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Time series
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Time series: random data plus trend, with best-fit line and different applied filters
A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, 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.
Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting [1], container shipping freight rate forecasting [2],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 the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called "time series analysis", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.[3] Interrupted time series analysis is the analysis of interventions on a single time 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 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 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[4]).
Contents
[hide]
1 Methods for analysis
2 Time series and panel data
3 Analysis
3.1 Motivation
3.2 Exploratory analysis
3.3 Curve fitting
3.4 Function approximation
3.5 Prediction and forecasting
3.6 Classification
3.7 Signal estimation
3.8 Segmentation
4 Models
4.1 Notation
4.2 Conditions
4.3 Tools
4.4 Measures
5 Visualization
5.1 Overlapping charts
5.2 Separated charts
6 Software
7 See also
8 References
9 Further reading
10 External links
Methods for analysis[edit]
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; 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 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.
Time series and panel data[edit]
A time series 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 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 (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.
Analysis[edit]
There are several types of motivation and data analysis available for time series which are appropriate for different purposes and etc.
Motivation[edit]
In 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 and estimation, while in the context of data mining, pattern recognition and machine learning time series analysis can be used for clustering, classification, query by content, anomaly detection as well as forecasting.
Exploratory analysis[edit]
Tuberculosis incidence US 1953-2009
Further information: Exploratory analysis
The clearest way to examine a regular time series manually is with a line chart such as the one shown for tuberculosis in the United States, made with a spreadsheet program. The number of cases was standardized to a rate per 100,000 and the percent change per year in this rate was calculated. The nearly steadily dropping line shows that the TB incidence was decreasing in most years, but the percent change in this rate varied by as much as +/- 10%, with 'surges' in 1975 and around the early 1990s. The use of both vertical axes allows the comparison of two time series in one graphic.
Other techniques include:
Autocorrelation analysis to examine serial dependence
Spectral analysis to examine cyclic behavior which need not be related to seasonality. For example, sun spot activity varies over 11 year cycles.[5][6] Other common examples include celestial phenomena, weather patterns, neural activity, commodity prices, and economic activity.
Separation into components representing trend, seasonality, slow and fast variation, and cyclical irregularity: see trend estimation and decomposition of time series
Curve fitting[edit]
Main article: Curve fitting
Curve fitting[7][8] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points,[9] possibly subject to constraints.[10][11] Curve fitting can involve either interpolation,[12][13] where an exact fit to the data is required, or smoothing,[14][15] in which a "smooth" function is constructed that approximately fits the data. A related topic is regression analysis,[16][17] 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,[18][19] to infer values of a function where no data are available,[20] and to summarize the relationships among two or more variables.[21] Extrapolation refers to the use of a fitted curve beyond the range of the observed data,[22] and is subject to a degree of uncertainty[23] since it may reflect the method used to construct the curve as much as it reflects the observed data.
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 the available information ("reading between the lines").[24] 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.[25] Alternatively polynomial interpolation or spline interpolation is used where piecewise polynomial functions are fit into time intervals such that they fit smoothly together. A different problem which 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 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.
Function approximation[edit]
Main article: Function approximation
In general, a function approximation problem asks us to select 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, 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 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[26] 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 a unified treatment in statistical learning theory, where they are viewed as supervised learning problems.
Prediction and forecasting[edit]
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 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.
Fully formed statistical models for stochastic simulation purposes, so as to generate alternative versions of the time series, representing what might happen over non-specific time-periods in the future
Simple or fully formed statistical models to describe the likely outcome of the time series in the immediate future, given knowledge of the most recent outcomes (forecasting).
Forecasting on time series is usually done using automated statistical software packages and programming languages, such as R, S, SAS, SPSS, Minitab, pandas (Python) and many others.
Classification[edit]
Main article: Statistical classification
Assigning time series pattern to a specific category, for example identify a word based on series of hand movements in sign language
Signal estimation[edit]
See also: Signal processing and Estimation theory
This approach is based on harmonic analysis and filtering of signals in the frequency domain using the Fourier transform, and spectral density estimation, the development of which 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 a certain point in time. See Kalman filter, Estimation theory, and Digital signal processing
Segmentation[edit]
Main article: Time-series segmentation
Splitting 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 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.
