Misplaced Pages

#767232

45-469: Z1 , Z-1 , or Z.1 may refer to: Z.1 or the Flow of Funds , a U.S. government fiscal report Z.1, an anti-tank barrier known as Admiralty scaffolding Z-1 (band) , a Japanese idol group Z1 class Melbourne tram Z-1 (comics) , a DC comics character Z1 (computer) , a mechanical computer designed by Konrad Zuse from 1935 to 1936 Z1 Battle Royale ,

90-596: A function among a well-defined class that closely matches ("approximates") a target function in a task-specific way. One can distinguish two major classes of function approximation problems: First, for known target functions, approximation theory is the branch of numerical analysis that investigates how certain known functions (for example, special functions ) can be approximated by a specific class of functions (for example, polynomials or rational functions ) that often have desirable properties (inexpensive computation, continuity, integral and limit values, etc.). Second,

135-508: A 2018 video game Z1 TV , a Czech TV channel Z1 Zagrebačka Televizija , a Croatian regional television network Z-1 Suit , an experimental space suit AEG Z.1 , a German aircraft built before World War I from Zagreb BMW Z1 , a two-seat roadster German destroyer Z1  Leberecht Maass Great Northern Railway Z-1 class , a class of electric locomotives used by the Great Northern Railway (U.S.) NER Class Z1 ,

180-472: A causal effect on the observed series): the distinction from the multivariate case is that the forcing series may be deterministic or under the experimenter's control. For these models, the acronyms are extended with a final "X" for "exogenous". Non-linear dependence of the level of a series on previous data points is of interest, partly because of the possibility of producing a chaotic time series. However, more importantly, empirical investigations can indicate

225-448: A certain point in time. An equivalent effect may be achieved in the time domain, as in a Kalman filter ; see filtering and smoothing for more techniques. Other related techniques include: Curve fitting is the process of constructing a curve , or mathematical function , that has the best fit to a series of data points, possibly subject to constraints. Curve fitting can involve either interpolation , where an exact fit to

270-571: A certain structure which can be described using a small number of parameters (for example, using an autoregressive or moving-average model ). In these approaches, the task is to estimate the parameters of the model that describes the stochastic process. By contrast, non-parametric approaches explicitly estimate the covariance or the spectrum of the process without assuming that the process has any particular structure. Methods of time series analysis may also be divided into linear and non-linear , and univariate and multivariate . A time series

315-698: A class of British steam locomotives (redesignated class Z in 1914) Goodyear Z-1, N-class blimps of the U.S. Navy HZ-1 Aerocycle , an experimental U.S. Army flying platform of the 1960s Kawasaki Z1 , a motorcycle Sony HVR-Z1 , a HDV format camcorder manufactured by Sony Sony Xperia Z1 , a smartphone manufactured by Sony Zork I , an interactive fiction computer game the train code for high-speed train between Shanghai and Beijing Z1 Digital Studio , an international digital product studio based in Seville See also [ edit ] 1Z (disambiguation) [REDACTED] Topics referred to by

360-418: A function where no data are available, and to summarize the relationships among two or more variables. Extrapolation refers to the use of a fitted curve beyond the range of the observed data, and is subject to a degree of uncertainty since it may reflect the method used to construct the curve as much as it reflects the observed data. For processes that are expected to generally grow in magnitude one of

405-579: A given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility ). Time series analysis can be applied to real-valued , continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language ). Methods for time series analysis may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and wavelet analysis ;

450-475: A given time series, attempting to illustrate time dependence at multiple scales. See also Markov switching multifractal (MSMF) techniques for modeling volatility evolution. A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. An HMM can be considered as the simplest dynamic Bayesian network . HMM models are widely used in speech recognition , for translating

495-450: A means of transferring knowledge about a sample of a population to the whole population, and to other related populations, which is not necessarily the same as prediction over time. When information is transferred across time, often to specific points in time, the process is known as forecasting . Assigning time series pattern to a specific category, for example identify a word based on series of hand movements in sign language . Splitting

SECTION 10

#1732852355768

540-516: A publication called the Z.1 Statistical Release. Current and historical releases available in PDF, CSV, or XML format. Data frequency is annual from yearend 1945 and quarterly beginning in 1952Q1. Detailed interactive documentation is also available. The flow of funds accounts follow naturally from double-entry bookkeeping ; every financial asset is also a liability of some domestic or foreign human entity. A fundamental fact about any economic sector

585-530: A regular time series is manually with a line chart . The datagraphic shows tuberculosis deaths in the United States, along with the yearly change and the percentage change from year to year. The total number of deaths declined in every year until the mid-1980s, after which there were occasional increases, often proportionately - but not absolutely - quite large. A study of corporate data analysts found two challenges to exploratory time series analysis: discovering

630-418: A single series. Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies , in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where

