Moving Average Analysis Research Articles

Analysing the intrinsic multifractal nature of seismic sequences distributed along the Pacific Ring of Fire

A multifractal methodology was utilized to analyze a set of seismic sequences distributed along the Pacific Ring of Fire, sourced from the National Earthquake Information Center (NEIC) catalog. The analysis employed the Multifractal Detrended Moving Average (MFDMA) analysis method to characterize the presence of intrinsic multifractality using different multifractal indicators. The findings of this study can be summarized as follows: our statistical tests confidently assert that only the Kuriles, Peru, and Solomon Islands zones demonstrate an absence of intrinsic multifractality; tests were also applied using the exponent , but they were inconclusive. We believe these results contribute to an enhanced understanding of the multifractal characteristics of seismic activity and their implications for earthquake dynamics.

Forecasting Inflation in Kenya Using ARIMA Model

This study was to investigates the dynamics of inflation in Kenya through the application of advanced time series modeling techniques, specifically Autoregressive Integrated Moving Average (ARIMA) analysis. Inflation is a critical economic indicator that directly influences monetary policy, investment decisions, and overall economic stability. Given the dynamic of inflation in emerging economies such as Kenya, a fine understanding of its patterns and the ability to make accurate forecasts are imperative for policymakers, businesses, and investors. The ARIMA (2,2,2) model was employed to capture the underlying trend and seasonality in the inflation data, providing insights into the historical behavior of inflation in Kenya. In this study, we used R programming software and STATA to analyze and generate meaningful information from the data. The data was obtained from World Bank for a period from 1960 to 2022.

Analysis of production projections and factors that correlated with rice production in Indonesia

Introduction: Meeting rice needs is largely determined by the level of domestic rice production. However, the various dynamics that occur provide encouragement to provide an overview of the conditions for meeting food needs, namely in the form of rice, based on trends in rice production and how independent variables correlate with rice production. The aim of this research is to determine the projected rice production for 2023-2032 and determine the strength of the relationship between the dependent variable (rice production) and the independent variables in the form of harvested area, price of harvested dry grain, price of urea fertilizer and price. Methods: namely a quantitative descriptive method with secondary time series data from 1993-2022. Data analysis techniques for forecasting studies are by using Autoregressive Integrated Moving Average (ARIMA) analysis. Meanwhile, in the correlation analysis of rice production using the Pearson correlation. Results: Projections for rice commodity production in Indonesia from 2023 to 2032 using the MA1 model show a positive trend. Analysis of the relationship between the independent variable and the dependent variable, namely harvest area (X1), rice price (X2), urea fertilizer price (X3), and price of other food commodities, namely corn (X4) shows a significant positive relationship with rice production in Indonesia. Conclusion: Projections for rice commodity production in Indonesia from 2023 to 2032, which were analyzed using the MA 1 model, obtained data with a positive trend which states that based on past data, Indonesian rice production will continue to increase every year in line with domestic food needs.

An investigation of income inequality through autoregressive integrated moving average and regression analysis

Income inequality is a prominent contributor to health disparities in the U.S. As a leading capitalist nation, the U.S. registers the highest healthcare expenditure among developed countries yet grapples with widening income disparities. The chasm between the rich and the underprivileged has expanded significantly in recent decades, profoundly impacting American society. This study explores the nuances of income inequality, its ramifications, and potential remedies, analyzed through the Gini Coefficient. Advanced forecasting models, including AutoRegressive Integrated Moving Average and Regression Analysis, are employed to anticipate future patterns. The research highlights the value of healthcare analytics in understanding the complexities of income inequality. The findings underscore the pressing need for effective policies to address this mounting challenge.

Effect of Futures Trading Restrictions on Market Efficiency: A Multifractal Analysis

Based on the China Securities Index 300 (CSI 300 index) futures trading restrictions in 2015, this paper uses the multifractal detrending moving-average cross-correlation analysis method (MF-X-DMA) to investigate the effect of introducing futures trading restrictions on the market efficiency of CSI 300 index spot and futures markets. We begin by using multifractal detrending moving-average analysis (MF-DMA) and find that the futures trading restrictions improve spot market efficiency but decrease futures market efficiency. Moreover, we examine the cross-correlation between spot and futures markets and the information transmission process. MF-X-DMA analysis shows an increase in the level of persistent cross-correlation between spot and futures markets, and a decrease in the multifractality degree of cross-correlation, suggesting that the relationship between spot and futures markets becomes stronger and less complicated after the futures trading restrictions. Moreover, the nonlinear Granger causality test shows that futures returns do not Granger cause spot returns after the restrictions. Therefore, the futures trading restrictions may mitigate the harmful effect of speculative trading in the futures market and thus improve spot market efficiency.

