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Topic: Time series

Related Topics

Autoregressive

Autoregressive moving average model

Moving average

Artificial neural network

Frequency domain

Mathematical model

Autoregressive conditional heteroskedasticity

Markov process

Prediction

Fourier transform

White noise

Stochastic process

Deterministic algorithm

Scientific determinism

Heteroskedasticity

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	Time Series Analysis: Statnotes, from North Carolina State University, Public Administration Program
		Dependence in a time series refers to serial dependence -- that is, the correlation of observations of one variable at one point in time with observations of the same variable at prior time points.
		It is the object of many forms of time series analysis to identify the type of dependency which exists, then to create mathematical formulae which emulate the dependence, and only then to proceed with forecasting or policy analysis.
		Autocorrelation is the serial correlation of error terms for estimates of a time series variable, resulting from the fact that the value of a datum in time t in the series is dependent on the value of that datum in time t - 1 (or some higher lag).
	www2.chass.ncsu.edu /garson/pa765/time.htm (9082 words)

	Time Series Analysis
		Time series analysis is generally used when there are 50 or more data points in a series.
		Time series are analyzed in order to understand the underlying structure and function that produce the observations.
		Time series are very complex because each observation is somewhat dependent upon the previous observation, and often is influenced by more than one previous observation.
	userwww.sfsu.edu /~efc/classes/biol710/timeseries/timeseries1.htm (3109 words)

	Operation - Operations Management / Industrial Engineering
		An example of a time series for 25 periods is plotted in Fig.
		The model supposes that there are two components of variability for the time series; the variation of the mean value with time and the noise.
		For time series however, most methods recognize that data from recent times are more representative of current conditions than data from times well in the past.
	www.me.utexas.edu /~jensen/ORMM/omie/operation/unit/forecast/time_series.html (1484 words)

	Time Series Analysis
		Time Series Analysis (TSA) is popular for estimating the process that underlies some output, or for forecasting from some observed behaviour over time.
		Can linkages be easily established between components in separate time series analyses, or does the method suffer from the "little room" approach - the problem has been removed from its surroundings and taken to a mathematically small room, where it has been dissected under controlled conditions in isolation from its context.
		The analyst can examine all of the components of the time series, make connections with other knowledge to be used in the time series analysis, or use the result of the time series analysis in some other analysis, or the whole operation can be run in batch, still with connection to other knowledge.
	www.inteng.com.au /time_series_analysis.htm (1797 words)

	Time Series
		Time series analysis package containin 78 procedures, including seasonal arima models (also with some parameters set equal to zero), substet ar models, estimating missing values in time series, spectrum estimates, and simulation of arma models with normally distributed errors.
		SS Univariate Time Series (Shuetrim Mar96) allows full maximum likelihood estimation of Structural Times Series models in state space form (SSF).
		Time Series Standard Errors (Roncalli Oct96) computes the standard errors for forecast error impulse, orthogonalized impulse and forecast error variance decomposition.
	www.american.edu /academic.depts/cas/econ/gaussres/timeseri/timeseri.htm (437 words)

	Time Series Analysis and Forecasting
		The methodologies of time series analysis and forecasting using ARIMA and transfer function models developed in the previous chapters are all useful for time series data mining, particularly automatic time series modeling and outlier detection.
		In time series modeling, however, data cannot be arbitrarily deleted during model estimation due to the existence of serial correlation or seasonality.
		In time series analysis, it is not uncommon for the pattern or the relationship of the time series to be temporarily disrupted by outliers or structural changes.
	www.scausa.com /tsfbook.htm (4245 words)

	Camille Utterback
		The Liquid Time Series explores how the concept of 'point of view' is predicated on embodied existence.
		The result of this exploration, however, is a series of pieces in which imagery of time, as well as space, is disrupted by users' motions.
		This piece destabilizes a basic premise of time based media—that the unit of recording is also the unit of playback.
	www.camilleutterback.com /liquidtime.html (401 words)

	Time Series Analysis, Stochastic Process
		A time series is a series of observations made over some period of time [—α, 0].
		Time series analysis is the fitting of stochastic processes to time series.
		The purpose of time series analysis is to study the more interesting case in which terms corresponding to different points in time have interdependencies.
	www.riskglossary.com /articles/time_series_stochastic_process.htm (1195 words)