Models[edit]
Models for time series data can have many forms and represent different stochastic processes. When modeling variations in 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.[27] 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 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 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 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),[28] and (Abarbanel) [29]
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 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 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 a time series of spoken words into text.
Notation[edit]
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
X = {X1, X2, ...}.
Another common notation is
Y = {Yt: t ∈ T},
where T is the index set.
Conditions[edit]
There are two sets of conditions under which much of the theory is built:
Stationary process
Ergodic process
However, ideas of stationarity must be expanded to consider two important ideas: strict stationarity and 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 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.[30]
Tools[edit]
This section is in a list format that may be better presented using prose. You can help by converting this section to prose, if appropriate. Editing help is available. (February 2012)
Tools for investigating time-series data include:
Consideration of the autocorrelation function and the spectral density function (also cross-correlation functions and cross-spectral density functions)
Scaled cross- and auto-correlation functions to remove contributions of slow components[31]
Performing a Fourier transform to investigate the series in the frequency domain
Use of a filter to remove unwanted noise
Principal component analysis (or empirical orthogonal function analysis)
Singular spectrum analysis
"Structural" models:
General State Space Models
Unobserved Components Models
Machine Learning
Artificial neural networks
Support Vector Machine
Fuzzy Logic
Gaussian Processes
Hidden Markov model
Queueing Theory Analysis
Control chart
Shewhart individuals control chart
CUSUM chart
EWMA chart
Detrended fluctuation analysis
Dynamic time warping[32]
Cross-correlation[33]
Dynamic Bayesian network
Time-frequency analysis techniques:
Fast Fourier Transform
Continuous wavelet transform
Short-time Fourier transform
Chirplet transform
Fractional Fourier transform
Chaotic analysis
Correlation dimension
Recurrence plots
Recurrence quantification analysis
Lyapunov exponents
Entropy encoding
Measures[edit]
Time series metrics or features that can be used for time series classification or regression analysis:[34]
Univariate linear measures
Moment (mathematics)
Spectral band power
Spectral edge frequency
Accumulated Energy (signal processing)
Characteristics of the autocorrelation function
Hjorth parameters
FFT parameters
Autoregressive model parameters
Mann–Kendall test
Univariate non-linear measures
Measures based on the correlation sum
Correlation dimension
Correlation integral
Correlation density
Correlation entropy
Approximate entropy[35]
Sample entropy
Fourier entropyuk
Wavelet entropy
Rényi entropy
Higher-order methods
Marginal predictability
Dynamical similarity index
State space dissimilarity measures
Lyapunov exponent
Permutation methods
Local flow
Other univariate measures
Algorithmic complexity
Kolmogorov complexity estimates
Hidden Markov Model states
Rough path signature[36]
Surrogate time series and surrogate correction
Loss of recurrence (degree of non-stationarity)
Bivariate linear measures
Maximum linear cross-correlation
Linear Coherence (signal processing)
Bivariate non-linear measures
Non-linear interdependence
Dynamical Entrainment (physics)
Measures for Phase synchronization
Measures for Phase locking
Similarity measures:[37]
Cross-correlation
Dynamic Time Warping[32]
Hidden Markov Models
Edit distance
Total correlation
Newey–West estimator
Prais–Winsten transformation
Data as Vectors in a Metrizable Space
Minkowski distance
Mahalanobis distance
Data as Time Series with Envelopes
Global Standard Deviation
Local Standard Deviation
Windowed Standard Deviation
Data Interpreted as Stochastic Series
Pearson product-moment correlation coefficient
Spearman's rank correlation coefficient
Data Interpreted as a Probability Distribution Function
Kolmogorov–Smirnov test
Cramér–von Mises criterion
Visualization[edit]
Time series can be visualized with two categories of chart: Overlapping Charts and Separated Charts. Overlapping Charts display all time series on the same layout while Separated Charts presents them on different layouts (but aligned for comparison purpose)[38]
Overlapping charts[edit]
Braided Graphs
Line Charts
Slope Graphs
GapChart
Separated charts[edit]
Horizon Graphs
Reduced Line Charts (small multiples)
Silhouette Graph
Circular Silhouette Graph
Software[edit]
Working with Time Series data is a relatively common use for statistical analysis software. As a result of this, there are many offerings both commercial and open source. Some examples include:
CRAN supplementary statistics package for R[39]
Analysis and Forecasting with Weka[40]
Predictive modeling with GMDH Shell[41]
Functions and Modeling in the Wolfram Language[42]
Time Series Objects in MATLAB[43]
SAS/ETS in SAS software[44]
Expert Modeler in IBM SPSS Statistics and IBM SPSS Modeler
Automatic Time series Forecasting with LDT[45]
EViews is a statistical package for Windows, used mainly for time-series oriented econometric analysis.
