Time series

Software: A Programming Approach FETSP * Forecasting Using SAS Forecast Server Software FSTU21 * Using SAS High-Performance Forecasting Software HPF * Modeling Trend, Cycles, and Seasonality in Time Series Data Using PROC UCM LWBARS * Stationarity Testing and Other Time Series Topics LWBADD Market Research * Applied Clustering Techniques CLUS92 * Design of Experiments for Direct Marketing DOEF92 Operations Researcher * Building and Solving Optimization Models with SAS/OR

Big Data in Oil & Gas Sector 1. Introduction Big Data in Oil and Gas industry is not something new. The industry has long dealt with huge amounts of data to make critical decisions over the period of time. For many years energy companies had invested in seismic software, data visualization and other digital tools & technologies for planning and optimization purposes. But now a day, most of the enterprises have started craving a certain desire for better execution of E&P activities. Since the crude

have been faltering in relation to competitors. The original method for forecasting how many transformers they will need to meet said demand was to examine the sales figures of the preceding months as well as the previous two years around the same time and they would hypothesize how many transformer they would need. Although this method proved plausible in previous testing phases there have been instances of under or over stocking. Supplier issues are also beginning to heightened concerns in regards

Statement of Purpose Junru Xia I first discovered my interest in statistics in my sophomore year at the University of California, Berkeley. In a statistics lecture, the professor explained how statistics can be applied in real life by giving an example that Amazon often promotes products by emailing its customers and recommending products according to sophisticated analysis of customers’ search records, as well as their transaction data. Previously having been immersed in the world of abstract statistics

economic situation of New Zealand. The data provided is sourced from Statistics New Zealand Infoshare, recorded from 1988 to 2015 and records quarterly gross weight of exports in tonnes. Plan Using the data provided, I will use NZ Grapher to plot a time series and a decomposition on Auckland Airport Exports from 1988 to 2015. The data will be interpreted taking into account the raw data, trend, residuals, and seasonal effect. A forecast will be made using the

underlying uncertainty. Moreover, it is equally applicable to any kind of entity: deposits, weather, market demand or even human life. In fact forecasting is such a fundamental component of human life that we forecast about self and others all the time. Probably, it stems from the innate human desire to know all the unknown, especially his future. Consequently, more and more techniques of forecasting have been developed- from intuition to rigorous complex mathematical analysis. So why is forecasting

relevant information. • Company marketing research report: The company’s internal data and market knowledge is vital, the most essential data which a a company need is the knowledge of the right product and services that a client needs. Hence time to time market and customer research is required for new item thoughts and in addition expected change in procedures. This sort of data can be costly to assemble

With the similar target, Gordiievych and Shubin (2015) did not give any description of their data. 4.1.2. Different range and size of data For airline prices prediction studies, it is a common practice to use time series data like airline ticket prices. The date ranges of data used by different studies vary from several months to as long as 18 years. For example, Chen et al. (2015) used 110 days of data in their study, and the other study from Zhang et al. (2010)

Production Planning Introduction The intention of this project is to demonstrate the function of production planning in a non - artificial environment. Through this simulation we are able to forecast, with a degree of certainty the monthly requirements for end products, subassemblies, parts and raw materials. We are supplied with information that we are to base our decisions on. The manufacturer depicted in this simulation was actually a General Electric facility that produced black and white

that describes how each of these predictors has affected staffing levels in the past. This equation is used to predict future staffing levels. This is an example of ______________: a) regression analysis b) ratio analysis c) trend analysis d) Markov analysis View Feedback Question 2 5 / 5 points Affirmative action plans and programs do not originate from ________. a) voluntary employer efforts b) court-imposed remedies for discriminatory practices c) consent agreements