Seismic velocity field estimation using sonic well logs decomposed by Singular Spectrum Analysis (SSA) and Exponentially Weighted Moving Average (EWMA) - A case of study in Recôncavo Basin, Brazil

The determination of an accurate seismic wavefield velocity propagating in the subsurface is a problem faced currently by geoscientists. However, velocity analysis on land seismic data is often compromised due to lack of a good signal-to-noise ratio, absence of guide reflectors, and low fold, which significantly affects the quality of the final velocity model and, consequently, the seismic image. This paper introduces an improvement in the root mean square (RMS) velocity model obtained from the Polo-Miranga seismic cube, located in the Recôncavo sedimentary basin in northeast of Brazil, using slowness well log data. The available vagarosity logs were smoothed using the Exponentially Weighted Moving Average (EWMA) and Singular Spectrum Analysis (SSA) to determine a new measurement that reflects the low frequency of the p-wave seismic velocity to be employed in the velocity analysis. Estimating the best parameter of the EWMA algorithm required some tests to smooth the slowness. At the same time, the first component of the sonic decomposition into three other curves was a reasonable way to estimate the velocity. Those techniques showed robust results and enhanced some reflectors in the 3D stack. Furthermore, the proposed methodology’s performance is validated through the normal moveout correction (NMO) simulation and on the time-slice image.

Prediksi Nilai Inflasi Post Covid 19 di Indonesia

The COVID-19 pandemic has positively impacted inflation in Indonesia, where inflation decreased to 1.33 in June 2021. It would be better if Indonesia could maintain this figure, but in July 2021, the inflation rate began to rise again; even in April 2022, the inflation rate reached 3.47. This study will predict the inflation value after the covid-19 pandemic using Autoregressive Moving Average (ARIMA) analysis. Using monthly time-series data, we study inflation in Indonesia during the period 2010 – 2022, then predict the inflation value in the following months using Autoregressive Moving Average (ARIMA) analysis. The data is stationary at the first difference. Several possible ARIMA models analyzed show that the ARIMA (1,1,1) model was chosen as the best model. The results of this study indicate that monthly inflation in Indonesia is likely to continue to rise. The government is expected to make appropriate policies to reduce inflation based on these results.

Forecasting A Weekly Red Chilli Price in Bengkulu City Using Autoregressive Integrated Moving Average (ARIMA) and Singular Spectrum Analysis (SSA) Methods

Red chili occupies a strategic position in the Indonesian economic structure because its use applies to almost all Indonesian dishes. Therefore, controlling the price of red chili is anecessity to maintain national economic stability. The purpose of this research is to forecast a red chili weekly price using ARIMA and SSA based on the weekly data of chili prices from January 2016 - December 2019 sourced from Statistics Indonseia (BPS) Branch Office of Bengkulu Province. The data have been analyzed using software R. Based on MAPE, ARIMA (2,1,2) provides the best forecasting with value 0.49% while SSA 10.64%.

Autoregressive Moving Average Jointly-Diagonalizable Spatial Covariance Analysis for Joint Source Separation and Dereverberation

This paper describes a computationally-efficient statistical approach to joint (semi-)blind source separation and dereverberation for multichannel noisy reverberant mixture signals. A standard approach to source separation is to formulate a generative model of a multichannel mixture spectrogram that consists of source and spatial models representing the time-frequency power spectral densities (PSDs) and spatial covariance matrices (SCMs) of source images, respectively, and find the maximum-likelihood estimates of these parameters. A state-of-the-art blind source separation method in this thread of research is fast multichannel nonnegative matrix factorization (FastMNMF) based on the low-rank PSDs and jointly-diagonalizable full-rank SCMs. To perform mutually-dependent separation and dereverberation jointly, in this paper we integrate both moving average (MA) and autoregressive (AR) models that represent the early reflections and late reverberations of sources, respectively, into the FastMNMF formalism. Using a pretrained deep generative model of speech PSDs as a source model, we realize semi-blind joint speech separation and dereverberation. We derive an iterative optimization algorithm based on iterative projection or iterative source steering for jointly and efficiently updating the AR parameters and the SCMs. Our experimental results showed the superiority of the proposed ARMA extension over its AR- or MA-ablated version in a speech separation and/or dereverberation task.