bayesloop: Probabilistic programming framework that facilitates objective model selection for time-varying parameter models[46]
See also[edit]
Anomaly time series
Chirp
Decomposition of time series
Detrended fluctuation analysis
Digital signal processing
Distributed lag
Estimation theory
Forecasting
Hurst exponent
Monte Carlo method
Panel analysis
Random walk
Scaled correlation
Seasonal adjustment
Sequence analysis
Signal processing
Trend estimation
Unevenly spaced time series
Time series database
References[edit]
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Jump up ^ Imdadullah. "Time Series Analysis". Basic Statistics and Data Analysis. itfeature.com. Retrieved 2 January 2014.
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Jump up ^ Bloomfield, P. (1976). Fourier analysis of time series: An introduction. New York: Wiley. ISBN 0471082562.
Jump up ^ Shumway, R. H. (1988). Applied statistical time series analysis. Englewood Cliffs, NJ: Prentice Hall. ISBN 0130415006.
Jump up ^ Sandra Lach Arlinghaus, PHB Practical Handbook of Curve Fitting. CRC Press, 1994.
Jump up ^ William M. Kolb. Curve Fitting for Programmable Calculators. Syntec, Incorporated, 1984.
Jump up ^ S.S. Halli, K.V. Rao. 1992. Advanced Techniques of Population Analysis. ISBN 0306439972 Page 165 (cf. ... functions are fulfilled if we have a good to moderate fit for the observed data.)
Jump up ^ The Signal and the Noise: Why So Many Predictions Fail-but Some Don't. By Nate Silver
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Jump up ^ Numerical Methods of Curve Fitting. By P. G. Guest, Philip George Guest. Page 349.
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Jump up ^ An Introduction to Risk and Uncertainty in the Evaluation of Environmental Investments. DIANE Publishing. Pg 69
Jump up ^ Hamming, Richard. Numerical methods for scientists and engineers. Courier Corporation, 2012.
Jump up ^ Friedman, Milton. "The interpolation of time series by related series." Journal of the American Statistical Association 57.300 (1962): 729-757.
Jump up ^ Gandhi, Sorabh, Luca Foschini, and Subhash Suri. "Space-efficient online approximation of time series data: Streams, amnesia, and out-of-order." Data Engineering (ICDE), 2010 IEEE 26th International Conference on. IEEE, 2010.
Jump up ^ Gershenfeld, N. (1999). The Nature of Mathematical Modeling. New York: Cambridge University Press. pp. 205–208. ISBN 0521570956.
Jump up ^ Kantz, Holger; Thomas, Schreiber (2004). Nonlinear Time Series Analysis. London: Cambridge University Press. ISBN 978-0521529020.
Jump up ^ Abarbanel, Henry (Nov 25, 1997). Analysis of Observed Chaotic Data. New York: Springer. ISBN 978-0387983721.
Jump up ^ Boashash, B. (ed.), (2003) Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, Elsevier Science, Oxford, 2003 ISBN 0-08-044335-4
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Further reading[edit]
Box, George; Jenkins, Gwilym (1976), Time Series Analysis: forecasting and control, rev. ed., Oakland, California: Holden-Day
Cowpertwait P.S.P., Metcalfe A.V. (2009), Introductory Time Series with R, Springer.
