b a c k g r o u n d & aim: one of the common used models in time series is auto regressive integrated moving  average  (arima)  model.  arima  will  do  modeling  only  linearly.  artificial  neural networks (ann) are modern methods that be used for time series forecasting.  these models can identify non-linear relationships  among data. the breast cancer has the most mortality of cancers among...

Due to notable depletion of fuel, non-conventional energy aids the present grid for Power management across the country. Wind energy indeed has major contribution next to solar. Prediction of wind power is essential to integrate wind farms into the grid. Due to intermittency and variability of wind power, forecasting of wind behavior becomes intricate. Wind speed forecasting tools can resolve t...

This paper applies the Hodrck-Prescott (HP) filter to forecast short-term residential real estate prices under cyclical movements. We separate the trend component from the cyclical component. We show that each regional residential market reacts not only to previous price movements, but also that these regional markets react to previous shocks under Auto Regressive Integrated Moving Average (ARI...

A definition of reliability appropriate for systems containing significant software that includes trustworthiness and is independent of requirements will be stated and argued for. The systems addressed will encompass the entire product development process as well as both product and its documentation. Cost incurred as a result of faults will be shown to be appropriate as a performance measureme...

This article presents the results of reviewing predictive capacity Google Trends for national elections in Chile. The electoral between Michelle Bachelet and Sebastián Piñera 2006, Eduardo Frei 2010, Evelyn Matthei 2013, Alejandro Guillier 2017, Gabriel Boric José Antonio Kast 2021 were reviewed. time series analyzed organized on basis relative searches candidacies, assisted by R software, main...

Many environmental and socioeconomic time–series data can be adequately modeled using Auto-Regressive Integrated Moving Average (ARIMA) models. We call such time–series ARIMA time–series. We consider the problem of clustering ARIMA time–series. We propose the use of the Linear Predictive Coding (LPC) cepstrum of time–series for clustering ARIMA time–series, by using the Euclidean distance betwe...

Received Jul 22, 2012 Revised Oct 23, 2012 Accepted Nov 14, 2012 Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades but not very often used to forec...

It is an important issue to study the prediction precision of Particulate Matter 2.5, PM2.5 (28 μg/m3), concentration change. The concentration of PM2.5 is influenced by many factors, and its change is characterized by non-linearity and randomness. This paper establishes a prediction model of PM2.5 concentration change to fit the nonlinear and random trend by combining Auto-Regressive Integrate...

This paper presents the adaptation of CORBFN, an evolutionary cooperative-competitive hybrid algorithm for the design of Radial Basis Function Networks, for short-term forecasting of the price of extra virgin olive oil. In the proposed cooperative-competitive environment, each individual represents a Radial Basis Function, and the entire population is responsible for the final solution. In orde...

This paper presents a comprehensive study of ANFIS+ARIMA+IT2FLS models for forecasting the weather of Raipur, Chhattisgarh, India. For developing the models, ten year data (2000-2009) comprising daily average temperature (dry-wet), air pressure, and wind-speed etc. have been used. Adaptive Network Based Fuzzy Inference System (ANFIS) and Auto Regressive Moving Average (ARIMA) models based on In...