Application Of Time Series Methods Research Articles

Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies

Abstract The use of digital devices to collect data in mobile health studies introduces a novel application of time series methods, with the constraint of potential data missing at random or missing not at random (MNAR). In time-series analysis, testing for stationarity is an important preliminary step to inform appropriate subsequent analyses. The Dickey–Fuller test evaluates the null hypothesis of unit root non-stationarity, under no missing data. Beyond recommendations under data missing completely at random for complete case analysis or last observation carry forward imputation, researchers have not extended unit root non-stationarity testing to more complex missing data mechanisms. Multiple imputation with chained equations, Kalman smoothing imputation, and linear interpolation have also been used for time-series data, however such methods impose constraints on the autocorrelation structure and impact unit root testing. We propose maximum likelihood estimation and multiple imputation using state space model approaches to adapt the augmented Dickey–Fuller test to a context with missing data. We further develop sensitivity analyses to examine the impact of MNAR data. We evaluate the performance of existing and proposed methods across missing mechanisms in extensive simulations and in their application to a multi-year smartphone study of bipolar patients.

Application of time series methods for dengue cases in North India (Chandigarh)

Time series study is of paramount importance especially in developing countries such as India. The objective of this study is to forecast the number of dengue cases by applying time series models. Furthermore, this study also aims to uncover the correlation of ecological variables with dengue cases using a p value test. The ecological variables considered are the rainfall (with a 2-month lag period) and average relative humidity. An evaluation of host factors such as age and gender is done with the number of cases to determine the most affected strata. Monthly data on dengue cases from 2014 to 2017 are taken from the integrated disease surveillance programme, Government of India, and data on ecological variables, humidity and rainfall are taken from www.Indiastat.com . A 2-month lag period is considered for rainfall. These data were analysed by the autoregressive integrated moving average (ARIMA) and exponential smoothing model (ES). Predictive performance of these techniques was assessed by evaluation parameters such as the Akaike information criterion (AIC), Bayesian information criterion (BIC), mean absolute error and mean absolute scaled error. Applying time series models was useful in the prediction. There were many more males than females according to the data for 2014–2017. The Akaike information criterion (AIC), Bayesian information criterion (BIC) and mean absolute scaled error for ARIMA are 48.6526, 448.333333, 449.30008, 453.08 and 0.507826, respectively. Based on model indices such as the Akaike information criterion (AIC), Bayesian information criterion (BIC), mean absolute error and mean absolute scaled error, the ARIMA performed better than the ES model. Rainfall at the 2-month lag was correlated with the number of cases.

Predicting catch per unit effort from a multispecies commercial fishery in Port Phillip Bay, Australia

Quantitative models that predict stock abundance can inform stock assessments and adaptive management that allows for less stringent controls when abundance is high and environmental conditions are suitable, or tightening controls when abundance is low and environmental conditions are least suitable. Absolute estimates of stock abundance are difficult and expensive to obtain, but data from routine reporting in commercial fisheries logbooks can provide an indicator of stock status. Autoregressive integrated moving average (ARIMA) models were constructed using catch per unit effort (CPUE) from commercial fishing in Port Phillip Bay from 1978–79 to 2009–10. Univariate and multivariate models were compared for short-lived species (Sepioteuthis australis), and species represented by 1–2 year-classes (Sillaginodes punctatus) and 5–6 year-classes (Chrysophrys auratus). Simple transfer models incorporating environmental variables produced the best predictive models for all species. Multivariate ARIMA models are dependent on the availability of an appropriate time series of explanatory variables. This study demonstrates an application of time series methods to predict monthly CPUE that is relevant to fisheries for species that are short lived or vulnerable to fishing during short phases in their life history or where high intra-annual variation in stock abundance occurs through environmental variability.

Application of Time Series Method in Forecasting Near-space Atmospheric Windormalsize

受多种因素影响，临近空间大气环境要素复杂多变，预报难度很大.本文采用时间序列法中的自回归滑动平均（ARMA）模型对临近空间大气风场开展统计预报方法研究，基于廊坊（39.4°N，116.7°W）中频雷达在88km高度的大气纬向风数据开展预报试验.本次预报试验的样本数据为2015年9月24日至10月24日风场数据，利用过去7天数据对未来第8天风场数据进行预报.试验结果显示，ARMA模型对临近空间大气风场预报有一定的适用性.当风场变化规律性较强，即样本数据风场呈现出比较显著的24h周期性变化时，ARMA模型预报效果较好；当风场发生突变时，预报效果变差.与实测数据的对比结果表明，ARMA模型预报结果的误差在9~27m·s^-1，预报效果优于同阶自回归（AR）模型，略优于高阶AR模型.

The minimum wage and the Lithuanian labour market

This paper presents a comprehensive and non-standard labour market analysis based on univariate and multivariate models for wages. The novelty of this paper lies in the use of non-normalized cointegrating vectors for labour market analysis. Wages are the basis of labour market models, as well as the key factor for employees and employers; therefore, the central analytical axis is a classical wage bargaining process, where one side requires and the other side proposes a certain level of wages. Analysis is divided into two parts: foremost, a careful analysis of Lithuanian wages is conducted and a univariate model for the investigation of interactions between the minimum wage and the rest of the wages is proposed; only after the minimum wage model is drafted can the multivarate model for the whole economy be built up. Briefly, the methodology used in this article can be annotated as a synthesis of sequential theoretical and empirical considerations that combine the results of theoretical macroeconomics with data-generating patterns and stylized facts. The model is considered as the final one only if macro-theory preconditions, statistical prerequisites, and stylized real-world requirements are met and fulfilled. In addition, this article gives an example and a quantitatively, as well as qualitatively, motivated suggestion as to how to incorporate minimum wages into econometric models and puts forward an explanation for why it is necessary to include minimum wage dynamics into labour market analysis. The article is nothing but an empirical case study that demonstrates how many minor details have to be taken into account until a realistic labour market model is built up. Although the paper deals with the labour market, the suitable application of time series methods is the main subject of the analysis.