Durbin J., Koopman S.J. (2001), Time Series Analysis by State Space Methods, Oxford University Press.
Gershenfeld, Neil (2000), The Nature of Mathematical Modeling, Cambridge University Press, ISBN 978-0-521-57095-4, OCLC 174825352
Hamilton, James (1994), Time Series Analysis, Princeton University Press, ISBN 0-691-04289-6
Priestley, M. B. (1981), Spectral Analysis and Time Series, Academic Press. ISBN 978-0-12-564901-8
Shasha, D. (2004), High Performance Discovery in Time Series, Springer, ISBN 0-387-00857-8
Shumway R. H., Stoffer (2011), Time Series Analysis and its Applications, Springer.
Weigend A. S., Gershenfeld N. A. (Eds.) (1994), Time Series Prediction: Forecasting the Future and Understanding the Past. Proceedings of the NATO Advanced Research Workshop on Comparative Time Series Analysis (Santa Fe, May 1992), Addison-Wesley.
Wiener, N. (1949), Extrapolation, Interpolation, and Smoothing of Stationary Time Series, MIT Press.
Woodward, W. A., Gray, H. L. & Elliott, A. C. (2012), Applied Time Series Analysis, CRC Press.
External links[edit]
Time series at Encyclopaedia of Mathematics.
A First Course on Time Series Analysis — An open source book on time series analysis with SAS.
Introduction to Time series Analysis (Engineering Statistics Handbook) — A practical guide to Time series analysis.
MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases.
A Matlab tutorial on power spectra, wavelet analysis, and coherence on website with many other tutorials.
TimeViz survey
Gaussian Processes for Machine Learning: Book webpage
CRAN Time Series Task View - Time Series in R
TimeSeries Analysis with Pandas
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Pro Football Hall of Fame Game
From Wikipedia, the free encyclopedia
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Pro Football Hall of Fame Game
Tom Benson Hall of Fame Stadium before the start of the 2017 Pro Football Hall of Fame Game
Recent and upcoming games
2018 season
Baltimore Ravens 17, Chicago Bears 16
2019 season
TBA
The Pro Football Hall of Fame Game is an annual National Football League (NFL) exhibition game that is held the weekend of the Pro Football Hall of Fame's induction ceremonies. The game is played at Tom Benson Hall of Fame Stadium, which is located adjacent to the Hall of Fame building in Canton, Ohio. It is traditionally the first game played in the NFL preseason for any given year, marking the end of the NFL's six-month off-season.
Contents
1 Team selection
1.1 Scheduling
2 Television and radio
3 Game history
4 Appearances
5 References
6 External links
Team selection[edit]
The two teams that play in the Pro Football Hall of Fame Game are typically selected by the league in advance of the remainder of the preseason schedule. The participants are usually announced around the time that the new Hall of Fame members are announced, which coincides with Super Bowl week. Often, if a particularly notable player will be entering the Hall of Fame that year, a team they were strongly associated with may be selected to play in the game to help maximize attendance and publicity of the game itself. From 1971 to 2010, the opponents for each game usually included one AFC team and one NFC team. In 2009, as recognition of the 50th anniversary of the American Football League, the game paired two AFC teams who were part of the "original eight" franchises of the AFL, the Tennessee Titans (dressed as their previous incarnation, the Houston Oilers) and the Buffalo Bills, whose owner, Ralph Wilson, was inducted into the Hall that year. An all-NFC matchup was scheduled for 2011,[1] but it was canceled due to the 2011 NFL lockout; the following year, another intra-conference matchup of two NFC teams took its place. From 2011 onward, each team selected to play in the game has had at least one prominent alumnus being inducted into the Hall that year.
