A multi-perspective analysis of social context and personal factors in office settings for the design of an effective mobile notification system

In this study, we investigate the effects of social context, personal and mobile phone usage on the inference of work engagement/challenge levels of knowledge workers and their responsiveness to well-being related notifications. Our results show that mobile application usage is associated to the responsiveness and work engagement/challenge levels of knowledge workers. We also developed multi-level (within- and between-subjects) models for the inference of attentional states and engagement/challenge levels with mobile application usage indicators as inputs, such as the number of applications used prior to notifications, the number of switches between applications, and application category usage. The results of our analysis show that the following features are effective for the inference of attentional states and engagement/challenge levels: the number of switches between mobile applications in the last 45 minutes and the duration of application usage in the last 5 minutes before users' response to ESM messages

Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

This study reports a statistical analysis of monthly sunspot number time series and observes non homogeneity and asymmetry within it. Using Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p,q)). Based on minimization of AIC we find 3 and 1 as the best values of p and q respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott's index of second order and coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).Comment: 17 pages, 4 figure

Pre-processing inputs for optimally-configured time-delay neural networks

A procedure for pre-processing non-stationary time series is proposed for modelling with a time-delay neural network (TDNN). The procedure stabilises the mean of the series and uses a fast Fourier transform to determine the TDNN input size. Results of applying this procedure on five well-known data sets are compared with existing hybrid neural network techniques, demonstrating improved prediction performance

Summarizing Time Series: Learning Patterns in 'Volatile' Series

Most financial time series processes are nonstationary and their frequency characteristics are time-dependant. In this paper we present a time series summarization and prediction framework to analyse nonstationary, volatile and high-frequency time series data. Multiscale wavelet analysis is used to separate out the trend, cyclical fluctuations and autocorrelational effects. The framework can generate verbal signals to describe each effect. The summary output is used to reason about the future behaviour of the time series and to give a prediction. Experiments on the intra-day European currency spot exchange rates are described. The results are compared with a neural network prediction framework

Financial Information Grid – an ESRC eSocial Science Pilot

The large volume of time-varying quantitative and qualitative data available online is needed in a realistic simulation in theoretical econometrics from an academic perspective. And the analysis of such data is strategically important for trading in the financial markets. A distributed environment has been created that offers two services – time-serial and news analysis – using Globus and Java Commodity Grids. A demonstrator that can be used for bootstrapping and for detecting financial market ‘sentiment ’ in real time is reported in this paper. 1