Recent work has proposed a certainty trend (CT) elimination technique employed for the auto-regressive/autoregressive and moving-average (AR/ARMA) model pulse position prediction. In this paper, we investigate the intra pulse parameter estimation and pulse position prediction of the chirp and stochastic pulse position modulation (CSPPM) combined signal. The quick dechirp method is adopted to th...

The purpose of the present study is to look into the characteristics of the mean monthly total ozone time series over Arosa (46.8N/ 9.68E), Switzerland using statistical methodologies. In this paper, the time series pertains to the data between 1932 and 1971. The intrinsic deterministic patterns of the time series have been investigated through autocorrelation analysis. A second order Auto Regr...

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of an assigned output. In the first example, we use the daily weather dataset of the municipality of Naples (Italy) starti...

Paper presents a simplistic approach towards the detection and dynamics analysis of volcanic eruptions represented as unevenly spaced spatio-temporal time series of satellite retrieved hot spots. The paper discusses isolation and interpolation of hot spots data produced by Nightfire algorithm for the purposes of short-term volcanic activity ARIMA based forecasting. The case study for Chirpoi Sn...

Worldwide, influenza is estimated to result in approximately 3 to 5 million annual cases of severe illness and approximately 250,000 to 500,000 deaths. We need an accurate time-series model to predict the number of influenza patients. Although time-series models with different time lags as feature spaces could lead to varied accuracy, past studies simply adopted a time lag in their models witho...

This study aimed to contribute to the management of a healthcare organization by providing management information using time-series analysis of business data accumulated in the hospital information system, which has not been utilized thus far. In this study, we examined the performance of the prediction method using the auto-regressive integrated moving-average (ARIMA) model, using the business...

Like all manufacturing companies, refineries use many sensors to monitor and control the process of refining, therefore it is very crucial to detect any sensor faults or anomalies as early as possible, and to be able to replace or repair a sensor well in advance of any fault. Objective of this paper is to present a method for detecting anomalies in a sensor data, as well as to predict next occu...

Markov chains (MC) are statistical models used to predict very short short-term wind speed accurately. Such generally trained with a single moving window. However, time series do not possess an equal length of behavior for all horizons. Therefore, window can provide reasonable estimates but is optimal choice. In this study, forecasting model proposed that integrates MCs adjusting dynamic The se...

BACKGROUND Car accidents are currently a social issue globally because they result in the deaths of many people. The aim of this study was to examine traffic accidents in suburban Tehran and forecast the number of future accidents using a time-series model. METHODS The sample population of this cross-sectional study was all traffic accidents that caused death and physical injuries in suburban...