Time Series Techniques Research Articles

Assessing changes in clusters of wildlife road mortalities after the construction of wildlife mitigation structures.

Collisions with vehicles can be a major threat to wildlife populations, so wildlife mitigation structures, including exclusionary fencing and wildlife crossings, are often constructed. To assess mitigation structure effectiveness, it is useful to compare wildlife road mortalities (WRMs) before, during, and after mitigation structure construction; however, differences in survey methodologies may make comparisons of counts impractical. Location‐based cluster analyses provide a means to assess how WRM spatial patterns have changed over time. We collected WRM data between 2015 and 2019 on State Highway 100 in Texas, USA. Five wildlife crossings and exclusionary fencing were installed in this area between September 2016 and May 2018 for the endangered ocelot (Leopardus pardalis) and other similarly sized mammals. Roads intersecting State Highway 100 were mitigated by gates, wildlife guards, and wing walls. However, these structures may have provided wildlife access to the highway. We combined local hot spot analysis and time series analysis to assess how WRM cluster intensity changed after mitigation structure construction at fine spatial and temporal scales and generalized linear regression to assess how gaps in fencing and land cover were related to WRM cluster intensity in the before, during, and after construction periods. Overall, WRMs/survey day decreased after mitigation structure construction and most hot spots occurred where there were more fence gaps, and, while cluster intensity increased in a few locations, these were not at fence gaps. Cluster intensity of WRMs increased when nearer to fence gaps in naturally vegetated areas, especially forested areas, and decreased nearer to fence gaps in areas with less natural vegetation. We recommend that if fence gaps are necessary in forested areas, less permeable mitigation structures, such as gates, should be used. Local hot spot analysis, coupled with time series and regression techniques, can effectively assess how WRM clustering changes over time.

Climate Change Dynamics versus Human Biodiversity Responses in a Rainfall Upland Area

The study has explored statistical and time series techniques that are used in the analysis of the data on conditional and specified activities of the atmosphere, ambient and sensible activities in Ado Ekiti, South West Nigeria. The plots of the atmospheric-ambient temperatures against sequential time series reveals an uneven pattern in temperature exponents or changes over the period covered, with behavioral trends with high relative wind speed resulting in most precipitations. It is visualized from the results that the ambient indices are with deviance of about compared to atmospheric data. Observations also shown from our comparisons that with atmospheric temperatures at about , saturated cloud resulting in precipitations were visible. Results show that, minus the tropical average atmospheric temperature which is , maximum temperature determining features for precipitations are about and in May and June respectively, prevalent for specified days under study. Rarely on clear air days with about , possible precipitation was stratified or widespread when maximum temperature reaching about and justifying human biodiversity activities in the area of study.

Performance evaluation of forecasting models based on time series and machine learning techniques: an application to light fuel consumption in Brazil

PurposeFuel demand forecast is a fundamental tool to guide private planning actions and public policies aim to guarantee energy supply. This paper aims to evaluate different forecasting methods to project the consumption of light fuels in Brazil (fuel used by vehicles with internal combustion engine).Design/methodology/approachEight different methods were implemented, besides of ensemble learning technics that combine the different models. The evaluation was carried out based on the forecast error for a forecast horizon of 3, 6 and 12 months.FindingsThe statistical tests performed indicated the superiority of the evaluated models compared to a naive forecasting method. As the forecast horizon increase, the heterogeneity between the accuracy of the models becomes evident and the classification by performance becomes easier. Furthermore, for 12 months forecast, it was found methods that outperform, with statistical significance, the SARIMA method, that is widely used. Even with an unprecedented event, such as the COVID-19 crisis, the results proved to be robust.Practical implicationsSome regulation instruments in Brazilian fuel market requires the forecast of light fuel consumption to better deal with supply and environment issues. In that context, the level of accuracy reached allows the use of these models as tools to assist public and private agents that operate in this market.Originality/valueThe study seeks to fill a gap in the literature on the Brazilian light fuel market. In addition, the methodological strategy adopted assesses projection models from different areas of knowledge using a robust evaluation procedure.

Seasonal and Secular Periodicities Identified in the Dynamics of US FDA Medical Devices (1976-2020): Portends Intrinsic Industrial Transformationand Independence of Certain Crises.

