Autoregressive integrated moving average ARIMA Exponential smoothing;

12 often intertwined to generate new models. For example, the autoregressive moving average model ARMA combines the AR model and the MA model. Another example of this is the autoregressive integrated moving average ARIMA model, which combine all three of the models previously mentioned. The most commonly used model for time series data is the autoregressive process. The autoregressive process is a difference equation determined by random variables. The distribution of such random variables is the key component in modeling time series. Data collection techniques for discrete time series can be done in two ways, as described as follows: 1. Through sampling of continuous time series. it means the continuous data is sampled in the same time interval. 2. Through the accumulation of a variable in a period of time. For example, rainfall is usually accumulated over a certain period of time days, months, etc.. Mathematical models for stochastic and deterministic dynamic problem: 1. If the value of a future future value of a time series can be appropriately determined by a mathematical function, eg: Zt = cos 2πft, it can be called deterministic time series. 2. If the future value can only be described in a probability distribution. It can be called as a stochastic time series. The basic model used in the time series analysis is ARIMA that can be expressed by a linear combination of observation variables data and independent random variables distributed normally. Based the describe above, the approaches time series that uses to forecasting electricity demand in Libya are:

1. Autoregressive integrated moving average ARIMA

Autoregressive Integrated Moving Average ARIMA time series is used in various disciplines of studies such as anthropology, business, and criminology and other. The purpose of ARIMA is finding an accurate model that represents the pattern of the past and future of a time series data. It also has varied patterns such as random, trend, cyclical or a combination of these patterns. The time series perpustakaan.uns.ac.id commit to user 13 considered in this paper is Autoregressive Integrated Moving Average ARIMA time series.

2. Exponential smoothing;

Exponential smoothing is probably the widely used class of procedures for smoothing discrete time series in order to forecast the immediate future. This popularity can be attributed to its simplicity, its computational efficiency, the ease of adjusting its responsiveness to changes in the process being forecast, and its reasonable accuracy. The idea of exponential smoothing is to smooth the original series the way the moving average does and to use the smoothed series in forecasting future values of the variable of interest. In exponential smoothing, however, it is need to allow the more recent values of the series to have greater influence on the forecast of future values than the more distant observations. Exponential smoothing is a simple and pragmatic approach to forecasting, whereby the forecast is constructed from an exponentially weighted average of past observations. The largest weight is given to the present observation, less weight to the immediately preceding observation, even less weight to the observation before that, and so on exponential decay of influence of past data Amlabu et al; 2013 . There are a variety of these methods, such as single exponential smoothing, Holt‟s linear method, and Holt-Winters‟ method and their variations. Although still used in several areas of business and eco-nomic forecasting, these are now supplemented by the other four methods mentioned previously. Ostertagová, K. et al. 2011 .

2.4.6. To Make sure Stochasic Time Series Analysis