Quantitative Methods Dr. Aravind Banakar –

Quantitative Methods Finoplastika Industries Ltd, Nigeria (20 Marks) Time series analysis has two important aims: 1) recognizing the quality of the phenomenon shown by the series of studies, and 2) Both the aims need the plan of the viewed time series data is recognized and somewhat officially explained: A time series is said to be a 'collection of observations made in sequence with time'. For example: recording level of daily rainfall, periodical total domestic product of US, and monthly strength of the. workers in Marine Corps for a specific rank and MOS. The evaluation of time series gives instruments for picking a symbolic model and delivering forecasts. There are two sorts of times series data: Continuous: in this the data consists of study at every moment, for example, seismic movement recorded on a seismogram. Discrete: the data contains recordings taken at different periods,like, statistics of each month crime.

Until the data is absolutely haphazard, studies in time series are usually related to each and the following studies could be partly ascertain by the last values. For instance, the reasons pertaining to the meteorology which have an effect on the temperature for any given day tend to have some affect on the next day's climate. Hence, the observations of the past temperature are helpful for predicting temperatures for the following days. A time series can be deterministic if there are no haphazard or feasible features but goes in a set and foreseeable manner. The data gathered during the classical physics experiment like showing Newton's Law of Motion, is one example of a deterministic time series. The stochastic type of series is more appropriate to the econometric function. Stochastic variables contain undefined or arbitrary viewpoint. Though the worth of each study cannot be precisely foreseen, calculating the various observations could follow the expected method. These methods can be explained through the statistical models.

According to these models, studies differ erratically on the underlying mean value which is the role of time. Time series data can be put in the following categories: one or more performance factors; trend, seasonality, cyclical function and random sound. Various kinds of time series predicting models give forecasts through extrapolating the previous performance of the values of a specified \'l!riable of interest. Consecutive study in econometric times series are generally not free and forecast can be made on the basis of last observations. Although precise predictions can be made with deterministic time series, predictions of stochastic time series are restricted to 'conditional statements regarding the future on the basis of particular hypothesis.' Armstrong (2001) says, "The basic Assumption is that the variable ui!! continue in the future as it has behaved in the past. " Particularly, the time series predictions are suitable for stochastic type of data in which the fundamental root cause of variation like, trend, cyclical performance, seasonality, and uneven variations, do not change radically m time. Therefore, modeling is considered to be more suitable temporarily instead of permanent predictions.

Answer the following question. Q1.Write briefly on time-series analysis. (Hint: recognizing the quality of the phenomenon shown by the series of studies, and, both the aims need the plan of the viewed time series data is recognized and somewhat officially explained)

Global Study Solutions Dr. Aravind Banakar –