675-951: A time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides , counts of sunspots , and the daily closing value of the Dow Jones Industrial Average . A time series is very frequently plotted via a run chart (which is a temporal line chart ). Time series are used in statistics , signal processing , pattern recognition , econometrics , mathematical finance , weather forecasting , earthquake prediction , electroencephalography , control engineering , astronomy , communications engineering , and largely in any domain of applied science and engineering which involves temporal measurements. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of

720-406: A time series of spoken words into text. Many of these models are collected in the python package sktime . A number of different notations are in use for time-series analysis. A common notation specifying a time series X that is indexed by the natural numbers is written Another common notation is where T is the index set . There are two sets of conditions under which much of the theory

765-418: A time-series into a sequence of segments. It is often the case that a time-series can be represented as a sequence of individual segments, each with its own characteristic properties. For example, the audio signal from a conference call can be partitioned into pieces corresponding to the times during which each person was speaking. In time-series segmentation, the goal is to identify the segment boundary points in

810-416: A unified treatment in statistical learning theory , where they are viewed as supervised learning problems. In statistics , prediction is a part of statistical inference . One particular approach to such inference is known as predictive inference , but the prediction can be undertaken within any of the several approaches to statistical inference. Indeed, one description of statistics is that it provides

855-482: Is a time series data set candidate. If determining a unique record requires a time data field and an additional identifier which is unrelated to time (e.g. student ID, stock symbol, country code), then it is panel data candidate. If the differentiation lies on the non-time identifier, then the data set is a cross-sectional data set candidate. There are several types of motivation and data analysis available for time series which are appropriate for different purposes. In

900-419: Is built: Ergodicity implies stationarity, but the converse is not necessarily the case. Stationarity is usually classified into strict stationarity and wide-sense or second-order stationarity . Both models and applications can be developed under each of these conditions, although the models in the latter case might be considered as only partly specified. In addition, time-series analysis can be applied where

945-416: Is closely related to interpolation is the approximation of a complicated function by a simple function (also called regression ). The main difference between regression and interpolation is that polynomial regression gives a single polynomial that models the entire data set. Spline interpolation, however, yield a piecewise continuous function composed of many polynomials to model the data set. Extrapolation

SECTION 20

#1732852355768

990-404: Is different from Wikidata All article disambiguation pages All disambiguation pages Flow of Funds The sectors and instruments are listed below. These balance sheets measure levels of assets and liabilities. From each balance sheet a corresponding flows statement can be derived by subtracting the levels data for the preceding period from the data for the current period. (In

1035-641: Is its balance sheet, a breakdown of its physical and financial assets, and of its liabilities. The only physical assets noted in the FF accounts are those of private nonfinancial sectors. Nonfinancial sectors: Financial sector: The UK flow of funds accounts are prepared by the Office for National Statistics in a series of matrices. The first tables will be published in Blue Book 2014 , to be released in September 2014. They contain

1080-400: Is one type of panel data . Panel data is the general class, a multidimensional data set, whereas a time series data set is a one-dimensional panel (as is a cross-sectional dataset ). A data set may exhibit characteristics of both panel data and time series data. One way to tell is to ask what makes one data record unique from the other records. If the answer is the time data field, then this

1125-406: Is the process of estimating, beyond the original observation range, the value of a variable on the basis of its relationship with another variable. It is similar to interpolation , which produces estimates between known observations, but extrapolation is subject to greater uncertainty and a higher risk of producing meaningless results. In general, a function approximation problem asks us to select

1170-450: The codomain (range or target set) of g is a finite set, one is dealing with a classification problem instead. A related problem of online time series approximation is to summarize the data in one-pass and construct an approximate representation that can support a variety of time series queries with bounds on worst-case error. To some extent, the different problems ( regression , classification , fitness approximation ) have received

1215-479: The advantage of using predictions derived from non-linear models, over those from linear models, as for example in nonlinear autoregressive exogenous models . Further references on nonlinear time series analysis: (Kantz and Schreiber), and (Abarbanel) Among other types of non-linear time series models, there are models to represent the changes of variance over time ( heteroskedasticity ). These models represent autoregressive conditional heteroskedasticity (ARCH) and

1260-483: The available information ("reading between the lines"). Interpolation is useful where the data surrounding the missing data is available and its trend, seasonality, and longer-term cycles are known. This is often done by using a related series known for all relevant dates. Alternatively polynomial interpolation or spline interpolation is used where piecewise polynomial functions are fitted in time intervals such that they fit smoothly together. A different problem which

1305-668: The collection comprises a wide variety of representation ( GARCH , TARCH, EGARCH, FIGARCH, CGARCH, etc.). Here changes in variability are related to, or predicted by, recent past values of the observed series. This is in contrast to other possible representations of locally varying variability, where the variability might be modelled as being driven by a separate time-varying process, as in a doubly stochastic model . In recent work on model-free analyses, wavelet transform based methods (for example locally stationary wavelets and wavelet decomposed neural networks) have gained favor. Multiscale (often referred to as multiresolution) techniques decompose