Because this game and the Hall of Fame's induction ceremonies are scheduled on the weekend before the league's regular four-week exhibition season begins, both teams end up playing five exhibition games instead of the normal four, and unlike the Canadian Football League (which plays its two-game exhibition season over three weeks), the league does not remove antone. The last four expansion teams to have been added to the league each played in the Hall of Fame Game as their first game. In 1995, expansion clubs Jacksonville and Carolina played each another, and in 2002, Houston was one of the participants. When the Cleveland Browns returned to the league with a rebooted roster in 1999, they too played in the Hall of Fame Game. The Baltimore Ravens, while officially considered as being established in 1996, did not play in the Hall of Fame Game until 2018. With the Ravens' participation in the 2018 game, all 32 current NFL teams have now played in the Hall of Fame game at least once.
Scheduling[edit]
Prior to the AFL–NFL merger, the Hall of Fame Game was played in August or September, in some cases at the end of the preseason. In 1970, it was moved to the beginning of the preseason. Prior to 2002, it was not uncommon for the game to be played in July. Since 2002, when the league permanently moved the start of the season to the weekend after Labor Day, the game has always been played in early August.
The 2011 game was originally scheduled between the St. Louis Rams and the Chicago Bears, but the game was canceled due to an ongoing labor dispute that had disrupted nearly all league activity during the 2011 offseason. The two clubs had set a deadline of July 22 to ratify a resolution in enough time to prepare for the game. The league and players did not ratify the agreement until July 25, forcing cancellation of the game.
The 2016 edition, which was scheduled to be played between the Green Bay Packers and the Indianapolis Colts, was canceled due to unsafe playing conditions. Mike Silver of NFL.com reported that on the morning of game day, the logos at midfield and the end zones had been painted using paint which was not intended for use on artificial turf. When it was apparent the paint wasn't drying fast enough, the field was heated to speed up the drying process, but this caused the turf's rubber to melt. These issues led to unfavorable play conditions. The affected areas were described as being slick and "like cement," making it impossible to get decent footing. Stadium officials attempted to address this by applying a solvent, reportedly paint thinner, to the turf. A Packers employee noticed a label warning that this substance could result in burns when exposed to skin, and alerted others to the discovery. When officially cancelling the game, both the league and the Players Association cited safety concerns.[2][3][4] Both teams were told at 6:40 p.m.—an hour and 20 minutes before kickoff—that the game was going to be canceled. However, fans in the stadium only learned of the pending cancellation via social media. No official announcement was made until just before the scheduled 8 p.m. kickoff.[5]
On December 28, 2016, the NFL announced that the 2017 edition of the game would be played on a Thursday (August 3). This scheduling change made the Hall of Fame Game the first event of the Hall of Fame Weekend. The 2018 game was also played on a Thursday (August 2).[6] Come 2019, the game will be played on August 1.
Television and radio[edit]
Unlike the majority of NFL preseason games, which air on local TV stations, the Hall of Fame Game airs nationwide. From 1999 to 2005, the game was held on Monday night, televised as part of ABC's Monday Night Football package. It had previously been held typically on Saturday afternoons, except from 1963 to 1965 on Sunday afternoons, televised as part of ABC's Wide World of Sports package (still using the MNF crew). In 1998, the game was put in the MNF package, and played on a Saturday night, which served as a test run for the move to Monday night. Starting in 2006, it was moved to Sunday night, coinciding with the new NBC Sunday Night Football[7] television package. The 2007 game was telecast on NFL Network as NBC was intending to televise the China Bowl game in Beijing, China (a game which was postponed to 2009 and later canceled). The Hall of Fame Game has since bounced between both networks. The 2012 game aired on NFL Network due to NBC's coverage of the Summer Olympics. After a two-year absence, the game returned to NBC in 2013 and remained on the network until 2015. The 2016 game was scheduled to air on ESPN as it was to be played two days into the 2016 Summer Olympics but the game was canceled due to field conditions, it returned to NBC in 2017 and will remain on the network until 2019. The 2020 game will be on either ESPN or NFL Network due to NBC's coverage of the 2020 Summer Olympics.
Westwood One owns national radio rights; it is the only preseason game to air on a nationwide network. Each team's radio network is also allowed to broadcast the games (albeit usually with fewer affiliates since exhibition games are traditionally much lower priority to non-sports and non-flagship stations).
