Related Articles ( ARIMA )
Annual Forecasting Using a Hybrid Approach
In this paper, we used a hybrid method based on wavelet transforms and ARIMA models and applied on the time series annual data of rain precipitation in the Province of Erbil-Iraq in millimeters. A sample size has been taken during the period 1970 - 2014.We intended to obtain the ability to explain how ...
Forecasting Wholesale Prices of Maize in Tanzania Using Arima Model
This paper aimed at modeling and forecasting wholesale prices of maize in Tanzania using Autoregressive Integrated Moving average model for data from February 2004 to August 2017 obtained from the Bank of Tanzania. Maize crop growers lack fundamental knowledge on which periods do prices of their harvests ...
Forecasting international tourist arrivals in zanzibar using box – jenkins SARIMA model
The arrival of international tourists contributes to the generation of foreign currencies and creates employment opportunities to the local people. Modelling and forecasting tourist arrivals plays a major role in tourism planning and marketing and therefore crucial for policy decision-making towards ...
Forecasting of Covid-19 deaths in South Africa using the autoregressive integrated moving average time series model
Covid-19 epidemic continues to escalate globally posing life threats to humans. Time series modeling plays a key role for the prediction of data-driven scenarios. A case for Covid-19 pandemic future numbers occurrence is one of the open forecasting scenario for application of the time series modeling. ...
A hybrid Modeling and Forecasting of Carbon dioxide Emissions in Tanzania
Carbon dioxide (CO2) emissions is among of global environmental pollutants contributing to climate change. The current study aims to create an Autoregressive Integrated Moving Average with external factors (ARIMAX) model to predict CO2 emissions in Tanzania. In this study, an Autoregressive ...
Detection of Outlier in Time Series with Application to Dohuk Dam Using the SCA Statistical System
Outliers are data points or observations that stand out significantly from the rest of the group in terms of size or frequency. They are also referred to as "abnormal data". Before fitting a forecasting model, outliers are often eliminated from the data set, or if not removed, the forecasting model ...