1350-556: The context of statistics , econometrics , quantitative finance , seismology , meteorology , and geophysics the primary goal of time series analysis is forecasting . In the context of signal processing , control engineering and communication engineering it is used for signal detection. Other applications are in data mining , pattern recognition and machine learning , where time series analysis can be used for clustering , classification , query by content, anomaly detection as well as forecasting . A simple way to examine

1395-421: The curves in the graphic (and many others) can be fitted by estimating their parameters. The construction of economic time series involves the estimation of some components for some dates by interpolation between values ("benchmarks") for earlier and later dates. Interpolation is estimation of an unknown quantity between two known quantities (historical data), or drawing conclusions about missing information from

Z1 - Misplaced Pages Continue

1440-402: The data is required, or smoothing , in which a "smooth" function is constructed that approximately fits the data. A related topic is regression analysis , which focuses more on questions of statistical inference such as how much uncertainty is present in a curve that is fit to data observed with random errors. Fitted curves can be used as an aid for data visualization, to infer values of

1485-470: The data. Time series forecasting is the use of a model to predict future values based on previously observed values. Generally, time series data is modelled as a stochastic process . While regression analysis is often employed in such a way as to test relationships between one or more different time series, this type of analysis is not usually called "time series analysis", which refers in particular to relationships between different points in time within

1530-552: The feature extraction using chunking with sliding windows. It was found that the cluster centers (the average of the time series in a cluster - also a time series) follow an arbitrarily shifted sine pattern (regardless of the dataset, even on realizations of a random walk ). This means that the found cluster centers are non-descriptive for the dataset because the cluster centers are always nonrepresentative sine waves. Models for time series data can have many forms and represent different stochastic processes . When modeling variations in

1575-481: The former three. Extensions of these classes to deal with vector-valued data are available under the heading of multivariate time-series models and sometimes the preceding acronyms are extended by including an initial "V" for "vector", as in VAR for vector autoregression . An additional set of extensions of these models is available for use where the observed time-series is driven by some "forcing" time-series (which may not have

1620-446: The latter include auto-correlation and cross-correlation analysis. In the time domain, correlation and analysis can be made in a filter-like manner using scaled correlation , thereby mitigating the need to operate in the frequency domain. Additionally, time series analysis techniques may be divided into parametric and non-parametric methods. The parametric approaches assume that the underlying stationary stochastic process has

1665-465: The level of a process, three broad classes of practical importance are the autoregressive (AR) models, the integrated (I) models, and the moving-average (MA) models. These three classes depend linearly on previous data points. Combinations of these ideas produce autoregressive moving-average (ARMA) and autoregressive integrated moving-average (ARIMA) models. The autoregressive fractionally integrated moving-average (ARFIMA) model generalizes

1710-451: The observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for

1755-447: The same term This disambiguation page lists articles associated with the same title formed as a letter–number combination. If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=Z1&oldid=1234666769 " Category : Letter–number combination disambiguation pages Hidden categories: Short description

1800-466: The sectors and instruments shown below: Monetary Gold and Special Drawing Rights Currency and Deposits Debt securities Loans Equity and investment fund shares/units Insurance technical reserves Financial derivates and employee stock options Other accounts payable / receivable Time series In mathematics , a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly,

1845-573: The series are seasonally stationary or non-stationary. Situations where the amplitudes of frequency components change with time can be dealt with in time-frequency analysis which makes use of a time–frequency representation of a time-series or signal. Tools for investigating time-series data include: Time-series metrics or features that can be used for time series classification or regression analysis : Time series can be visualized with two categories of chart: Overlapping Charts and Separated Charts. Overlapping Charts display all-time series on

Z1 - Misplaced Pages Continue

1890-639: The shape of interesting patterns, and finding an explanation for these patterns. Visual tools that represent time series data as heat map matrices can help overcome these challenges. This approach may be based on harmonic analysis and filtering of signals in the frequency domain using the Fourier transform , and spectral density estimation . Its development was significantly accelerated during World War II by mathematician Norbert Wiener , electrical engineers Rudolf E. Kálmán , Dennis Gabor and others for filtering signals from noise and predicting signal values at

1935-558: The statistical analysis of time series , this operation is known as " first differencing .") The change in a level item between two adjacent periods is known as a "fund flow"; hence the name for these accounts. The flow of funds (FOF) accounts of the United States are prepared by the Flow of Funds section of the Board of Governors of the Federal Reserve System and are published quarterly in

1980-454: The target function, call it g , may be unknown; instead of an explicit formula, only a set of points (a time series) of the form ( x , g ( x )) is provided. Depending on the structure of the domain and codomain of g , several techniques for approximating g may be applicable. For example, if g is an operation on the real numbers , techniques of interpolation , extrapolation , regression analysis , and curve fitting can be used. If

2025-499: The time-series, and to characterize the dynamical properties associated with each segment. One can approach this problem using change-point detection , or by modeling the time-series as a more sophisticated system, such as a Markov jump linear system. Time series data may be clustered, however special care has to be taken when considering subsequence clustering. Time series clustering may be split into Subsequence time series clustering resulted in unstable (random) clusters induced by

#767232