Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In par...

Now-a-days, different sectors of the economy are being significantly affected by the electricity variable. In this research, we analyzed the monthly electricity consumption in Pakistan for the period of January 1990 through December 2011, using linear and non linear modeling techniques. They include ARIMA, Seasonal ARIMA (SARIMA) and ARCH/GARCH models. Electricity consumption model reveals a si...

The electric power load forecasting is critical for stable electric power system supply. In this paper, a seasonal ARIMA model was used to effectively forecast power load data characterized using periodicity. A numerical example reveals that the seasonal ARIMA model effectively forecast periodic power load.

This study compares X-12-ARIMA and MING, two new seasonal adjustment methods designed to handle outliers and structural changes in a time series. X-12-ARIMA is a successor to the X-ll-ARIMA seasonal adjustment method, and is being developed at the U.S. Bureau of the Census (Findley et al. (1988)). MING is a “Mixture based Non-Gaussian” method for sea* sonal adjustment using time series structur...

This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA) models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI). About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR) data were selected to develop the ARIMA models from the er...

Kalman filters and ARIMA models provide optimum control and evaluation techniques (in a minimum squared error sense) for clocks and precision oscillators. Typically, before the models can be used, an analyeie of data provides estimates of the model parameters (e.g., the phi's and theta's for an ARIMA model). These model parameters are often evaluated in a batch mode on a computer after a large ...