	Statistics Glossary - time series data
		A time series is a sequence of observations which are ordered in time (or space).
		In some time series, seasonal variation is so strong it obscures any trends or cycles which are very important for the understanding of the process being observed.
		Exponential smoothing is a smoothing technique used to reduce irregularities (random fluctuations) in time series data, thus providing a clearer view of the true underlying behaviour of the series.
	www.stats.gla.ac.uk /steps/glossary/time_series.html (1003 words)

	Time Series Analysis
		There are two main goals of time series analysis: (a) identifying the nature of the phenomenon represented by the sequence of observations, and (b) forecasting (predicting future values of the time series variable).
		As in most other analyses, in time series analysis it is assumed that the data consist of a systematic pattern (usually a set of identifiable components) and random noise (error) which usually makes the pattern difficult to identify.
		In plots of series, the distinguishing characteristic between these two types of seasonal components is that in the additive case, the series shows steady seasonal fluctuations, regardless of the overall level of the series; in the multiplicative case, the size of the seasonal fluctuations vary, depending on the overall level of the series.
	www.statsoft.com /textbook/sttimser.html (13554 words)

	Time Series Analysis for Business Forecasting
		Almost all time series published by the US government are already deseasonalized using the seasonal index to unmasking the underlying trends in the data, which could have been caused by the seasonality factor.
		It is the pattern generated by the time series and not necessarily the individual data values that offers to the manager who is an observer, a planner, or a controller of the system.
		It is a way to decompose a given series into stationary and non-stationary components in such a way that their sum of squares of the series from the non-stationary component is minimum with a penalty on changes to the derivatives of the non-stationary component.
	home.ubalt.edu /ntsbarsh/stat-data/Forecast.htm (12753 words)

	Time Series Forecasting with Neural Networks
		Application of neural networks in time series forecasting [9, 10, 12, 13] is based on the ability of neural networks to approximate nonlinear functions.
		This analysis is mainly used in detecting the autocorrelations between successive observations of time series, and used in the well-known ARIMA models with Box-Jenkins methods that are very efficient in forecasting linear time series [7].
		In time series forecasting using statistical approaches, the autocorrelation function is extremely useful in obtaining a partial description of a time series for forecasting [7].
	www.complexity.org.au /ci/vol02/cmxhk/cmxhk.html (2454 words)

	Time Series Analysis
		The time series window is divided into 3 panes: the left one is for changing the window properties and for selecting series (variables), the right upper is for displaying several time series and the right lower is for displaying the current series.
		Time series data can be transformed by calculating differences, smoothing, trend suppression, using a number of functions, etc. The menu Transformations contains commands for creating new time series based on values of selected series.
		Series to be taken for calculation are selected in the dialogue box "Selection of series" (see section "Preparation of Analysis").
	www.unesco.org /webworld/portal/idams/html/english/E1timesi.htm (1268 words)

	Microsoft Time Series Algorithm
		For example, because the data in the diagram shows the series for historical and forecasted bicycle sales over a period of several months, the date column is the case series.
		A time series algorithm requires that the column or columns to be predicted must be continuous.
		Specifies the minimum number of time slices that are required to generate a split in each time series tree.
	msdn.microsoft.com /en-us/library/ms174923.aspx (1104 words)

	Time series analysis and forecasting, Caterpillar SSA method
		Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
		Thus, the result of the 'Caterpillar'-SSA processing is a decomposition of the time series into several components, which can often be identified as trends, seasonalities and other oscillatory series, or noise components.
		The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and nonstationary, almost deterministic and noisy time series are to be analyzed.
	gistatgroup.com (313 words)

	Time - Notes on McTaggart, "The Unreality of Time"
		He suggests that without the A-series we can't make sense either of an event ceasing to exist and another event beginning to exist, or of an event changing from an event of one kind to an event of another.
		The verb 'to be' is normally tensed, but tense presupposes the A-series, so to do a serious job of restricting ourselves to the B-series, we have to abandon tense.
		If time requires the A-series, but expressing all the expressible facts about the world does not require the A-series, then the world is not temporal.
	www.trinity.edu /cbrown/time/mctaggart.html (1110 words)

	Time (Stanford Encyclopedia of Philosophy)
		Other issues concerning the topology of time include (i) whether time is branching or non-branching, (ii) whether time is open or closed, (iii) whether there can be two or more disconnected time streams, (iv) whether time has an intrinsic direction, and (v) whether time is dense or continuous or neither.
		The question of whether there could be time without change has been debated by philosophers since the days of Plato and Aristotle, and has traditionally been thought to be closely tied to the question of whether time exists independently of the events that occur in time.
		His reason for this is that change (he says) is essential to time, and the B series without the A series does not involve genuine change (since B series positions are forever “fixed,” whereas A series positions are constantly changing).
	plato.stanford.edu /entries/time (5500 words)

