Comparing Models for Time Series Analysis

Historically, traditional methods such as Autoregressive Integrated Moving Average (ARIMA) have played an important role for researchers studying time series data. Recently, as advances in computer science and machine learning have gained widespread attention, researchers of time series analysis have brought new techniques to the table. In this paper, we examine the performance difference between ARIMA and a relatively recent development in the machine learning community called Long-Short Term Memory Networks (LSTM). Whereas many traditional methods assume the existence of an underlying stochastic model, these algorithmic approaches make no claims about the generation process. Our primary measure of performance is how well each model forecasts out-of-sample data. We find that data with strong seasonal structure are forecast comparatively well by either method. On the other hand, without strong seasonality, there is very little information that can be extracted and both methods tend to perform poorly in forecasting

Nurse Work Environment and Hospital Outcomes

The research question is as follows: To what extent do nurse work environments affect a hospital’s performance? The purpose of this paper is to identify potential relationships between a popular measure of nurse work environments called the Practice Environment Scale of the Nursing Work Index (PES-NWI) and patient outcomes, specifically 30 day mortality. While previous research on the same topic have largely utilized traditional regression approaches to study the relationship, this paper will use a pre-processing technique called matching to reduce the imbalance between observations in the treated and control groups. Matching enables us to reduce biases frequently present in many social science studies and strengthen the validity of the conclusions drawn. The resulting comparison of patient outcome showed no statistically significant difference between high and low PES-NWI hospitals

Safety Impacts of the Actuated Signal Control at Urban Intersections

To reduce travel time, the actuated signal controls have been implemented at urban intersections. However, the safety impacts of actuated signal controls thus far have rarely been examined. In this assessment of the safety impact of urban intersections with semi-actuated signal controls, the safety performance functions and EB approaches were applied. The semi-actuated signal controls have increased injuries and total crashes in all crash types by around 5.9% and 3.8%, respectively. Regarding the most common crash types, such as angle, sideswipe & rear-end, and head-on crashes, semi-actuated signal controls have been seen to decrease injuries by 7.7%. Total crashes have been reduced by over 9.2% through the use of semi-actuated signal controls. This may be result of optimal signal timings considering traffic conditions during peak time periods. In conclusion, safety impact factors which have been established in this study can be used to improve safety and minimize travel times using semi-actuated signal controls

The competition number of a graph having exactly one hole

AbstractLet D be an acyclic digraph. The competition graph of D has the same set of vertices as D and an edge between vertices u and v if and only if there is a vertex x in D such that (u,x) and (v,x) are arcs of D. The competition number of a graph G, denoted by k(G), is the smallest number k such that G together with k isolated vertices is the competition graph of an acyclic digraph. In this paper, we show that the competition number of a graph having exactly one chordless cycle of length at least 4 is at most two. We also give a large family of such graphs whose competition numbers are less than or equal to one

Multi-Screen Strategy for Selling Mobile Content to Customers

Our research aims to discover the role of multiple smart devices and their different screen sizes in paid content sales, thus building a multi-screen content sales strategy. Our econometric model adopts a difference-in-differences method to measure the impact of multi-screen devices on users’ content consumption through screen size effects. In a natural experiment setting, we sample 238 individual customers who registered a single smartphone with a 3- to 4-inch screen at the beginning and then added devices with a similar or larger screen. Our paper determines the parameters in existing theoretical frameworks of online consumer utility (product selection and digital content price) to determine paid content purchase behavior in a multi-screen environment. Our key findings are that the price sensitivity of content decreases as a user registers new smart devices and registering new devices with larger screens positively influences less popular content consumption more than a small screen device does

A NETWORK LINK PREDICTION MODEL BASED ON OBJECT-OBJECT MATCH METHOD

In this paper, we proposed and evaluated a new network link prediction method that can be used to predict missing links in a social network. In the proposed model, to improve the prediction accuracy, the network link prediction problem is transformed to a general object-object match prediction problem, in which the nodes of a network are regarded as objects and the neighbors of a node are regarded as the node\u27s associated features. Also a machine learning framework is devised for the systematic prediction. We compare the prediction accuracy of the proposed method with existing network link prediction methods using well-known network datasets such as a scientific co-authorship network, an e-mail communication network, and a product co-purchasing network. The results showed that the proposed approach made a significant improvement in all three networks. Also it reveals that considering the neighbor\u27s neighbors are critical to improve the prediction accuracy

Enhanced overall efficiency of GaInN-based light-emitting diodes with reduced efficiency droop by Al-composition-graded AlGaN/GaN superlattice electron blocking layer

AlxGa1-xN/GaN superlattice electron blocking layers (EBLs) with gradually decreasing Al composition toward the p-type GaN layer are introduced to GaInN-based high-power light-emitting diodes (LEDs). GaInN/GaN multiple quantum well LEDs with 5- and 9-period Al-composition-graded AlxGa1-xN/GaN EBL show comparable operating voltage, higher efficiency as well as less efficiency droop than LEDs having conventional bulk AlGaN EBL, which is attributed to the superlattice doping effect, enhanced hole injection into the active region, and reduced potential drop in the EBL by grading Al compositions. Simulation results reveal a reduction in electron leakage for the superlattice EBL, in agreement with experimental results. (C) 2013 AIP Publishing LLC.open1133sciescopu