1,721,184 research outputs found
La concentrazione di SO2 combinando i dati raccolti da centraline fisse e mobili: un modello state space
No full textGiven atmospheric measurement from a network of monitoring sites in the area of a city and over an extended period of time, an important problem is to identify the spatial and temporal structure of data.
In this paper we focus on the identification and estimate of a statistical model to analyse the SO2 in the city of Padua, where data are collected by some fixed stations and some mobile stations moving without any specific rule in different new locations, staying in every location for a variable number of days.
The proposed method divides the global variability in large scale and small scale using some stochastic process as component of variability.
The estimate is provided using a state space formulation of the model.
As applications of the model we propose the spatial and temporal prevision of the concentration of SO2 . Finally, an exercise is proposed to choose an optimal network for the mobiles monitoring stations for a fixed future time
Analisi della concentazione di SO2 combinando i dati raccolti da centraline fisse e mobili: un modello state space.
Full text linkGiven athmospheric measurement from a network of monitoring sites in the area of a city and over an extended period of time, an important problem is to identify the spatial and temporal structure of data.
In this paper we focus on the identification and estimate of a statistical model to analyse the SO2 in the city of Padua, where data are collected by some fixed stations and some mobile stations moving without any specific rule in different new locations, staying in every location for a variable number of days.
The proposed method divides the global variability in large scale and small scale using some stochastic process as component of variability.
The estimate is provided using a state space formulation of the model.
As applications of the model we propose the spatial and temporal prevision of the concentration of SO2. Finally, an exercise is proposed to choose an optimal network for the mobiles monitoring stations for a fixed future time
Mining massive Data Sets from web
No full textIn this paper we discuss tools for behavioural analysis of visitors to a website,
focusing on classical and new web mining procedures. A short reference to very popular
web-analytic tools is followed by the presentation of some more sophisticated models,
based on raw data, used to predict cluster sequences of pages and time on site. All models
are applied to analysing the behaviour of visitors to the website of a business consulting
company
Bayesian semiparametric customer base segmentation of mobile phone users based on longitudinal traffic data
No full textCustomer segmentation is one of the most important purposes of customer base analysis for telecommunication companies. Because companies accumulate very large amounts of data on customer behavior, segmentation is typically achieved by profiling and clustering traffic behavior jointly with demographic data and contracts characteristics. Unfortunately, most algorithms and models used for segmentation do not take into account the longitudinal characteristics of data. In particular, in telecommunication traffic analysis, the importance of decreasing patterns of traffic in customers' lives is well known, and it is relevant to aggregate all clients with such a pattern, while other unknown clusters may be of interest for the marketing manager. Our approach to address this problem is based on specifying the distribution of functions as a mixture of a parametric hierarchical model describing the decreasing pattern segment and a nonparametric contamination that allows unanticipated curve shapes in subjects' traffic. The parametric component is chosen based on prior knowledge, while the contamination is characterized as a functional Dirichlet process
La previsione del churn
No full textUno dei problemi piu` importanti per il marketing di un ́azienda di
telecomunicazioni consiste nella previsione dei clienti che presto
decideranno di cambiare gestore ed abbandonare i servizi dell ́azienda (churn). La previsione e`, in genere, fatta cercando di sfruttare la maggior parte delle moltissime informazioni che le aziende possiedono su ciascun cliente. La complessita` dei dati disponibili porta alla necessita ́ di utilizzare modelli di previsione non sempre semplici.
L ́articolo presenta alcuni esempi reali di modellazione della propensione alla disattivazione e alcune riflessioni, basate sull ́esperienza dell ́autore, sul miglior modo di affrontare le problematiche legate al churn
Non-parametric space-time modeling of SO2 in presence of many missing data
No full textGiven pollution measurement from a network of monitoring sites in
the area of a city and over an extended period of time, an important problem is
to identify the spatial and temporal structure of the data. In this paper we focus
on the identification and estimate of a statistical non parametric model to analyse
the SO2 in the city of Padua, where data are collected by some fixed stations and
some mobile stations moving without any specific rule in different new locations.
The impact of the use of mobile stations is that for each location there are times
when data was not collected. Assuming temporal stationarity and spatial isotropy
for the residuals of an additive model for the logarithm of SO2 concentration, we
estimate the semivariogram using a kernel-type estimator. Attempts are made to
avoid the assumption of spatial isotropy. Bootstrap confidence bands are obtained
for the spatial component of the additive model that is a deterministic function
which defines the spatial structure. Finally, an example is proposed to design an
optimal network for the mobiles monitoring stations in a fixed future time, given
all the information available
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