In 2007 and 2008, the game was broadcast live in both Ireland and the United Kingdom by Sky Sports. It was not shown in 2009 because of a dispute between Sky Sports and the NFL over TV rights. ESPN America (formerly North American Sports Network) broadcasts the game in the rest of Europe.
From 1998 to 2005, the Global Television Network aired the game in Canada, simulcasting ABC; upon the move to Sunday night in 2006, the CTV Television Network aired the game until 2010. With CTV airing the 2012 Summer Olympics, TSN broadcast the Hall of Fame Game in 2012. Azteca 7 aired Spanish-language coverage of the Hall of Fame Game in Mexico from 1998 to 2005; the Game moved to Canal 5 in 2006 and aired on that station until 2010. Due to the cancellation of the 2011 Hall of Fame Game and Televisa's Summer Olympics coverage, the Hall of Fame Game moved to Televisa Deportes in 2012 and then to Galavision in 2013.
Tom Benson Hall of Fame Stadium is part of the Cleveland market, and is also within 75 miles of numerous other television markets in northeast Ohio and western Pennsylvania. As such, the NBC affiliates (and before them, the ABC affiliates) were within league requirements to blackout the game in the rare event the game does not sell out, during the time the league had a blackout policy (the policy has been suspended indefinitely since 2015). Because the stadium only holds 22,375 fans (less than half the capacity of any other NFL stadium except the StubHub Center, which is the temporary home of the Los Angeles Chargers while their permanent stadium Los Angeles Stadium at Hollywood Park is built) and the Hall of Fame induction ceremonies bring large numbers of outside fans to Canton, the game has, to date, always sold out; several NFL teams, as well as the Super Bowl, have similarly enjoyed long sellout streaks.[citation needed]
Game history[edit]
Day
Date
Away team
Away score
Home team
Home score
Recap
Saturday August 11, 1962 New York Giants 21 St. Louis Cardinals 21 Recap
Sunday September 8, 1963 Cleveland Browns 7 Pittsburgh Steelers 16 Recap
Sunday September 6, 1964 Baltimore Colts 48 Pittsburgh Steelers 17 Recap
Sunday September 12, 1965 Detroit Lions 3 Washington Redskins 20 Recap
Saturday August 5, 1967 Cleveland Browns 13 Philadelphia Eagles 28 Recap
Saturday August 3, 1968 Dallas Cowboys 24 Chicago Bears 30 Recap
Saturday September 13, 1969 Green Bay Packers 38 Atlanta Falcons 24 Recap
Saturday August 8, 1970 New Orleans Saints 14 Minnesota Vikings 13 Recap
Saturday July 31, 1971 Houston Oilers 6 Los Angeles Rams 17 Recap
Saturday July 29, 1972 Kansas City Chiefs 23 New York Giants 17 Recap
Saturday July 28, 1973 San Francisco 49ers 20 New England Patriots 7 Recap
Saturday July 27, 1974 Buffalo Bills 13 St. Louis Cardinals 21 Recap
Saturday August 2, 1975 Washington Redskins 17 Cincinnati Bengals 9 Recap
Saturday July 24, 1976 Denver Broncos 10 Detroit Lions 7 Recap
Saturday July 30, 1977 Chicago Bears 20 New York Jets 6 Recap
Saturday July 29, 1978 Miami Dolphins 3 Philadelphia Eagles 17 Recap
Saturday July 28, 1979 Dallas Cowboys 13 Oakland Raiders 20 Recap
Saturday August 2, 1980 San Diego Chargers 0 Green Bay Packers 0 Recap[n 1]