Can police shootings be reduced by requiring officers to document when they point firearms at citizens?

ObjectiveTo examine the impact of a novel firearm ‘pointing’ policy that requires officers to document when they directly point their guns at citizens.MethodsSixteen years (2003–2018) of narrative officer-involved shooting (OIS)...

Detection of oriented fractal scaling components in anisotropic two-dimensional trajectories

We propose a novel class of mixed fluctuations with different orientations and fractal scaling features as a model for anisotropic two-dimensional (2D) trajectories hypothesized to appear in complex systems. Furthermore, we develop the oriented fractal scaling component analysis (OFSCA) to decompose such mixed fluctuations into the original orientation components. In the OFSCA, the original orientations are detected based on the principle that the original angles are orthogonal to the angles with the minimum and maximum scaling exponents of the mixed fluctuations. In our approach, the angle-dependent scaling properties are estimated using the Savitzky–Golay-filter-based detrended moving-average analysis (DMA), which has a higher detrending order than the conventional moving-average-filter-based DMA. To illustrate the OFSCA, we demonstrate that the numerically generated time-series of mixed fractional Gaussian noise (fGn) processes with non-orthogonal orientations and different scaling exponents is successfully decomposed into the original fGn components. We demonstrate the existence of oriented components in the 2D trajectories by applying OFSCA to real-world time-series, such as human postural fluctuations during standing and seismic ground acceleration during the great 2011 Tohoku-oki earthquake.

Analisis Temporal Suhu Permukaan Laut di Perairan Indonesia Selama 32 Tahun (Era AVHRR)

One of the important factor that influences global climate dynamics is sea surface temperature (SST). Indonesian waters are semi-closed and located between the Pacific and Indian Oceans so that they have different characteristics of SPL in each region. The purpose of this research is to know the trend and local characteristics of Indonesian SST and adjacent areas in response to 6-monthly and seasonal variability with moving average method and correlation. The data used are SST data from AVHRR satellite with domain 15°N-15°S, 90°-145° E. The results showed of increase trend 0.34°C in Indonesian sea for 32 years (1981-2012). The characteristics of SST in Indonesian territory are closely related to the Mosoon cycle where in the East period in the northern region the Natuna Sea is warmer in contrast to the West monsoon period, in the southern part of the Arafura Sea to the warmer Sawu Sea and around the equator experiencing the warmest in the transitional period. The moving-average analysis shows that 6-monthly variability appears to be dominated in equatorial waters including the Java Sea and Banda Sea whereas seasonal variability occurs in the northern and southern regions of Indonesia's waters including the Pacific Ocean Oceans, the northwest Pacific Ocean and the southeast Indian Ocean. Based on correlation analysis, ENSO has a strong (negative) relationship in eastern Indonesia waters including the Sea in the northern part of Papua and the northwest Pacific Ocean while IOD has a strong (negative) relationship in the western Sumatra Sea, Banda Sea and Arafura Sea.

A Novel Approach to Study the Variability of NGC 5548

Abstract Understanding the properties of the continuum radiation and broad emission lines of active galactic nuclei provides significant information not only to model the radiation mechanism and constrain the geometry and kinematics of the broad-line region (BLR) but also to probe the central engine of the sources. Here we investigate the multifractal behavior of the Hβ emission line and the 5100 Å continuum flux light curves of NGC 5548. The aim is to search for multiscaling signatures in the light curves and check if there is a possible nonlinear relationship between them. To this end, we use a multifractality analysis technique called the Multifractal Detrended Moving Average analysis. We detect multifractal (nonlinear) signatures in the full monitoring and densely sampled period of the Hβ line and 5100 Å continuum light curves of NGC 5548, possibly indicating the presence of complex and nonlinear interaction in the 5100 Å continuum and Hβ emission line regions. Moreover, the degree of multifractality of the Hβ line is found to be about twice that of the 5100 Å continuum. The nonlinearity of both emissions could be generated when the BLR reprocesses the radiation from the central compact source. Finally, we found that antipersistent long-range temporal correlation is the main source of the multifractality detected in both light curves.

