Use Of Time Series Methods Research Articles

Personalized medicine comes to a blood center near you

Personalized medicine comes to a blood center near you

Measuring the Impact of the COVID-19 Pandemic on Employment Using Time Series Generated Synthetic Controls

This paper measures the change in US unemployment and participation rates by gender due to the onslaught of the COVID-19 pandemic. It uses a triple difference estimation, DiDiD, and time series generated synthetic controls to compare these rates. The results suggest, thought not strongly, that the effects of the pandemic on male and female unemployment and participation rates do not differ statistically. This paper encourages the use of time series methods to complement the traditional impact evaluation approaches, which to date have leaned mostly on cross-section and panel observations.

A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors

Advancements in sensing and network technologies have increased the amount of data being collected to monitor the worker conditions. In this study, we consider the use of time series methods to forecast physical fatigue using subjective ratings of perceived exertion (RPE) and gait data from wearable sensors captured during a simulated in-lab manual material handling task (Lab Study 1) and a fatiguing squatting with intermittent walking cycle (Lab Study 2). To determine whether time series models can accurately forecast individual response and for how many time periods ahead, five models were compared: naïve method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and the vector error correction model (VECM). For forecasts of three or more time periods ahead, the VECM model that incorporates historical RPE and wearable sensor data outperformed the other models with median mean absolute error (MAE) <1.24 and median MAE <1.22 across all participants for Lab Study 1 and Lab Study 2, respectively. These results suggest that wearable sensor data can support forecasting a worker’s condition and the forecasts obtained are as good as current state-of-the-art models using multiple sensors for current time prediction.

Fluoroquinolone Use and Seasonal Patterns of Ciprofloxacin Resistance in Community-Acquired Urinary Escherichia coli Infection in a Large Urban Center.

Urinary tract infections caused by the bacterium Escherichia coli are among the most frequently encountered infections and are a common reason for antimicrobial prescriptions. Resistance to fluoroquinolone antimicrobial agents, particularly ciprofloxacin, has increased in recent decades. It is intuitive that variation in fluoroquinolone resistance is driven by changes in antimicrobial use, but careful study of this association requires the use of time-series methods. Between April 2010 and December 2014, we studied seasonal variation in resistance to ciprofloxacin, trimethoprim-sulfamethoxazole, and ampicillin in community-acquired urinary E. coli isolates in Montreal, Quebec, Canada. Using dynamic linear models, we investigated whether seasonal variation in resistance could be explained by seasonal variation in community antimicrobial use. We found a positive association between total fluoroquinolone use lagged by 1 and 2 months and the proportion of isolates resistant to ciprofloxacin. Our results suggest that resistance to ciprofloxacin is responsive to short-term variation in antimicrobial use. Thus, antimicrobial stewardship campaigns to reduce fluoroquinolone use, particularly in the winter when use is highest, are likely to be a valuable tool in the struggle against antimicrobial resistance.

Anomaly Detection in Network Traffic Using Selected Methods of Time Series Analysis

In this paper a few methods for anomaly detection in computer networks with the use of time series methods are proposed. The special interest was put on Brown's exponential smoothing, seasonal decomposition, naive forecasting and Exponential Moving Average method. The validation of the anomaly detection methods has been performed using experimental data sets and statistical analysis which has shown that proposed methods can efficiently detect unusual situations in network traffic. This means that time series methods can be successfully used to model and predict a traffic in computer networks as well as to detect some unusual or unrequired events in network traffic.

Pitfalls in the Use of Time Series Methods to Study Deterrence and Capital Punishment

Evaluate the use of various time series methods to measure the deterrence effect of capital punishment. The analysis of the time series approach to deterrence is conducted at two levels. First, the mathematical foundations of time series methods are described and the link between the time series properties of aggregate homicide and execution series and individual decision making is developed. Second, individual studies are examined for logical consistency. The analysis concludes that time series methods used to study the deterrence effects of capital punishment suffer from fundamental limitations and fail to provide credible evidence. The common limitation of these studies is their lack of attention to identification problems. Suggestions are made as to directions for future work that may be able to mitigate the weaknesses of the current literature. Time series studies of capital punishment suffer from sufficiently serious identification problems that existing empirical findings are compatible with either the presence or the absence of a deterrent effect.

