Consider the fractional ARIMA time series with innovations that have innnite variance. This is a nite parameter model which exhibits both long-range dependence (long memory) and high variability. We prove the consistency of an estimator of the unknown parameters which is based on the periodogram and derive its asymptotic distribution. This shows that the results of Mikosch, Gadrich, Kl uppelber...

Temperature is one of the main climatic elements that can indicate climate change as climate change seems to be one of the most important issues in the recent two decades. The aim of this research is to study temporal variation in temperature over Dibrugarh city, Assam, India during the period 1981–2010. In this article we are interested in the time series modeling of the average monthly mean t...

In this paper we develop a semi-parametric approach to model nonlinear relationships in serially correlated data. To illustrate the usefulness of this approach, we apply it to a set of hourly electricity load data. This approach takes into consideration the effect of temperature combined with those of timeof-day and type-of-day via nonparametric estimation. In addition, an ARIMA model is used t...

Statistical evidence suggests that the autocorrelation function of a compressed-video sequence is better captured by e ? p k than by k ? = e ? log k (long-range dependence) or e ?k (Markovian). A video model with such a correlation structure is introduced based on the so-called M=G=1 input processes. In essence, the M=G=1 process is a stationary version of the busy-servers process of a discrete...

With the continuous advancement of machine learning and increasing availability internet-based information, there is a belief that these approaches datasets enhance accuracy price prediction. However, this study aims to investigate validity claim. The examines effectiveness large dataset sophisticated methodologies in forecasting foreign exchange rates (FX) commodity prices. Specifically, we em...

During the COVID-19 outbreak, governments, scientists, health workers, and numerous people worked on strategies or solutions for halting disease propagation. Unfortunately, need monitoring is steeply increasing, taking necessary restrictive actions currently unavoidable. Due to lack of epidemiological data constantly changing numbers, constructing less error-prone predictive models reliable mat...

Theoretician who simplified nuclear physics and revamped Japan’s science.

this paper attempts to compare the forecasting performance of the arima model and hybrid arma-garch models by using daily data of the iran’s exchange rate against the u.s. dollar (irr/usd) for the period of 20 march 2014 to 20 june 2015. the period of 20 march 2014 to 19 april 2015 was used to build the model while remaining data were used to do out of sample forecasting and check the forecasti...

Abstract The present study aimed to use the autoregressive integrated moving average (ARIMA) model forecast foodborne disease incidence in Shenzhen city and help guide efforts prevent disease. data of diseases comes from infectious diarrhea surveillance network, community student network. January 2012 December 2017 was used for model-constructing, while 2018 model-validating. mean absolute perc...