Saturday August 1, 1981 Atlanta Falcons 10 Cleveland Browns 24 Recap
Saturday August 7, 1982 Baltimore Colts 14 Minnesota Vikings 30 Recap
Saturday July 30, 1983 New Orleans Saints 14 Pittsburgh Steelers 27 Recap
Saturday July 28, 1984 Seattle Seahawks 38 Tampa Bay Buccaneers 0 Recap
Saturday August 3, 1985 New York Giants 21 Houston Oilers 20 Recap
Saturday August 2, 1986 New England Patriots 21 St. Louis Cardinals 16 Recap
Saturday August 8, 1987 San Francisco 49ers 20 Kansas City Chiefs 7 Recap
Saturday July 30, 1988 Cincinnati Bengals 14 Los Angeles Rams 7 Recap
Saturday August 5, 1989 Washington Redskins 31 Buffalo Bills 6 Recap
Saturday August 4, 1990 Cleveland Browns 0 Chicago Bears 13 Recap
Saturday July 27, 1991 Detroit Lions 14 Denver Broncos 3 Recap
Saturday August 1, 1992 New York Jets 41 Philadelphia Eagles 14 Recap
Saturday July 31, 1993 Green Bay Packers 3 Los Angeles Raiders 19 Recap
Saturday July 30, 1994 San Diego Chargers 17 Atlanta Falcons 21 Recap
Saturday July 29, 1995 Carolina Panthers 20 Jacksonville Jaguars 14 Recap
Saturday July 27, 1996 Indianapolis Colts 10 New Orleans Saints 3 Recap
Saturday July 26, 1997 Minnesota Vikings 28 Seattle Seahawks 26 Recap
Saturday August 1, 1998 Pittsburgh Steelers 6 Tampa Bay Buccaneers 30 Recap
Monday August 9, 1999 Dallas Cowboys 17 Cleveland Browns 20 Recap
Monday July 31, 2000 New England Patriots 20 San Francisco 49ers 0 Recap
Monday August 6, 2001 St. Louis Rams 17 Miami Dolphins 10 Recap
Monday August 5, 2002 Houston Texans 17 New York Giants 34 Recap
Monday August 4, 2003 Green Bay Packers 0 Kansas City Chiefs 9 Recap[n 2]
Monday August 9, 2004 Denver Broncos 17 Washington Redskins 20 Recap
Monday August 8, 2005 Chicago Bears 27 Miami Dolphins 24 Recap
Sunday August 6, 2006 Oakland Raiders 16 Philadelphia Eagles 10 Recap
Sunday August 5, 2007 New Orleans Saints 7 Pittsburgh Steelers 20 Recap
Sunday August 3, 2008 Indianapolis Colts 16 Washington Redskins 30 Recap
Sunday August 9, 2009 Buffalo Bills 18 Tennessee Titans 21 Recap
Sunday August 8, 2010 Dallas Cowboys 16 Cincinnati Bengals 7 Recap
Sunday August 7, 2011 Canceled due to 2011 NFL lockout Ref.[n 3]
Sunday August 5, 2012 Arizona Cardinals 10 New Orleans Saints 17 Recap
Sunday August 4, 2013 Miami Dolphins 20 Dallas Cowboys 24 Recap
Sunday August 3, 2014 New York Giants 17 Buffalo Bills 13 Recap
Sunday August 9, 2015 Pittsburgh Steelers 3 Minnesota Vikings 14 Recap
Sunday August 7, 2016 Canceled due to poor field conditions Ref.[n 4]
Thursday August 3, 2017 Arizona Cardinals 18 Dallas Cowboys 20 Recap
Thursday August 2, 2018 Chicago Bears 16 Baltimore Ravens 17 Recap
Thursday August 1, 2019 TBA TBA
Notes
Jump up ^ The 1980 game ended with 5:29 remaining in the fourth quarter in a 0-0 tie due to severe lightning.
Jump up ^ The 2003 game ended with 5:49 remaining in the third quarter due to severe lightning with the Chiefs leading 9-0.
Jump up ^ The 2011 game was scheduled to be St. Louis Rams vs. Chicago Bears, but was canceled due to the lockout.
Jump up ^ The 2016 game was scheduled to be Green Bay Packers vs Indianapolis Colts, but was canceled due to poor field conditions.
