=UcR/α, where Uc is the midplane velocity, ν is the kinematic viscosity and α is the thermal diffusivity. There has been significant amount of work for ϵ≫1, therefore this work will focus on the range, 0.03≤ϵ≤1, which has been chosen to show the effect of geometry and inertia on the flow features and the subsequent transfer. As ϵ is decreased the Nusselt number increases and this behaviour is explained with velocity and temperature profiles.Particularly for ϵ=0.03, the local Nusselt number has a significant vertical variation (in the z-direction) when Λ and Pe are increased past certain values. For Λ≪1 and Pe>103 this is due to the non-uniform incident flow present in the Hele-Shaw cell while for Λ=O(1), it is the secondary flow that is induced at the sides of the cylinder. For these latter flows the transfer in the midplane is reduced relative to near the side walls, due to the triple turning point radial velocity profile adjacent to the circular cylinder. For Λ≤1, the Nusselt number is only a function of Pe. However, for Λ>1, the secondary flow results in a slightly increased Nusselt number.

On the robustification of digital event-based stabilizers for nonlinear time-delay systems

In this paper, the robust stabilization problem by means of quantized sampled-data event-based (QSE) controllers is investigated for nonlinear systems affected by state delays and unknown disturbances. In particular, a methodology for the design of robust QSE stabilizers is provided for control-affine nonlinear systems affected by unknown actuation disturbances and unknown measurement errors. Firstly, the notion of Steepest Descent Feedback (SDF), continuous or not, is suitably revised in order to deal with the robustification of event-based controllers. Then, Input-to-State Stability (ISS) redesign methodologies are used to provide the robustification term which is added to the SDF at hand in order to arbitrarily attenuate the effects of unknown external disturbances affecting the considered control scheme. A spline approximation approach is used in order to cope with the problem of the possible non-availability in the buffer of suitable past values of the system state required for the correct application of the proposed robust QSE controller. It is proved that there exist a suitably fast sampling and an accurate quantization of the input/output channels such that: the robust QSE implementation of SDFs, continuous or not, ensures the semi-global practical stability of the related closed-loop system, regardless of the above disturbances, provided that the observation errors affects marginally the new added control term. The stabilization in the sample-and-hold sense theory is used as a tool to prove the results. The provided results include the case of non-uniform quantization of the input/output channels and the case of aperiodic sampling. Applications are presented in order to validate the results.

Liberty and Order: Legal Interests in China's Domestic Crime Legislation and Amendment

The family is an independent community, in which members have closer life ties and deeper subjective emotions than ordinary social members. The diversity of the internal relationships within the family and the different expressions of family ethics determine the pluralistic normative construction of criminal law when it intervenes in the family. However, with the transformation of the social structure, the function of the family has gradually declined. The traditional Chinese society's "family-oriented" ideology has fallen behind, and the past value judgment of "emphasizing order and disregarding freedom" does not conform to the social values of the people. Firstly, in terms of the configuration of penalties, the statutory penalties for domestic crimes are lighter than those for similar crimes, which conflicts with the increasing sense of equality, rights, and modern family concepts in society. Secondly, in the judicial practice process, the judicial organs ignore unfair interest arrangements, attach importance to outdated value judgments, overemphasize departmental interests, and infringe on the legitimate rights and interests of vulnerable groups. These behaviors all go against the values of ordinary people. This article takes the crimes of purchasing trafficked women and child abuse in traditional Chinese family crimes as examples, combined with current social trends and the transformation of traditional family values and order. Based on the unique and complex features of the protected legal interests in family crimes, it balances the values of order protection and human rights protection, faces up to the connotation and extension changes of domestic crimes in the new era, and establishes the family law theory of criminal law.

Predictive Modeling of Light–Matter Interaction in One Dimension: A Dynamic Deep Learning Approach

The mathematical modeling and the associated numerical simulation of the light–matter interaction (LMI) process are well-known to be quite complicated, particularly for media where several electronic transitions take place under electromagnetic excitation. As a result, numerical simulations of typical LMI processes usually require a high computational cost due to the involvement of a large number of coupled differential equations modeling electron and photon behavior. In this paper, we model the general LMI process involving an electromagnetic interaction medium and optical (light) excitation in one dimension (1D) via the use of a dynamic deep learning algorithm where the neural network coefficients can precisely adapt themselves based on the past values of the coefficients of adjacent layers even under the availability of very limited data. Due to the high computational cost of LMI simulations, simulation data are usually only available for short durations. Our aim here is to implement an adaptive deep learning-based model of the LMI process in 1D based on available temporal data so that the electromagnetic features of LMI simulations can be quickly decrypted by the evolving network coefficients, facilitating self-learning. This enables accurate prediction and acceleration of LMI simulations that can run for much longer durations via the reduction in the cost of computation through the elimination of the requirement for the simultaneous computation and discretization of a large set of coupled differential equations at each simulation step. Our analyses show that the LMI process can be efficiently decrypted using dynamic deep learning with less than 1% relative error (RE), enabling the extension of LMI simulations using simple artificial neural networks.

Does archaeology deliver evidence about the past or co-create contemporary values?

The paper focuses on heritage value systems, particularly investigating the archaeological understanding of heritage values and evaluation. The literature review shows that the postmodern archaeological paradigm predominantly covers the topic, while the perspective of the intrinsic value is less explored. The starting point of our research is the thesis that archaeological paradigms obstruct better public support if they refrain from using axiological considerations. By archaeological paradigm, we refer to the processual and post-processual ones (the latter focusing on understanding past social phenomena). Axiology, as the theory of values, developed in the second half of the 20th century. Its approach is now used in many areas of social sciences (for example, education and medical care). Public archaeology is more open to societal needs than processual and post-processual archaeology but has yet to apply a values-based approach consistently. To clarify our thesis, we analyse the theoretical and practical considerations on the values of (archaeological) heritage, including the issues of the categorization of values, reflection on who assigns values to heritage and how this affects the reception of archaeological knowledge in society. The paper addresses some critical aspects of the evaluation in archaeological heritage conservation, management and building alliances with locals and communities who identify with heritage. I outline a comprehensive archaeological evaluation system considering systemic, extrinsic and intrinsic archaeological values in the conclusions.

