Sonja Pravilovic

Using multiple time series analysis for geosensor data forecasting

Forecasting in geophysical time series is a challenging problem with numerous applications. The presence of correlation (i.e.spatial correlation across several sites and time correlation within each site) poses difficulties with respect to traditional modeling, computation and statistical theory. This paper presents a cluster-centric forecasting methodology that allows us to yield a characterization of correlation in geophysical time series through a spatio-temporal clustering step. The clustering phase is designed for partitioning time series of numeric data routinely sampled at specific space locations. A forecasting model is then computed by resorting to multivariate time series analysis, in order to predict the future values of a time series by utilizing not only its own historical values, but also information from other cluster-time series. Experimental results highlight the importance of dealing with both temporal and spatial correlation and validate the proposed cluster-centric strategy in the computation of a multivariate time series forecasting model.

Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data

Clustering geosensor data is a problem that has recently attracted a large amount of research. In this paper, we focus on clustering geophysical time series data measured by a geo-sensor network. Clusters are built by accounting for both spatial and temporal information of data. We use clusters to produce globally meaningful information from time series obtained by individual sensors. The cluster information is integrated to the ARIMA model, in order to yield accurate forecasting results. Experiments investigate the trade-off between accuracy and efficiency of the proposed algorithm.

An Intelligent Technique for Forecasting Spatially Correlated Time Series

The analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern leads to insights that would otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. In this paper, we propose an intelligent forecasting technique, which explicitly accounts for the property of spatial autocorrelation when learning linear autoregressive models (ARIMA) of spatial correlated ecologic time series. The forecasting algorithm makes use of an autoregressive statistical technique, which achieves accurate forecasts of future data by taking into account temporal and spatial dimension of ecologic data. It uses a novel spatial-aware inference procedure, which permits to learn the autoregressive model by processing a time series in a neighborhood (spatial lags). Parameters of forecasting models are jointly learned on spatial lags of time series. Experiments with ecologic data investigate the accuracy of the proposed spatial-aware forecasting model with respect to the traditional one.

Process mining to forecast the future of running cases

Processes are everywhere in our daily lives. More and more information about executions of processes are recorded in event logs by several information systems. Process mining techniques are used to analyze historic information hidden in event logs and to provide surprising insights for managers, system developers, auditors, and end users. While existing process mining techniques mainly analyze full process instances (cases), this paper extends the analysis to running cases, which have not yet completed. For running cases, process mining can be used to notify future events. This forecasting ability can provide insights for check conformance and support decision making. This paper details a process mining approach, which uses predictive clustering to equip an execution scenario with a prediction model. This model accounts for recent events of running cases to predict the characteristics of future events. Several tests with benchmark logs investigate the viability of the proposed approach.

Enhancing Regression Models with Spatio-temporal Indicator Additions

The task being addressed in this paper consists of trying to forecast the future value of a time series variable on a certain geographical location, based on historical data of this variable collected on both this and other locations. In general, this time series forecasting task can be performed by using machine learning models, which transform the original problem into a regression task. The target variable is the future value of the series, while the predictors are previous past values of the series up to a certain p-length time window. In this paper, we convey information on both the spatial and temporal historical data to the predictive models, with the goal of improving their forecasting ability. We build technical indicators, which are summaries of certain properties of the spatio-temporal data, grouped in the spatio-temporal clusters and use them to enhance the forecasting ability of regression models. A case study with air temperature data is presented.

Wind Power Forecasting Using Time Series Cluster Analysis

The growing integration of wind turbines into the power grid can only be balanced with precise forecasts of upcoming energy productions. This information plays as basis for operation and management strategies for a reliable and economical integration into the power grid. A precise forecast needs to overcome problems of variable energy production caused by fluctuating weather conditions. In this paper, we define a data mining approach, in order to process a past set of the wind power measurements of a wind turbine and extract a robust prediction model. We resort to a time series clustering algorithm, in order to extract a compact, informative representation of the time series of wind power measurements in the past set. We use cluster prototypes for predicting upcoming wind powers of the turbine. We illustrate a case study with real data collected from a wind turbine installed in the Apulia region.

Very Short-Term Wind Speed Forecasting Using Spatio-Temporal Lazy Learning

A wind speed forecast corresponds to an estimate of the upcoming production of a wind farm. The paper illustrates a variant of the Nearest Neighbor algorithm that yields wind speed forecasts, with a fast time resolution, for a (very) short time horizon. The proposed algorithm allows us to monitor a grid of wind farms, which collaborate by sharing information (i.e. wind speed measurements). It accounts for both spatial and temporal correlation of shared information. Experiments show that the presented algorithm is able to determine more accurate forecasts than a state-of-art statistical algorithm, namely auto. ARIMA.

Leveraging correlation across space and time to interpolate geophysical data via CoKriging

ABSTRACT Managing geophysical data generated by emerging spatiotemporal data sources (e.g. geosensor networks) presents a growing challenge to Geographic Information System science. The presence of correlation poses difficulties with respect to traditional spatial data analysis. This paper describes a novel spatiotemporal analytical scheme that allows us to yield a characterization of correlation in geophysical data along the spatial and temporal dimensions. We resort to a multivariate statistical model, namely CoKriging, in order to derive accurate spatiotemporal interpolation models. These predict unknown data by utilizing not only their own geosensor values at the same time, but also information from near past data. We use a window-based computation methodology that leverages the power of temporal correlation in a spatial modeling phase. This is done by also fitting the computed interpolation model to data which may change over time. In an assessment, using various geophysical data sets, we show that the presented algorithm is often able to deal with both spatial and temporal correlations. This helps to gain accuracy during the interpolation phase, compared to spatial and spatiotemporal competitors. Specifically, we evaluate the efficacy of the interpolation phase by using established machine-learning metrics (i.e. root mean squared error, Akaike information criterion and computation time).

Sampling Training Data for Accurate Hyperspectral Image Classification via Tree-Based Spatial Clustering.

The classification of hyperspectral images is a challenging task due to the high dimensionality of the task (i.e. large amount of pixels described over a high number of spectral channels) coupled with the small number of labeled examples typically available for learning. In the last decades, Support Vector Machines (SVMs) have gained in popularity in the field of the hyperspectral image classification as they address large attribute spaces and produce solutions from sparsely labeled data. However, they require “representative” training samples of the unknown class distribution to be accurate. In general, these samples are manually selected by expert visual inspection or field survey. This paper describes a learning schema, where the most suitable pixels to train the classifier are automatically selected via a spectral-spatial clustering phase. This reduces the expert effort required for sampling training pixels. Experimental results highlight that the proposed solution allows us to achieve a classification accuracy that outperforms the accuracy of both random and baseline sampling schemes.

Predictive Regional Trees to Supplement Geo-Physical Random Fields

Nowadays ubiquitous sensor stations are deployed to measure geophysical fields for several ecological and environmental processes. Although these fields are measured at the specific location of stations, geo-statistical problems demand for inference processes to supplement, smooth and standardize recorded data. We study how predictive regional trees can supplement data sampled periodically in an ubiquitous sensing scenario. Data records that are similar one to each other are clustered according to a rectangular decomposition of the region of analysis; a predictive model is associated to the region covered by each cluster. The cluster model depicts the spatial variation of data over a map, the predictive model supplements any unknown record that is recognized belong to a cluster region. We illustrate an incremental algorithm to yield time-evolving predictive regional trees that account for the fact that the statistical properties of the recorded data may change over time. This algorithm is evaluated with spatio-temporal data collections.