With the development of Internet and computer science, computer network is changing people’s lives. Meanwhile, Network traffic prediction model itself becomes more and more complex. It is an important research direction to quickly and accurately detect the intrusions or attacks. The performance efficiency of a network intrusion detection system is dominated by pattern matching algorithm. Howeve...

To investigate the variability in energy output from a network of photo-voltaic cells, solar radiation was recorded at ten sites every ten minutes in the Pentland Hills to the south of Edinburgh. We identify spatio-temporal auto-regressive moving average (STARMA) models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we s...

Speech synthesizers based on paramedic methods, still have not achieved the expected naturalness. This is due to less consideration on linear time variant nature between the neighbor phonemes. This paper presents a study to model the phoneme transitions between neighbor phonemes with lesser number of parameters using Auto Regressive Moving Average (ARMA) model, where Steiglitz-McBride algorithm...

Count-valued time series data are routinely collected in many application areas. We particularly motivated to study the count of daily new cases, arising from COVID-19 spread. First, we propose a Bayesian framework time-varying semiparametric AR(p) model for and then extend it more sophisticated INGARCH model. calculate posterior contraction rates proposed methods with respect average Hellinger...

Cognitive radio requires real time monitoring of the spectrum to determine the frequency of transmission. Spectrum analysers tend to employ a slow frequency sweep and hence such measurements can only be used for modelling of the spectrum, which can provide vital information for frequency planning and management. Occupancy measurements every hour over a seven-day period were performed in the UK ...

Time series data mining (TSDM) techniques explores large amount of time series data in search of interesting relationships among variables. The TSDM methods overcome limitations including stationarity and linearity requirements of traditional time series analysis by adapting data mining concepts for analyzing time series data. The Feed Forward Neural Net is one of the most widely used neural ne...

This study focuses on predicting and estimating possible stock assets in a favorable real-time scenario for financial markets without the involvement of outside brokers about broadcast-based trading using various performance factors data metrics. Sample from Y-finance sector was assembled API-based series quite accurate precise. Prestigious machine learning algorithmic performances both classif...

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...