Stock Price Prediction using Multi-Layered Sequential LSTM

Stock markets are frequently among the most volatile locations to invest in. The choice to buy or sell stocks is heavily influenced by statistical analysis of prior stock performance and external circumstances. All these variables are employed to maximize profitability. Stock value prediction is a hard undertaking that necessitates a solid computational foundation to compute longer-term share values. Stock prices are connected inside the market, making it harder to forecast expenses. Financial data is a category that includes past data from time series that provides a lot of knowledge and is frequently employed in data analysis tasks. This research provides a unique optimisation strategy for stock price prediction based on a Multi-Layer Sequential Long Short Term Memory (MLS LSTM) model and the adam optimizer in this context. Furthermore, to make reliable predictions, the MLS LSTM algorithm uses normalised time series data separated into time steps to assess the relationship between past and future values. Furthermore, it solves the vanishing gradient problem that plagues basic recurrent neural networks.

Visualization with Prediction Scheme for Early DDoS Detection in Ethereum.

Blockchain technologies have gained widespread use in security-sensitive applications due to their robust data protection. However, as blockchains are increasingly integrated into critical data management systems, they have become attractive targets for attackers. Among the various attacks on blockchain systems, distributed denial of service (DDoS) attacks are one of the most significant and potentially devastating. These attacks render the systems incapable of processing transactions, causing the blockchain to come to a halt. To address the challenge of detecting DDoS attacks on blockchains, existing visualization schemes have been developed. However, these schemes often fail to provide early DDoS detection since they typically display only past and current system status. In this paper, we present a novel visualization scheme that not only portrays past and current values but also forecasts future expected system statuses. We achieve these future predictions by utilizing polynomial regression with blockchain data. Additionally, we offer an alternative DDoS detection method employing statistical analysis, specifically the coefficient of determination, to enhance accuracy. Through our experiments, we demonstrate that our proposed scheme excels at predicting future blockchain statuses and anticipating DDoS attacks with minimal error. Our work empowers system managers of blockchain-based applications to identify and mitigate DDoS attacks at an earlier stage.

CanStoc: A Hybrid Stochastic–GCM System for Monthly, Seasonal and Interannual Predictions

Beyond their deterministic predictability limits of ≈10 days and 6 months, the atmosphere and ocean become effectively stochastic. This has led to the development of stochastic models specifically for this macroweather regime. A particularly promising approach is based on the Fractional Energy Balance Equation (FEBE), an update of the classical Budyko–Sellers energy balance approach. The FEBE has scaling symmetries that imply long memories, and these are exploited in the Stochastic Seasonal and Interannual Prediction System (StocSIPS). Whereas classical long-range forecast systems are initial value problems based on spatial information, StocSIPS is a past value problem based on (long) series at each pixel. We show how to combine StocSIPS with a classical coupled GCM system (CanSIPS) into a hybrid system (CanStoc), the skill of which is better than either. We show that for one-month lead times, CanStoc’s skill is particularly enhanced over either CanSIPS or StocSIPS, whereas for 2–3-month lead times, CanSIPS provides little extra skill. As expected, the CanStoc skill is higher over ocean than over land with some seasonal dependence. From the classical point of view, CanStoc could be regarded as a post-processing technique. From the stochastic point of view, CanStoc could be regarded as a way of harnessing extra skill at the submonthly scales in which StocSIPS is not expected to apply.

Volume Progression and Price–Volume Relationship of Commodity Futures: Case of Bullion and Base Metals

Purpose: The purpose of this study is to examine the behaviour of trading volumes in commodity futures markets over the life of the contract and to analyse the relationship between changes in price, volume and open interest. Methodology: The volume progression is examined by dividing the life of a futures contract to six phases and analysing how the volumes picked up during each phase. The relationship between price, volume and open interest was examined using the Pearson correlation and the vector autoregression (VAR) model. Findings: It is found that the volumes in commodity futures are clustered around the last phase of the contract’s life. As far as the relationship between price, volume and open interest is concerned, there is a very low correlation between the variables. Price is not influenced by volume or open interest. The volume changes are found to be influenced by open interest, and the open interest is influenced by its own past values. Practical Implications: The findings of the study have implications for traders in the commodity futures market as well as policymakers as it throws light on the crucial issue of lack of volumes in the futures market. Contribution/Originality: The study contributes to the literature in terms of analysis of volume progression, which is not researched extensively in the Indian market. The findings of the study, which contradict the findings of other similar studies, open up scope for further research.

The Relationship Between Oil Prices and Stock Prices of the European Renewable Energy Companies: A Vector Autoregressive Analysis

Abstract This article aims to examine the potential relationship between Brent crude oil futures prices and the index of the European renewable energy companies. After the overview of the European legislation and the most recent literature review on the topic, the article deploys a method of the Vector Autoregressive Model (VAR). The analysis includes weekly data over eight years (2015-2022). Our results indicate a positive correlation between Brent crude oil futures prices and the value of the European Renewable Energy Total Return (ERIX) index. The estimated bivariate VAR model indicates a statistically significant relationship, meaning that past values of the ERIX Index may be used to predict future Brent crude oil prices in the long run. Considering the most recent systemic disturbance in the world’s commodity market, future research should consider longer time series and possible relationships of other macroeconomic factors.