Anchored in 'average thinking' in studies of arid rangeland dynamics – The need for a step forward from traditional measures of variability

There is a wide consensus that arid and semi-arid environments are highly variable in space and time. The most frequent approaches to describe and analyze arid and semi-arid rangelands are based on the usage of averages and Normal distributions as main references to describe spatial and temporal differences. We argue that rangeland ecology science should move forward in methods to better capture the patterns of rangeland dynamics, rather than just focusing on simple measures of variability. We provide different simulated time series data in order to illustrate the limitations of some currently used methodologies. We call for the application of time series methods in order to better understand complex dynamics in arid and semi-arid rangelands.

Time series application for quantification of Alternate Bearing Intensity (ABI) in mango (Mangifera indica) cv Langra

The alternate bearing intensity (ABI) in mango (Mangifera indica L.) cv Langra, a distinct alternate bearer was statistically appraised through application of time series methods by subjecting yield data of one hundred trees growing under uniform cultural conditions at Central Institute for Subtropical Horticulture, Lucknow, Uttar Pradesh, over five years (2007-11). They were considered as 100 time series of short duration and arranged in 100 × 5 matrix for appraisal. The lag-one autocorrelation was calculated from the residual yield data obtained by removing year and tree effects. The quantification of ABI separately also by applying ‘I’ value and time series methods to the same set of data clearly brought out the limitations of ‘I’ value quantifying ABI as a descriptive statistic that sans inference test for significance thus, accounted for less than 60 percent of actuality vis a vis time series, attributable due to eliminating of trends generating a series of non-negative values by ignoring asymmetrical phase changes, an exclusive nature of a crop like mango. In the present analysis, higher precision of time series approach in quantification of ABI is largely by taking into account, the yield residuals and hence was found more efficient as compared to ‘I’ value. The autocorrelation was found negative and highly significant implying, distinctiveness in manifestation of alternate bearing rhythm. This analysis besides confirming strong and distinct alternate bearing tendency of Langra trees, provides a precise approach for quantification of ABI. The results of the present investigation could eventually lead to development of predictive models of yield in cv Langra as a component of decision support to empower growers for optimizing inputs management for sustained Langra mango tree health. In this paper, we present a simple but precise methodology to quantify alternate bearing intensity in Langra through autocorrelations that take into account unexplained components of alternate bearing phenomena.

Forecasting construction cost escalation

Escalation can account for a substantial part of construction costs. Therefore forecasts of the amount of escalation are required for budgetary and bidding purposes. This paper examines methods for forecasting construction escalation using statistical time series methods. Time series of construction cost indices are used as a proxy of construction cost escalation. The application of time series methods, their limitations, and their effect on the risk of cost escalation are demonstrated and evaluated. The analytical methods available are only useful in forecasting for short construction projects in stable conditions. This is because none of the methods can forecast escalation caused by unpredictable occurrences such as outbreak of war or certain government action. Construction cost escalation remains a risk to be borne by either the contractor or the owner, or both, depending on the terms of the contract; any logical approach to minimize the risk is worthwhile. Key words: construction cost escalation, cost indices, time series forecasting, exponential smoothing. Box–Jenkins methods, dynamic regression, Statistics Canada.

Forecasting Fertility: An Application of Time Series Methods To Parameterized Model Schedules

This study employs parameterized model schedules of the age profiles of fertility to summarize age-specific rates with a small number of parameters. Time series models of the parameters capture the temporal patterns of the age profiles, and use this information to project the profiles of future vital rates. The model schedules are estimated by application of nonlinear least squares to fertility rates for women aged 14 through 49, for each calendar year from 1917 through 1988. Time series models of the parameters provide a representation of the historical trends of fertility behavior and are employed to forecast fertility profiles. Forecast evaluation over a 10-year holdout sample demonstrates the viability of the methodology. The time series models are updated through 1988 to generate fertility forecasts to the year 2000. The forecasts, showing an increase in the mean age of childbearing and a 9% rise in the total fertility rate, are compared with projections from the U.S. Bureau of the Census.

An application of time series methods to financial guarantee insurance

This article comprises a summary of a study made in Finland concerning solvency issues in financial guarantee insurance. The time fluctuation of bankruptcy intensity is analyzed by fitting Box-Jenkins type models to empirical data, and this fluctuation is combined with the variation in the number of claims and the individual claim sizes, based on empirical claim size distribution. The estimated models are used to evaluate, for example, the variance of the claims ratio and of the solvency ratio of the financial guarantee insurer. The variation range of the solvency ratio and the appropriate premium level are discussed with numerical examples.

The Application of Time Series Methods to the Analysis of Repeated Surveys

Summary An estimate y, of a population value 0t from a survey carried out at time t can be expressed as y, = 8, + e,, where e, is the error of estimation. By representing 0, as a time series, possibly non-stationary, the problem of estimating 0 +. becomes one of signal extraction in the presence of noise. This paper extends the earlier theoretical results of Scott and Smith (1974) to surveys of complex design and applies the results to examples of both non-overlapping and overlapping surveys. The empirical results suggest that worthwhile gains can be obtained by using time series methods. This paper is particularly directed to the requirement of paragraph 3(a) in the statement of Editorial Policy.