Detrended cross-correlation patterns between North Atlantic oscillation and precipitation

In this study, the long-range relationships between North Atlantic Oscillation (NAO) and precipitation data obtained from Climate Prediction Centre (CPC) Merged Analysis of Precipitation (CMAP) from 1979 to 2016 are investigated using Detrended Fluctuation Moving Average Cross-Correlation Analysis (DMCA). In the atmosphere, teleconnections through strong convective processes sporadically affect various climatic regimes in Europe, Mediterranean basin, North Africa, Middle East, and Caucasus. The NAO is one of the teleconnection processes and results in heavy rainfall in the Mediterranean basin during its negative phase while it gives rise to rain in Europe during its positive phase. The DMCA technique shows that the NAO fluctuation series exhibit different long-range cross-correlation coefficients, ρDMCA(s) with “s” being the moving average time window length, between the precipitation values and NAO. Large ρDMCA(s) coefficients with time window(s) larger than 12 months were obtained particularly over the Mediterranean basin, near the North Pole including northern Europe. Furthermore, the ρDMCA(s) coefficients were grouped into clusters using K-mean method to distinguish the similar patterns. The 1st cluster refers to the negative phase of the NAO indicating warm-rainy conditions and dry spells, which is especially evident in the Mediterranean basin. The 2nd cluster represents the long-range cross-correlation with respect to the positive phase of NAO and precipitation values, particularly for the Western and Northern Europe. Conversely, the 3rd cluster is evaluated as power law of long-range cross-correlations between the precipitation and NAO with respect to the different time scale processes.

Long-range temporal correlation in Auditory Brainstem Responses to Spoken Syllable/da/

The speech auditory brainstem response (sABR) is an objective clinical tool to diagnose particular impairments along the auditory brainstem pathways. We explore the scaling behavior of the brainstem in response to synthetic /da/ stimuli using a proposed pipeline including Multifractal Detrended Moving Average Analysis (MFDMA) modified by Singular Value Decomposition. The scaling exponent confirms that all normal sABR are classified into the non-stationary process. The average Hurst exponent is H = 0:77 ± 0:12 at 68% confidence interval indicating long-range correlation which shows the first universality behavior of sABR. Our findings exhibit that fluctuations in the sABR series are dictated by a mechanism associated with long-term memory of the dynamic of the auditory system in the brainstem level. The q-dependency of h(q) demonstrates that underlying data sets have multifractal nature revealing the second universality behavior of the normal sABR samples. Comparing Hurst exponent of original sABR with the results of the corresponding shuffled and surrogate series, we conclude that its multifractality is almost due to the long-range temporal correlations which are devoted to the third universality. Finally, the presence of long-range correlation which is related to the slow timescales in the subcortical level and integration of information in the brainstem network is confirmed.

Identification of defective two dimensional semiconductors by multifractal analysis: The single-layer [formula omitted] case study

Two dimensional semiconductor such as single-layer transition metal dichalcogenides (SL-TMD) have attracted most attentions as an atomically thin layer semiconductor materials. Typically, lattice point defects (sulfur vacancy) created by physical/chemical method during growth stages, have disadvantages on electronic properties. However, photoluminescence (PL) spectroscopy is conventionally used to characterize single-layer films but until now it has not been used to show the presence of defects or estimate their population due to overall similarity of general feature PL spectra. To find a feasible and robust method to determine the presence of point defects on single layer MoS2 without changing the experimental setup, Multifractal Detrended Fluctuation Analysis (MF-DFA) and Multifractal Detrended Moving Average Analysis (MF-DMA) are applied on the PL spectrum of single layer MoS2. We compare the scaling behavior of PL spectrum of pristine and defective single layer MoS2 determined by MF-DFA and MF-DMA. Our results reveal that PL spectrum has multifractal nature and different various population of point defects (sulfur vacancy) on single layer MoS2 change dramatically multifractality characteristics (Hurst, Hölder exponents) of photoluminescence spectrum. It is exhibited creating more lattice point leads to smaller fluctuations in luminescent light that it can help to design special defect structure for light emitted devices. The relative populations of point defects are almost elucidated without utilizing expensive characterization instruments such as scanning tunneling microscopy (STM) and high resolution transmission electron microscopy (HR-TEM).

