TIME SERIES ANALYSIS Chapter Three Univariate Time Series Models Chapter Three Univariate time series models c WISE 1 3.1 Preliminaries We denote the univariate time series of interest as yt. • yt is observed for t = 1‚ 2‚ . . . ‚ T ; • y0‚ y−1‚ . . . ‚ y1−p are available; • Ωt−1 the history or information set at time t − 1. Call such a sequence of random variables a time series. Chapter Three Univariate time series models c WISE 2 Martingales Let {yt} denote
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TIME SERIES ANALYSIS Introduction Economic and business time series analysis is a major field of research and application. This analysis method has been used for economic forecasting‚ sales forecasting‚ stock market analysis and company internal control. In this paper‚ we will talk about time series and review techniques that are useful for analyzing time series data. Definition of Time Series and Time Series Analysis Time series is an ordered sequence of values of a variable at equally spaced
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Question 5 - 10 marks (Equity Options) It is January 2nd‚ 2014 and Google Inc. (GOOG) stock is currently trading on the Nasdaq at a price of $1‚105.00 US dollars. Using the information provided below‚ please answer the following questions: (Note: ’Last’ means the last traded price of the put or call option. Use this number for your calculations). Call options: Put options: a) Based on the current stock price‚ which one of the two options is in the money? by how much? (1 marks) b)
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on data from the past and present and analysis of trends. Forecasting entails the use of historic data to determine the direction of future trends. Forecasting is used by companies to determine how to allocate their budgets for an upcoming period of time. This is typically based on demand for the goods and service it offers compared to the cost of producing them. Investors utilize forecasting to determine if events affecting a company‚ such as sales expectations will increase or decrease the price
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Course Outline for Spring 2012‚ Statistics 153: Introduction to Time Series January 16‚ 2012 • Instructor: Aditya Guntuboyina (aditya@stat.berkeley.edu) • Lectures: 12:30 pm to 2 pm on Tuesdays and Thursdays at 160 Dwinelle Hall. • Office Hours: 10 am to 11 am on Tuesdays and Thursdays at 423 Evans Hall. • GSI: Brianna Heggeseth (bhirst@stat.berkeley.edu) • GSI Lab Section: 10 am to 12 pm OR 12 pm to 2 pm on Fridays at 334 Evans Hall (The first section will include a short Introduction
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Time Series Prediction of Earthquake Input by using Soft Computing Hitoshi FURUTA‚ Yasutoshi NOMURA Department of Informatics‚ Kansai University‚ Takatsuki‚ Osaka569-1095‚ Japan nomura@sc.kutc.kansai-u.ac.jp Abstract Time series analysis is one of important issues in science‚ engineering‚ and so on. Up to the present statistical methods[1] such as AR model[2] and Kalman filter[3] have been successfully applied‚ however‚ those statistical methods may have problems for solving highly nonlinear
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.2.3 Time series models Time series is an ordered sequence of values of a variable at equally spaced time intervals. Time series occur frequently when looking at industrial data. The essential difference between modeling data via time series methods and the other methods is that Time series analysis accounts for the fact that data points taken over time may have an internal structure such as autocorrelation‚ trend or seasonal variation that should be accounted for. A Time-series model explains
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report on the time-series analysis of continuously compounded returns for Ford and GM for the periods January 2002 till April 2007 using monthly stock prices. This analysis is aimed at estimating the ARIMA model that provides the best forecast for the series. This paper will be divided into 2 sections; the first section showing the Ford analysis and the second the GM analysis. Section 1: Ford Figure 1: Time series plot for raw Ford data. Figure 1 shows a time series plot of the
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TIME SERIES MODELS Time series analysis provides tools for selecting a model that can be used to forecast of future events. Time series models are based on the assumption that all information needed to generate a forecast is contained in the time series of data. The forecaster looks for patterns in the data and tries to obtain a forecast by projecting that pattern into the future. A forecasting method is a (numerical) procedure for generating a forecast. When such methods are not based upon
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Time Series Regression 3.1 A small regional trucking company has experienced steady growth. Use time series regression to forecast capital needs for the next 2 years. The company’s recent capital needs have been: ══════════════════════════════════════════════ Capital Needs Capital Needs (Thousands Of (Thousands Of Year Dollars) Year Dollars) -------------------------------------------
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