	InfoVis Cyberinfrastructure - Visualizing Time Series Data
		A time series is a sequence of events/observations which are ordered in one dimension- time.
		Time series data can be continuous, i.e., there is an observation at every instant of time (see figure below), or discrete, i.e., observations exist for regularly or irregularly spaced intervals.
		Time series are recorded, analyzed and used in diverse domains of science.
	iv.slis.indiana.edu /lm/lm-time-series.html#Usage_Hints (993 words)

	TIME-SERIES FORECASTING WITH FEED-FORWARD NEURAL NETWORKS:
		Difficulties inherent in time series forecasting and the importance of time series forecasting are presented next.
		Zhang and Thearling (1994) present another empirical evaluation of time series forecasting techniques, specifically feed-forward neural networks and memory-based reasoning, which is a form of k-nearest-neighbor search.
		Another more complex approach to time series forecasting can be found in Lawrence, Tsoi, and Giles (1996).
	www.karlbranting.net /papers/plummer/Paper_7_12_00.htm (3607 words)

	Time Series: A Fully Integrated Environment for Time-Dependent Data Analysis
		Time Series performs univariate and multivariate analysis and enables you to explore both stationary and nonstationary models.
		After reading in and plotting your data, use the built-in Time Series transforms for linear filtering, simple exponential smoothing, differencing, moving averages, and more to transform your raw data into a form suitable for modeling.
		This package is also an ideal instructional tool with its description of the fundamentals of time series analysis and its clear, concise examples.
	www.wolfram.com /products/applications/timeseries (295 words)

	Definition of Time series
		In statistics and signal processing, a time series is a sequence of data points, measured typically at successive times, spaced apart at uniform time intervals.
		Time series prediction is the use of a model to predict future events based on known past events: to predict future data points before they are measured.
		Performing a Fourier transform to investigate the series in the frequency domain.
	www.wordiq.com /definition/Time_series (268 words)

	Time Series Analysis and Forecasting
		Smoothing - a variety of smoothers are available to estimate the underlying trend in a time series.
		Characterizing a time series involves estimating not only a mean and standard deviation but also the correlations between observations separated in time.
		A common goal of time series analysis is extrapolating past behavior into the future.
	www.statgraphics.com /time_series_analysis.htm (455 words)

	Aptech Systems, Inc. - Time Series
		The Time Series module includes tools for estimating general ARIMA (p,d,q) models using an exact MLE procedure based on C. Ansley (Biometrika 1979, pp.
		The assumption is that there are multiple observations over time on a set of cross-sectional units (e.g., people, firms, countries).
		While these models were devised to study a cross-section of units over multiple time periods, they also correspond to models in which there are data for groups such as schools or firms with measurements on multiple observations within the group (e.g., students, teachers, employees).
	www.aptech.com /ga_ts.html (400 words)

	R Time Series Tutorial
		Please note that this is not a lesson in time series analysis.
		Another good read for exploring time series is Econometrics in R (a pdf file).
		It's time to move on to time series.
	www.stat.pitt.edu /stoffer/tsa2/R_time_series_quick_fix.htm (2520 words)

	Non-Linear Time-Series Prediction by Systematic Data Exploration on a Massively Parallel Computer
		For serial computers, a K-D Tree representation [10] can effectively reduce search complexity for the nearest neighbors when there is structure in data.
		If at any time the partial distance was greater than the the (square of) the furthest of the current set of K-nearest neighbors, that data point was discarded.
		For many times series problems, N can be quite large and thus it is often impossible to compute this for the entire series.
	www.thearling.com /text/sfitr/sfitr.htm (5796 words)

	STSA - The Time Series Analysis Toolbox
		The STSA toolbox aids in the rapid solution of many time series problems, some of which cannot be easily dealt with using a canned program or are not directly available in most analysis software packages.
		Statistical estimation of parameters of time series models, either linear or nonlinear, with several types of optimization methods.
		The STSA toolbox offers a complete solution for doing time series analysis: from simulation, to estimation, to residual analysis, to forecasting, complete with many auxiliary statistical functions for descriptive statistics, statistical plots, trend forecasting, time series smoothing and others.
	www.omatrix.com /stsa.html (1041 words)

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