The Auto Regressive Integrated Moving Average (ARIMA) model seems not to easily capture the nonlinear patterns exhibited by 2019 novel coronavirus (COVID-19) in terms of daily confirmed cases. As a result, Artificial Neural Network (ANN) and Error, Trend, Seasonality (ETS) modeling have been successfully applied resolve problems with estimation. Our research suggests that it would be ideal use ...

The research results of a quasi-experiment on an award-winning website are reported. A virtual community was built on this website to study its performance under the influence of two treatments. The first treatment was an administrative modification – a change of membership policy that restricted nonmembers’ access to a popular area of the website. The second treatment was the addition of an ad...

This article presents the results of reviewing predictive capacity Google Trends for national elections in Chile. The electoral between Michelle Bachelet and Sebastián Piñera 2006, Eduardo Frei 2010, Evelyn Matthei 2013, Alejandro Guillier 2017, Gabriel Boric José Antonio Kast 2021 were reviewed. time series analyzed organized on basis relative searches candidacies, assisted by R software, main...

The main challenge in a full-duplex transceiver design is created by the self-interference caused coupling of transmitted signal to transceiver’s own receiver. effect non-linear operation both power amplifier at transmitter and low noise receiver are considered cancelation. performance three cancelers studied: linear cancelation, auto-regressive moving-average (ARMA) based cancelation neural ne...

Diarrhoea is still a considerable public-health problem in developing countries, especially among children aged less than five years. The well-known relationship between seasonal variation and aetiological agents of diarrhoeal diseases helps inform the decisions about the prevention and control measures. The aim of this study was to identify the temporal patterns of deaths due to diarrhoea in c...

We propose a tracker-independent framework to determine time instants when a video tracker fails. The framework is divided into two steps. First, we determine tracking quality by comparing the distributions of the tracker state and a region around the state. We generate the distributions using Distribution Fields and compute a tracking quality score by comparing the distributions using the L1 d...

The features of dynamic, noise and instability, make the network traffic eruptive and unstable, and this obstructs the network traffic prediction. In order to figure out its characteristics and developing tendency accurately, the paper proposes a wavelet-transform-based prediction algorithm: Firstly, with the multi-resolution analysis of wavelet transform, the network traffic, which is difficul...

We develop a stochastic volatility option pricing model that exploits the informative content of historical high frequency data. Using the Two Scales Realized Volatility as a proxy for the unobservable returns volatility, we propose a simple (affine) but effective long-memory process: the Heterogeneous Auto-Regressive Gamma (HARG) model. This discrete–time process, combined with an exponential ...

This paper presents a comprehensive study of ANFIS+ARIMA+IT2FLS models for forecasting the weather of Raipur, Chhattisgarh, India. For developing the models, ten year data (2000-2009) comprising daily average temperature (dry-wet), air pressure, and wind-speed etc. have been used. Adaptive Network Based Fuzzy Inference System (ANFIS) and Auto Regressive Moving Average (ARIMA) models based on In...

One of the most natural and primary ways of data collection in wireless sensor networks is to periodically report sensed data values from sensor node to aggregator. However, this kind of data collection mechanism comes at the cost of power consumption and packet collision. In this paper, we developed an automatic ARIMA (Auto Regressive Integrated Moving Average) modeling based data aggregation ...