The US Food and Drug Administration (FDA) regulates medical devices (MD), which are predicated on a concoction of economic and policy forces (e.g., supply/demand, crises, patents), under primarily two administrative circuits: premarketing notifications (PMN) and Approvals (PMAs). This work considers the dynamics of FDA PMNs and PMAs applications as an proxy metric for the evolution of the MD industry, and specifically seeks to test the existence [and, if so, identify the length scale(s)] of economic/business cycles. Beyond summary statistics, the monthly (May, 1976 to December, 2020) number of observed FDA MD Applications are investigated via an assortment of time series techniques (including: discrete wavelet transform, running moving average filter, complete ensemble empirical mode with adaptive noise decomposition, and Seasonal Trend Loess decomposition) to exhaustively seek and find such periodicities. This work finds that from 1976 to 2020, the dynamics of MD applications are (1) non-normal, non-stationary (fractional order of integration < 1), non-linear, and strongly persistent (Hurst > 0.5); (2) regular (non-variance), with latent periodicities following seasonal, 1-year (short-term), 5–6 year (Juglar; mid-term), and a single 24-year (Kuznets; medium-term) period (when considering the total number of MD applications); (3) evolving independently of any specific exogenous factor (such as the COVID-19 crisis); (4) comprised of two inversely opposing processes (PMNs and PMAs) suggesting an intrinsic structural industrial transformation occurring within the MD industry; and, (6) predicted to continue its decline (as a totality) into the mid-2020s until recovery. Ramifications of these findings are discussed.Supplementary InformationThe online version contains supplementary material available at 10.1007/s43441-021-00334-4.

The Justices’ Words: The Relationship between Majority and Separate Opinions

Majority and separate opinions reflect the justices’ deliberations and strategic decision-making. As justices try to shape the legal outcome, private disagreements during the opinion-writing process spill out into the open, becoming the written words of majority and separate opinions. In this article, I ask how justices use separate opinions to shape the law. I argue that the length of an opinion provides a reasonable proxy of the law and the Court’s decision-making at work. Using time series techniques on the number of words in majority and separate opinions from 1953–2009, I examine whether there is a relationship between the number of words in majority and separate opinions. I demonstrate there is a fractional cointegration relationship between majority and separate opinion length. The majority and separate opinion relationship means there will not be a time in which the Court produces incredibly long separate opinions and succinct majority opinions, or lengthy majority opinions and brief separate opinions. I also find that separate opinion length causes the majority opinion to be shorter or longer. Error correction model results indicate that discussions that occur in one term do not conclude when the Court’s term ends, the effects continue in subsequent terms and cases. The law, as the Court generates it in its majority opinions, is shaped by separate opinions.

The Impact of Foreign Aid on Economic Growth: Context of Bangladesh

This article looks at the impact of foreign aid (foreign loans and grants) on economic growth in developing countries like Bangladesh. Using a time series analysis to perform an empirical inquiry for Bangladesh between 1995 and 2016. Several time series techniques are used in this work, including the Johansen–Juseliues test and the Granger causality test. In the short run, foreign loans have a greater impact on economic growth (per capita GDP), whereas foreign grants have no effect. There is also a one-way correlation between foreign loans and domestic investment. In the coming days, we recommend that the Bangladesh government be cautious and smart in preserving bilateral and multilateral aid relationships with donor organizations and countries.

On the Connection between the GEP Performances and the Time Series Properties

Artificial intelligence (AI) methods are interesting alternatives to classical approaches for modeling financial time series since they relax the assumptions imposed on the data generating process by the parametric models and do not impose any constraint on the model’s functional form. Even if many studies employed these techniques for modeling financial time series, the connection of the models’ performances with the statistical characteristics of the data series has not yet been investigated. Therefore, this research aims to study the performances of Gene Expression Programming (GEP) for modeling monthly and weekly financial series that present trend and/or seasonality and after the removal of each component. It is shown that series normality and homoskedasticity do not influence the models’ quality. The trend removal increases the models’ performance, whereas the seasonality elimination results in diminishing the goodness of fit. Comparisons with ARIMA models built are also provided.

