what is the use of time series?
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It is used, to gather information of useful events that are happening.\n\nThen, it is interpreted to come up with accurate findings and conclusions.
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Calculating money, age, time, products that are manufactured, statistics, etc.
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What is it supposed to be used for?
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most statistics follow a pattern, whether it be linear, quadratic, or whatever. Once you have enough data to establish a pattern, you can accurately predict the remainder of the line.\n\nFor instance, you have a line that looks to be half a parabola. Once it starts going back up, you can be pretty certain of where it's going to be.
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Learn stochastic calculus and maths modelization first. It's your only way to making consistent profit on the FX. Charts are a great way to "learn" except they have no PREDICTIVE value whatsoever... sounds familiar? ;)
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Time Series Data Library  \nTime Series Data Library. This is a collection of about 800 time series drawn from many different fields. To find a series, browse the subjects on the right or try the site search below. ... The Time Series Data Library was created by Rob Hyndman ... Sales. Simulated series. Sport. Transport & Tourism. Tree-rings ...
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There are no proven "automatic" techniques to identify trend components in the time series data; however, as long as the trend is monotonous (consistently increasing or decreasing) that part of data analysis is typically not very difficult. If the time series data contain considerable error, then the first step in the process of trend identification is smoothing. \n\nSmoothing. Smoothing always involves some form of local averaging of data such that the nonsystematic components of individual observations cancel each other out. The most common technique is moving average smoothing which replaces each element of the series by either the simple or weighted average of n surrounding elements, where n is the width of the smoothing "window" (see Box & Jenkins, 1976; Velleman & Hoaglin, 1981). Medians can be used instead of means. The main advantage of median as compared to moving average smoothing is that its results are less biased by outliers (within the smoothing window). Thus, if there are outliers in the data (e.g., due to measurement errors), median smoothing typically produces smoother or at least more "reliable" curves than moving average based on the same window width. The main disadvantage of median smoothing is that in the absence of clear outliers it may produce more "jagged" curves than moving average and it does not allow for weighting. \n\nIn the relatively less common cases (in time series data), when the measurement error is very large, the distance weighted least squares smoothing or negative exponentially weighted smoothing techniques can be used. All those methods will filter out the noise and convert the data into a smooth curve that is relatively unbiased by outliers (see the respective sections on each of those methods for more details). Series with relatively few and systematically distributed points can be smoothed with bicubic splines.\n\nAs far as text books on this topic there are number of books.  For example I read the following book:\nTime Series Analysis and Its Applications\nby Robert H Shumway, David S Stoffer\n\nor search over net "Trend analyses of time series"
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The best way to work this out would be to plot a speed time graph for the information.\n\nOr you could express it as a simultaneous equation.
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Fourier series are used throughout mathematics, physics and engineering. \n\nAll of the analysis of power systems and alternating current involve fourier series.\n\nRadio transmissions and frequency bands use fourier series\n\nSolving the wave equation, the heat equation, Maxwell's equation etc all can use fourier series to studies waves and heat and electromagnetism.\n\nFourier series is a standard tool in mathematics, in places like number theory (involved in producing secure communications).\n\nSonar systems use fourier series. Navigating submarines and finding other vessels is done use fourier series.\n\nRadar systems use fourier series. They are used for weather forecasting and speed guns used by the police.\n\nRepresenting a solution as a sum of sines and cosines is valuable whenever one is dealing with simple harmonic oscillators, which occur throughout physics. \n\nFourier series are the gateway to more sophisticated tools like Fourier transforms and other special function transforms. \n\nObviously, one could make a very long list. But his should give you some idea.
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The Trend Analysis module allows you to plot aggregated response data over time. This is especially valuable, if you are conducting a long running survey and would like to measure differences in perception and responses over time.\n\nThe following data points can be measured (Y-Axis)\n\n   1. Mean and Mean Percentile\n   2. Standard Deviation and Variance\n\nThe "Time Factor" (X-Axis) can have the following granularity\n\n   1. Daily\n   2. Weekly\n   3. Monthly\n   4. Quarterly (Jan-Mar, Apr-Jun, Jul-Sept, Oct-Dec)\n   5. Yearly
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The first uses financial and economic analysis to try and predict the movement of prices.  This could be the general economic data, earnings reports, industry reports, and so on.\n\nThe second uses the time series data of the price and volume and similar things.  It's like staring at the chart of prices and predicting what will happen in the future.  The assumption is that there are patterns in the data.
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If your question is how can you use neural networks in a real-time application the answer is:\n\nYou "train" the neural network on statistically normal data and then you "feed" the real-time data to the neural network and then you have your neural network output.  Unless you have a small amount of data there is always some small yet possibly perceivable processing delay.
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As I know data mining is tool that can help us find hidden patterns for somethings, I can it is discovery driven.\nThe hidden patterns may classified as below:\nAssociations=Occurrences linked to a single event\nSequences=Events are linked over time\nClassifications=Recognizes patterns that describe the group to which an item belongs by examining existing items that have been classified and inferring a set of rules.\nClustering=Finding new ways to organize data into groups\nForecasting=Uses a series of existing values to forecast what other value will be.
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I've already thought about it, and I think the answer is no.\n\nThe most near from social sciences applications is in economics\nwhere chaos theory is useful to measure and estimate movements of economic indicators or values, in short periods of time (as stocks or things like that).\nIn biology, they're using chaos theory for modelling evolution, and in artificial intelligence, it's also useful, but i have never heard about of an application in social science.\n\nWait!  I looked and I found something in Epidemiology!! \nokey, here's the link:\n\nwww.ulg.ac.be/mathgen/CHAOS/\nIJCAS/IJCAS_1_PS_PG_DD.pdf\n\nIt's kind of technical, be sure to have review something about fractals and autosimiliraty before take a look on it.
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More specifically (than the ones already here), the Power Series are used to express any type of equation using polynomials, a Maclaurin Series a special type of Taylor Series where a=0 and the Fourier Series is like the Power Series except it uses trig functions (sin and cos only) to express any type of function.
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(m)\n\nBusiness statistics is the science of good decision making in the face of uncertainty and is used in many disciplines such as financial analysis, econometrics, auditing, production and operations including services improvement, and marketing research. These sources feature regular repetitive publication of series of data. This makes the topic of time series especially important for business statistics. It is also a branch of applied statistics working mostly on data collected as a by-product of doing business or by government agencies. It provides knowledge and skills to interpret and use statistical techniques in a variety of business applications. A typical business statistics course is intended for business majors, and covers statistical study, descriptive statistics (collection, description, analysis, and summary of data), probability, and the binomial and normal distributions, test of hypotheses and confidence intervals, linear regression, and correlation.
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Moving average \n Used in charts and technical analysis, the average of security or commodity prices constructed in a period as short as a few days or as long as several years, and showing trends for the latest interval. As each new variable is included in calculating the average, the last variable of the series is deleted.