Appearances[edit]
Team
Games
W
L
T
PCT
Last appearance
Pittsburgh Steelers 6 3 3 0 .500 2015
Dallas Cowboys 6 3 3 0 .500 2017
Washington Redskins 5 5 0 0 1.000 2008
Chicago Bears 5 4 1 0 .800 2018
New York Giants 5 3 1 1 .700 2014
Cleveland Browns 5 2 3 0 .400 1999
New Orleans Saints 5 2 3 0 .400 2012
Arizona/St. Louis Cardinals 5 1 3 1 .300 2017
Minnesota Vikings 4 3 1 0 .750 2015
Philadelphia Eagles 4 2 2 0 .500 2006
Indianapolis/Baltimore Colts 4 2 2 0 .500 2008
Green Bay Packers 4 1 2 1 .375 2003
Miami Dolphins 4 0 4 0 .000 2013
Buffalo Bills 4 0 4 0 .000 2014
Oakland/Los Angeles Raiders 3 3 0 0 1.000 2006
New England Patriots 3 2 1 0 .667 2000
San Francisco 49ers 3 2 1 0 .667 2000
Los Angeles/St. Louis Rams 3 2 1 0 .667 2001
Kansas City Chiefs 3 2 1 0 .667 2003
Detroit Lions 3 1 2 0 .333 1991
Atlanta Falcons 3 1 2 0 .333 1994
Denver Broncos 3 1 2 0 .333 2004
Tennessee Titans/Houston Oilers 3 1 2 0 .333 2009
Cincinnati Bengals 3 1 2 0 .333 2010
New York Jets 2 1 1 0 .500 1992
Seattle Seahawks 2 1 1 0 .500 1997
Tampa Bay Buccaneers 2 1 1 0 .500 1998
Los Angeles/San Diego Chargers 2 0 1 1 .250 1994
Carolina Panthers 1 1 0 0 1.000 1995
Baltimore Ravens 1 1 0 0 1.000 2018
Jacksonville Jaguars 1 0 1 0 .000 1995
Houston Texans 1 0 1 0 .000 2002
Most appearances – 6: Pittsburgh Steelers (1963, 1964, 1983, 1998, 2007, and 2015) and Dallas Cowboys (1968, 1979, 1999, 2010, 2013, and 2017); if the 2011 game was not canceled due to the lockout, the Chicago Bears would be scheduled for their 6th appearance in 2018 (1968, 1977, 1990, 2005, 2011, and 2018)
Most wins – 5: Washington Redskins (1965, 1975, 1989, 2004, and 2008)
Longest active drought without Hall of Fame Game appearance – 27 seasons: Detroit Lions (last appearance – 1991); 26 seasons: New York Jets (last appearance – 1992); 24 seasons: Atlanta Falcons and Los Angeles Chargers (last appearance – 1994); 23 seasons: Carolina Panthers and Jacksonville Jaguars (last and only appearance – 1995)
References[edit]
Jump up ^ "Individual Tickets & Festival Fan Packages". Profootballhof.com. Archived from the original on September 10, 2012. Retrieved September 28, 2012.
Jump up ^ Silver, Mike (2016-08-04). "Hall of Fame Game cancellation: NFL made the right decision". NFL.com.
Jump up ^ Demovsky, Rob (August 7, 2016). "Poor field conditions force cancellation of Hall of Fame game". ESPN.com. Retrieved August 7, 2016.
Jump up ^ "Hall of Fame Game fiasco: How the wrong paint, melted rubber, and caustic paint thinner nixed Packers-Colts". Acme Packing Company (SBNation). Vox Media. Retrieved 10 August 2016.
Jump up ^ Doyel, Gregg (2016-08-07). "Doyel: NFL fans deserve better". The Indianapolis Star.
Jump up ^ "Pro Football Hall of Fame Experiences". hofexperiences.com. Retrieved January 16, 2018.
Jump up ^ ""Sunday Night Football" Debuts". NBC Sports History Page.
2006 NFL Record and Fact Book. p. 401. ISBN 1-933405-32-5.
"AFC-NFC Hall of Fame Game". Pro Football Hall of Fame. Archived from the original on September 4, 2006. Retrieved September 8, 2006.
External links[edit]
Pro Football Hall of Fame American Football League Legacy Game
All-time Pro Football Hall of Fame Game Results
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