Challenges Of Exchange Rate Fluctuation And Cpo Prices In Indonesia Palm Oil Industry

Objective : This study points to analyze the challenges of Rupiah Exchange Rates and International CPO Prices to Indonesia's CPO Export Volume.&#x0D; Design /methodology/approach : this research exercises explanatory type under a quantitative approach. Data collection techniques in this study were done by time series techniques. The data analysis technique used is Structural Equation Model (SEM) with the Smart Partial Least Square (PLS) tool.&#x0D; Findings : The study results indicate that: (1) Rupiah Exchange Rates do not have a significant effect on Indonesia's CPO Export Volume; (2) International CPO prices have a significant effect on Indonesia's CPO Export Volume.&#x0D; Practical implications : intensify business in economy sector in order to reach the lower class people.&#x0D; Paper type : research paper&#x0D; &#x0D; Keywords: Rupiah Exchange Rates, International CPO Prices, Export Volume

An empirical survey of data augmentation for time series classification with neural networks.

In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.

Application of Fuzzy Time Series (FTS) Algorithm in Production Planning of Indonesia’s Oil Refining Company

Forecasting and aggregate planning are crucial phases in production planning especially for oil refining company that takes expensive production cost. Accurate forecasting greatly influences the success of production planning since it is the starting point of production planning. Whereas aggregate planning becomes important because it functions to bridge between the demand or production target with the existing resource requirements. Seeing the importance of accurate forecasting and aggregate planning, this research emphasizes the use of Fuzzy Time Series (FTS) Algorithm to forecast Premium sales in Indonesia’s oil refining company. The comparison is also done between FTS with the other classical techniques in time series forecasting to test the reliability of algorithm and FTS outperforms the others based on the lowest MAPE value as much as 0.87%. FTS result is then used as an input in the aggregate planning by using heuristics method and comparing 3 strategies which are Level Strategy, Chase Strategy, and Hybrid Strategy. The result shows that Hybrid Strategy is the most efficient one because it produces the lowest production cost for three months production period as much as Rp 3,272,000,000.

A Review on Anomaly Detection in Time Series

Time series is a very common class of data sets. Among others, it is very simple to obtain time series data from a variety of various science and finance applications and an anomaly detection technique for time series is becoming a very prominent research topic nowadays. Anomaly identification covers intrusion detection, detection of theft, mistake detection, machine health monitoring, network sensor event detection or habitat disturbance. It is also used for removing suspicious data from the data set before production. This review aims to provide a detailed and organized overview of the Anomaly detection investigation. In this article we will first define what an anomaly in time series is, and then describe quickly some of the methods suggested in the past two or three years for detection of anomaly in time series

A multi-stage data mining approach for liquid bulk cargo volume analysis based on bill of lading data

Liquid bulk cargo (LBC) volume analysis has received considerably great attention recently since LBC is a valuable and high-demand cargo. Thus, it is important to establish an analysis system for LBC volume, as it can help inform strategies for port planning and management. Nevertheless, LBC volume analysis is a challenging task for researchers because trends in LBC volume are highly volatile and non-stationary. In this paper, a new framework for enabling informative LBC volume analysis based on bill of lading (BL) data is proposed, which consists of three parts: item segmentation, exploratory volume analysis, and volume prediction. Firstly, an innovative item segmentation system using item texts of BL data was developed, which can generate subcategory as well as category information of LBC items that existing system cannot provide. Next, exploratory volume analysis was performed to understand the volume characteristics of each categorized and subcategorized item in terms of geography and timeline. Lastly, manifold learning- and deep learning-based time series techniques were proposed to increase LBC volume prediction accuracy compared with existing statistical models. The experimental results for volume prediction show the accuracy increased by 34% and 18% in average at category and subcategory levels over baseline models. It is believed that our proposed method will be helpful for stakeholders in maritime logistics, giving them the insights that they need to make better decisions.

Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study

The volume of daily patient arrivals at Emergency Departments (EDs) is unpredictable and is a significant reason of ED crowding in hospitals worldwide. Timely forecast of patients arriving at ED can help the hospital management in early planning and avoiding of overcrowding. Many different ED patient arrivals forecasting models have previously been proposed by using time series analysis methods. Even though the time series methods such as Linear and Logistic Regression, Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Exponential Smoothing (ES), and Artificial Neural Network (ANN) have been explored extensively for the ED forecasting model development, the few significant limitations of these methods associated in the analysis of time series data make the models inadequate in many practical situations. Therefore, in this paper, Machine Learning (ML)-based Random Forest (RF) regressor, and Deep Neural Network (DNN)-based Long Short-Term Memory (LSTM) and Convolutional Neural network (CNN) methods, which have not been explored to the same extent as the other time series techniques, are implemented by incorporating meteorological and calendar parameters for the development of forecasting models. The performances of the developed three models in forecasting ED patient arrivals are evaluated. Among the three models, CNN outperformed for short-term (3 days in advance) patient arrivals prediction with Mean Absolute Percentage Error (MAPE) of 9.24% and LSTM performed better for moderate-term (7 days in advance) patient arrivals prediction with MAPE of 8.91% using weather forecast information. Whereas, LSTM model outperformed with MAPE of 8.04% compared to 9.53% by CNN and 10.10% by RF model for current day prediction of patient arrivals using 3 days past weather information. Thus, for short-term ED patient arrivals forecasting, DNN-based model performed better compared to RF regressor ML-based model.

Data Analysis and Forecasting of the COVID-19 Spread: A Comparison of Recurrent Neural Networks and Time Series Models

To understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google’s Mobility Reports and climate variables acquired from the Weather Online API. The results suggest that the logistic growth model fits best the pandemic’s behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables.

Challenges Of Exchange Rate Fluctuation And Cpo Prices In Indonesia Palm Oil Industry

Objective : This study points to analyze the challenges of Rupiah Exchange Rates and International CPO Prices to Indonesia's CPO Export Volume.&#x0D; Design /methodology/approach : this research exercises explanatory type under a quantitative approach. Data collection techniques in this study were done by time series techniques. The data analysis technique used is Structural Equation Model (SEM) with the Smart Partial Least Square (PLS) tool.&#x0D; Findings : The study results indicate that: (1) Rupiah Exchange Rates do not have a significant effect on Indonesia's CPO Export Volume; (2) International CPO prices have a significant effect on Indonesia's CPO Export Volume.&#x0D; Practical implications : intensify business in economy sector in order to reach the lower class people.&#x0D; Paper type : research paper&#x0D; &#x0D; Keywords: Rupiah Exchange Rates, International CPO Prices, Export Volume

Variable step-size evolving participatory learning with kernel recursive least squares applied to gas prices forecasting in Brazil.

A prediction model is an indispensable tool in business, helping to make decisions, whether in the short, medium, or long term. In this context, the implementation of machine learning techniques in time series forecasting models has a notorious relevance, as information processing and efficient and dynamic knowledge uncovering are increasingly demanded. This paper develops a model called Variable step-size evolving Participatory Learning with Kernel Recursive Least Squares, VS-ePL-KRLS, applied to the forecast of weekly prices for S500 and S10 diesel oil, at the Brazilian level, for biweekly and monthly horizons. The presented model demonstrates a better accuracy compared with analogous models in the literature, without loss of computational performance for all time series analyzed.

An impact of air pollution on moderate to severe relapses among multiple sclerosis patients

