Time series analysis

The Study of the ratio analysis technique to financial statements offers potential in expanding insight into specific strengths and weaknesses of a company financial situation. The primary objective of financial analysis is to provide information useful for decision making. 1.1 INTRODUCTION: RATIO ANALYSIS: There are various methods or techniques used in analyzing financial statements, such as comparative statement, trend analysis, common- size statement, schedule of changes in working capital

the highest level of accuracy for sales projections for the upcoming business year 2008. Ted is aware that forecasting can be an expensive undertaking if results are inaccurate, as such he utilized the most accessible work tool, Microsoft Excel time series forecasting method to run several forecasts using the historical sales data from the previous four years. He was however tentative about the results, as he is of the view that economic activity and oil prices plays a significant role in auto parts

Iterations of analysis eliminated data points that were listed as “unusual observations,” or any data point with a large standardized residual. After 5 iterations, the analysis showed improved residual plots. Randomness in the versus fits and versus order plots means that the linear regression model is appropriate for the data; a straight line

ability to decide whether a new set of observation is similar to the same distribution over a time series or not. It is considered for many application domains as a milestone and a watershed to their decision making process. Business and research sectors such as medical, financial, IT, cyber security and even crime investigation and terrorism are interested to invest in this field to have the ability for real time detection of unusual behavior. We are living in an era were we have zillions of data streams

Designers of goods and services use this method to understand their current customers and the buyers they would like to serve. „X Dlephi Method: The Delphi method compiles forecasts through sequential, independent responses by a group of experts to a series of questionnaires. The forecaster compiles and analyses the respondents¡¦ input and develops a new questionnaire for the same group of experts. This sequence works towards consensus that reflects input from all of the experts while preventing any

changes to improve their current supply chain troubles. They should examine how NCO 's marketing department derives their forecasting numbers, and they should try to run a more effective Just-in-Time (JIT) manufacturing process. First, NCO 's marketing section should adopt a combination of the Time series analysis and Qualitative, Grass roots forecasting techniques. If they use historical data along with current market trend inputs from NCO 's sales force, they will be able to prepare a more accurate

(or set of variables) at some future point in time (Goodier, 2010). It can be applied to a number of different situations when there is uncertainty about the future and the data collected can aid in decisions that need to be made (Armstrong, 2001). In relation to healthcare, forecasting models have been used to aid their sector’s departments to plan staff rota schedules, ensuring that a sufficient amount of senior staff are available at any given time throughout the day, week, month and year. As explained

Our propose approach is single database scan approach which all transactions read only one time. Initially, SIL and PTable are empty. At first time interval transaction $\left\{a,b,g,f\right\}$ is read and updates SIL with items $\left\{a\right\}$, $\left\{b\right\}$, $\left\{g\right\}$ and $\left\{f\right\}$ and set their timeset (TS) value 1 which represents occurred time. In the first row of table \ref{Figure:example1} shows SIL and PTable generated after the first timestamp. After second timestamps

are common methods of estimating association between variables, linear trend (slope), and statistical significance in time series data (Baumer & Lauristen, 2010; Hashima & Finkelhor, 1999; Lauristen, Rezey & Heimer, 2013). The many caveats associated with time series data, trend analysis, and linear regression were accounted for preliminary to analysis. It is common for time series data to be highly auto-correlated and thus contain residuals lacking independence (Asteriou & Hall, 2011). The Durbin-Watson

forecasting systems of interrelated time series and for analyzing the dynamic impact of random disturbances on the system of variables. The VAR approach sidesteps the need for structural modeling by treating every endogenous variable in the system as a function of the lagged values of all of the endogenous variables in the system. The vector autoregression (VAR) model is one of the most successful, flexible, and easy to use models for the analysis of multivariate time series. It is a natural extension of