A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use

We analyzed the peak load reductions due to a residential direct load control program for air-conditioners in southern Ontario in 2008. In this program, participant thermostats were increased by 2 °C for four hours on five event days. We used hourly, whole-house data for 195 participant households and 268 non-participant households, and four different methods of analysis ranging from simple spreadsheet-based comparisons of average loads on event days, to complex time-series regression. Average peak load reductions were 0.2–0.9 kWh/h per household, or 10–35%. However, there were large differences between event days and across event hours, and in results for the same event day/hour, with different analysis methods. There was also a wide range of load reductions between individual households, and only a minority of households contributed to any given event. Policy makers should be aware of how the choice of an analysis method may affect decisions regarding which demand-side management programs to support, and how they might be incentivized. We recommend greater use of time-series methods, although it might take time to become comfortable with their complexity. Further investigation of what type of households contribute most to aggregate load reductions would also help policy makers better target programs.

SPECTRAL PROCEDURES ENHANCE THE ANALYSIS OF THREE AGRICULTURAL TIME SERIES

Many agricultural and environmental variables are influenced by cyclic processes that occur naturally. Consequently their time series often have cyclic behavior. This study develops time series models for three different phenomena: (1) a 60 year-long state annual average crop yield record, (2) a four year-long daily stream flow record with values aggregated to weekly averages, and (3) a half-hour long wind speed record sampled at 10 hertz with values aggregated to 0.5 min averages. Trend tests, simple high pass filtering, and spectral analysis on original and detrended and residual data series are used to guide model development. Next, as a means to provide insight for researchers, nonlinear regression procedures are used to develop models in the time domain. The models considered may have a large scale trend, low to high frequency cycles, and, if need be, an autoregressive (AR) error structure. Selected models for all three sets included a trend component. The model for yield has a linear trend in time and includes two high frequency cycles of 2.3 and 2.5 years. The model for stream flow has a complicated trend consisting of splined polynomials in the square root of time. Cycles include an annual and approximately 8, 6, and 3 month periods. Also an AR1 error structure is added. Results suggest the wind speed can be modeled as a superposition of damped and undamped oscillations. A zero order fractional Bessel function models the trend, here a damped oscillation with a period of 10.5 min. Smaller scale regular cycles of 6.6, 3.3 and 2.2 min are added along with an AR1 error structure. The use of time series methods instead of the inverse transform on selected frequencies allows for simultaneous estimation of all components. Moreover it opens the door to the use of a much broader class of functions to model the trend, to the use of other kinds of periodic functions to model the cycles, and to the incorporation of structure in the error term. This approach may provide useful insight and a methodological approach for several ongoing and some future studies at the National Soil Tilth Laboratory.

Assessing the Effects of Terrorism on Tourism by Use of Time Series Methods

This paper presents an assessment of the effects of terrorism on tourism by using time series methods, namely the ARMAX (autoregressive moving average with explanatory variables) model. This is a single-equation approach, which has the ability to provide impact analysis easily. The use of the ARMAX model allows for the general shape of the lag distribution of the impacts of the explanatory variables based on the ratio of lag polynomials for the independent and dependent variables. The ARMAX models, like the ARIMA models, provide for a short-term assessment of terrorist incidents on tourism.

Are there asymmetries in UK Consumption? A time series analysis

In this paper we use time series techniques in order to test for asymmetric dynamics in UK consumption. The notion that fluctuation over the business cycle are asymmetric has been put forward at various times in the last century. Nevertheless, the most common representations of aggregate time series in macroeconomies are usually smooth and sluggish. The use of time series methods should be considered as only the first stage of an empirical investigation of asymmetries. It is also essential to develop economic models of asymmetric behaviour and to employ tests of asymmetric adjustment at the level structural realtionship.

Statistical process control procedures for correlated observations

AbstractMeasurements from industrial processes are often serially correlated. The impact of this correlation on the performance of the cumulative sum and exponentially weighted moving average charting techniques is investigated in this paper. It is shown that serious errors concerning the “state of statistical process control” may result if the correlation structure of the observations is not taken into account. The use of time series methods for coping with serially correlated observations is outlined. Paper basis weight measurements are used to illustrate the time series methodology.

Terrorism in a Bargaining Framework

A LTHOUGH hostage seizures are a small percentage of terrorist incidents, they represent some of the most spectacular and influential events.2 The takeover of the American embassy in Tehran on November 14, 1979, the seizure of eleven dPEC oil ministers on December 21, 1975, and the capture and killing of nine Israeli athletes on September 5, 1972, are incidents not easily forgotten. From 1968 through 1982, of the approximately 8,000 reported terrorist events, 540 (7 percent) were transna-

Time Series Methods In Water Resources Engineering

Two case studies are presented in this paper showing clearly the practical importance of the use of time series methods in the hydrologic design and the operation of water resources systems. The fi...