There has been increased interest in time series data mining recently. In some cases, approaches of real-time segmenting time series are necessary in time series similarity search and data mining, and this is the focus of this paper. A real-time iterative algorithm that is based on time series prediction is proposed in this paper. Proposed algorithm consists of three modular steps. (1) Modeling...

The objective of the study was to describe seasonality of hospitalizations for heart failure in tropical climate as it has been described in cold climates. Seasonal Auto-regressive Integrated Moving-Average model was applied to time-series data of heart failure hospitalizations between 1996 and 2004 in Niteroi (Southeastern Brazil), collected from the Brazilian National Health Service Database....

Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With increase installed WWTPs worldwide, modeling and forecast their energy have become a critical factor in WWTP design meet environmental economic requirements. The accurate swift forecasting soft-sensors are not only supportive daily electric financial budgeting by practitioners on micro-scale, but also b...

A two-dimensional (2-D) state-space self-tuning control (STC) scheme for multi-input multi-output (MIMO) linear discrete-time stochastic systems with actuator faults has been developed in this paper. To develop the 2-D state-space STC design, the 2-D linear discrete-time stochastic system is transformed to, and treated as, a two-dimensional Roesser’s (2D-RM) model. A 2-D state-space STC methodo...

Indonesia's price index serves as a barometer for the nation's economic condition. One of Indonesia’s is Wholesale Price Index (WPI). WPI that tracks average change in wholesale prices over time. Time series analysis can be used forecasting because one time data. long memory, which condition data from different periods have high link despite being separated by large amount The Autoregressive Fr...

A non parametric Auto-Regressive Conditional Heteroscedastic model for financial returns series is considered in which the conditional mean and volatility functions are estimated non-parametrically using Nadaraya Watson kernel. test statistic unknown abrupt change point takes into consideration heteroskedasticity, dependence, heterogeneity fourth moment of returns, since kurtosis a function con...

Long memory analysis is one of the most active areas in econometrics and time series where various methods have been introduced to identify estimate long parameter partially integrated series. One common models used represent that a ARFIMA (Auto Regressive Fractional Integration Moving Average Model) which diffs are fractional number called parameter. To analyze determine model, fractal must be...

A time series forecasting is an active research applied significantly in a variety of economics areas. Over the past three decades an auto-regressive integrated moving average (ARIMA) model, as one of the most important time series models, has been applied in financial markets forecasting. Recent researches in time series forecasting ARIMA models indicate some basic limitations which detract fr...