BackgroundMultiple Sclerosis (MS) is a chronic inflammatory disease of the central nervous system with both a genetic and environmental component. ObjectiveIn the current study, we examined an association between incidence of MS moderate to severe relapses and exposure to air pollutants and meteorological exposures. MethodsWe enrolled MS patients in Southern Israel during 2000-2017. Exposure assessment relied on satellite-based model of exposure to particulate matter of size <2.5 and 10 microns (PM2.5, PM10) and temperature at a spatial resolution of 1 km (Kloog et al., 2015). The information on exposure to nitrogen dioxide (NO2), sulfur dioxide (SO2) and ozone (O3) levels was completed from the database of the monitoring stations. We analyzed the data using a semi-ecological approach. The monthly incidence of MS-related relapses requiring hospitalization as a function of environmental factors was analyzed by time-series technique, adjusting to sex, age and smoking. We also used a case-crossover approach to compare environmental exposure of a patient on the day of the relapse with the exposure on the relapse-free days. All estimates were adjusted to the heat index and were divided by IQR. ResultsThere were 287 MS patients in the study, with an average age of 52.8 ± 16.7 years, 37% of them (107) being under 40. Mostly female (66.2%), and 13.6% of the patients smoking (47% non-smoking and 39.4% unknown). PM2.5 was independently associated with MS relapses within the non-smoking population [Relative Risk (RR)=1.28, 95%CI:1.01-1.62]. O3 was found adversely associated with MS relapses among patients younger than 40 [RR=1.58, 95%CI 1.03-4.43]. Based on the case-crossover approach, relapses were associated with elevated levels of PM10 and NO2 in all subjects [Odds Ratio (OR)=1.05, 95%CI:1.00-1.11; OR=1.85, 95%CI: 1.28-2.68, respectively]. An adverse association with PM2.5 was observed in non-smokers [OR 1.12, 95%CI 1.00-1.25]. ConclusionsThe findings show that MS relapses are adversely associated with an ambient exposure to PM and NO2.

Economic Growth, Inflation and Monetary Policy in Pakistan: Preliminary Empirical Estimates

In economic policy, construction Inflation is a core variable to be considered that determines the economic activity. To make a suitable monetary policy, it is very essential to check the price level and later on, many other variables are considered to achieve the goal. This study aims to reveal the affiliation of inflation on the growth of economic activities in Pakistan. Time series data set for the period 1989-2020 was used to have the empirical estimates. Augmented Dickey Fuller Unit Root Test is employed to check the unit root of the time series and Auto Regressive Distributive Lag techniques are used for empirical estimates. The present research uses Inflation as a dependent variable and Gross Domestic Product, Interest Rate, Money Supply, and Exchange Rate as the explanatory variables of the study. The findings of this analysis reveal that there's an antagonistic relation between Inflation and GDP.

Second Order Techniques for Learning Time-series with Structural Breaks

We study fundamental problems in learning nonstationary time-series: how to effectively regularize time-series models and how to adaptively tune forgetting rates. The effectiveness of L2 regularization depends on the choice of coordinates, and the variables need to be appropriately normalized. In nonstationary environment, however, what is appropriate can vary over time. Proposed regularization is invariant to the invertible linear transformation of coordinates, eliminating the necessity of normalization. We also propose an ensemble learning approach to adaptively tuning the forgetting rate and regularization-coefficient. We train multiple models with varying hyperparameters and evaluate their performance by the use of multiple hyper forgetting rates. At each step, we choose the best performing model on the basis of the best performing hyper forgetting rate. The effectiveness of the proposed approaches is demonstrated with real time-series.

Machine learning based decline curve analysis for short-term oil production forecast

Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific models to fit production data for production forecasting. Various decline curve models have been applied for unconventional wells, including the Arps model, stretched exponential model, Duong model, and combined capacitance-resistance model. However, it is not straightforward to determine which model should be used, as multiple models may fit a dataset equally well but provide different forecasts, and hastily selecting a model for probabilistic DCA can underestimate the uncertainty in a production forecast. Data science, machine learning, and artificial intelligence are revolutionizing the oil and gas industry by utilizing computing power more effectively and efficiently. We propose a data-driven approach in this paper to performing short term predictions for unconventional oil production. Two states of the art level models have tested: DeepAR and used Prophet time series analysis on petroleum production data. Compared with the traditional approach using decline curve models, the machine learning approach can be regarded as” model-free” (non-parametric) because the pre-determination of decline curve models is not required. The main goal of this work is to develop and apply neural networks and time series techniques to oil well data without having substantial knowledge regarding the extraction process or physical relationship between the geological and dynamic parameters. For evaluation and verification purpose, The proposed method is applied to a selected well of Midland fields from the USA. By comparing our results, we can infer that both DeepAR and Prophet analysis are useful for gaining a better understanding of the behavior of oil wells, and can mitigate over/underestimates resulting from using a single decline curve model for forecasting. In addition, the proposed approach performs well in spreading model uncertainty to uncertainty in production forecasting; that is, we end up with a forecast which outperforms the standard DCA methods.