(Q)SPR Models for Prediction of Hydrophobicity of Isatins

In the present study, the application of a wide variety of topological descriptors was investigated for predicting hydrophobicity (clogP) of isatin analogues. A total of four topochemical indices selected through decision tree (DT) were used for the development of single index based models using moving average analysis (MAA). The overall accuracy of prediction varied from a minimum of 95% to a maximum of 98% with regard to hydrophobicity.The values of sensitivity, specificity and Mathew's correlation coefficient for all MAA based models with regard to hydrophobicity (clogP) was found to be =78%, =94% and =0.85 respectively, suggesting robustness of proposed models. Since the compounds with high clogP values were found effective in carboxylesterases (CEs) inhibition, therefore, highly hydrophobic ranges of proposed MAA models can easily be exploited for the design and development of potent CEs inhibitors.

Time Series Analysis Methods and Applications for Flight Data. By Jianye Zhang and Peng Zhang. Springer: Berlin, Heidelberg, Germany, 2017; pp. 1–240; ISBN: 978-3-662-53430-4

This book [1] aims to present the best application for managing and clearly representing the massive amount of Flight Data (FD) that exists. [...]

Path Pendeccentric Connectivity Indices: Detour Matrix Based Molecular Descriptors for QSAR/QSPR Studies, Part 2

A pivotal role of tyrosine threonine kinase (TTK) has been established in tumor initiation, survival of genomically unstable and aneuploid cancer cells. In present study, path pendeccentric connectivity indices reported in part 1 of the manuscript were successfully applied for developing models for predicting TTK inhibitory activity of acetamide/carboxamide analogs. Diverse 2D and 3D molecular descriptors (MDs) were successfully utilized for developing models using artificial neural networks (ANN) and moving average analysis (MAA). The overall accuracy of prediction achieved for ANN and MAA based models was up to 96% for the training set and up to 92% during cross validation. The statistical utility of the said models was also evaluated through Matthews correlation coefficient, non error rate, sensitivity and intercorrelation analysis. Low IC50 values obtained for active ranges of the proposed MAA based models indicate the tremendous potential of said models for furnishing lead molecules for developing potent TTK inhibiting acetamide/carboxamide analogs.

Republication: Application of financial analysis techniques to vital sign data – A novel method of trend interpretation in the Intensive Care Unit

Modern critical care medicine is based on flow of information. This information requires significant amount of interpretation to become clinically valuable. Despite the wealth of data in the modern Intensive Care Units (ICUs), intensivists often rely on fragmentary information. In an attempt to improve data trending utilization, application of financial analysis (FA) methods to vital sign data samples was examined. Two randomly chosen vital sign datasets of patients who spent at least 30 days in the ICU were retrospectively reviewed. Hourly vital sign data were retrieved and recorded for each patient. Variables tracked included systolic blood pressure for patient #1 and heart rate for patient #2. These variables were then entered into specialized FA software and subjected to computer-based processing. Trends in the recorded data were examined using (1) the stochastic oscillator (SO), (2) the moving average (MAV) convergence-divergence tool, (3) price envelope analysis and (4) MAV analysis. Both blood pressure and heart rate analyses demonstrated that vital sign data could be successfully trended using FA techniques. Not only was the vital sign data easy to read and interpret when formatted in financial-like fashion, but some trends that were not apparent on gross inspection of the numeric data were clearly demonstrated on FA. Much like with financial patterns, trends noted within vital sign data appeared to be more significant when more than one indicator identified them, utilizing the concept of a confirmatory variable. Vital sign data, much like financial data, were subject to trend reversals. Such reversals in vital sign data appeared to follow rules similar to those followed by financial vehicles and markets. This report demonstrates that vital sign data can be subjected to the same manipulations as financial market data. Furthermore, FA tools appear to provide the interpreter of the data with means to define, confirm, and possibly predict trends and trend reversals.The following core competencies are addressed in this article: Medical knowledge, Practice-based learning and improvement, Systems-based practice.Republished with permission from: Stawicki SP. Application of financial analysis techniques to vital sign data – A novel method of trend interpretation in the Intensive Care Unit. OPUS 12 Scientist 2007;1(1):14-16.