,4bstme—A pmeedurefor sequentiaffy eatirnating the parameters and orders of mixed autoregmsive moving-average signaf modefs from tirneserfes data is presented. Iderrtfffftion ia performed by first fderstffying a purely asrtoregmwive aignaf model. Tire parametem and orders of tbe mixed autoregmsaive moving-average proeeaa are then gfven from tbe solutton of sfmple sdgebraic equations involving t...

When outputs of computational models are time series or functions of other continuous variables like distance, angle, etc., it can be that primary interest is in the general pattern or structure of the curve. In these cases, model sensitivity and uncertainty analysis focuses on the effect of model input choices and uncertainties in the overall shapes of such curves. We explore methods for chara...

We propose MTSC, a filter-and-refine framework for time series Nearest Neighbor (NN) classification. Training time series belonging to certain classes are first modeled through Hidden Markov Models (HMMs). Given an unlabeled query, and at the filter step, we identify the top K models that have most likely produced the query. At the refine step, a distance measure is applied between the query an...

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The dynamic and complex relationship among economic variables has attracted the researchers, policy makers and business people alike. This study is an attempt to test the dynamic relationship among gold price, stock returns, exchange rate and oil price. All these variables have witnessed significant changes over time and hence, it is absolutely necessary to validate the relationship periodicall...

This paper investigates the use of dynamic linear modeling and maximum likelihood estimator for water quality model structure identi cation. In addition to the posterior trajectories of model's parameters, the proposed method also examines the trajectory of the estimated prediction error variance. The premise is that the model predictability should be improved as we move down in a time series. ...

Often an analyst has time series data available from performance monitors and needs to make statistical sense of it for capacity planning purposes. For example, a twenty-four hour column chart produced by averaging multiple days of time interval samples yields a statistically stable view of a resource’s usage characteristics across the day and clearly identifies its busy period. Since monitorin...