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Time Series Analysis and Forecasting I

Time Series Analysis and Forecasting I

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Time Series Analysis and Forecasting I

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  1. Time Series Analysis and Forecasting I

  2. Introduction • A time series is a set of observations generated sequentially in time • Continuous vs. discrete time series • The observations from a discrete time series, made at some fixed interval h, at times 1, 2,…, N may be denoted by z(1), z(2),…, z(N)

  3. Introduction (cont.) • Discrete time series may arise in two ways: • 1- By sampling a continuous time series • 2- By accumulating a variable over a period of time • Characteristics of time series • Time periods are of equal length • No missing values

  4. Components of a time series Zt = Ft + at

  5. Areas of application • Forecasting • Determination of a transfer function of a system • Design of simple feed-forward and feedback control schemes

  6. Applications Economic and business planning Inventory and production control Control and optimization of industrial processes Lead time of the forecasts is the period over which forecasts are needed Degree of sophistication Simple ideas Moving averages Simple regression techniques Complex statistical concepts Box-Jenkins methodology Forecasting

  7. Self-projecting approach Cause-and-effect approach Approaches to forecasting

  8. Self-projecting approach Advantages Quickly and easily applied A minimum of data is required Reasonably short-to medium-term forecasts They provide a basis by which forecasts developed through other models can be measured against Disadvantages Not useful for forecasting into the far future Do not take into account external factors Cause-and-effect approach Advantages Bring more information More accurate medium-to long-term forecasts Disadvantages Forecasts of the explanatory time series are required Approaches to forecasting (cont.)

  9. Some traditional self-projecting models • Overall trend models • The trend could be linear, exponential, parabolic, etc. • A linear Trend has the form • Trendt = A + Bt • Short-term changes are difficult to track • Smoothing models • Respond to the most recent behavior of the series • Employ the idea of weighted averages • They range in the degree of sophistication • The simple exponential smoothing method:

  10. Some traditional self-projecting models (cont.) • Seasonal models • Very common • Most seasonal time series also contain long- and short-term trend patterns • Decomposition models • The series is decomposed into its separate patterns • Each pattern is modeled separately

  11. Drawbacks of the use of traditional models • There is no systematic approach for the identification and selection of an appropriate model, and therefore, the identification process is mainly trial-and-error • There is difficulty in verifying the validity of the model • Most traditional methods were developed from intuitive and practical considerations rather than from a statistical foundation • Too narrow to deal efficiently with all time series

  12. ARIMA models • Autoregressive Integrated Moving-average • Can represent a wide range of time series • A “stochastic” modeling approach that can be used to calculate the probability of a future value lying between two specified limits

  13. ARIMA models (Cont.) • In the 1960’s Box and Jenkins recognized the importance of these models in the area of economic forecasting • “Time series analysis - forecasting and control” • George E. P. Box Gwilym M. Jenkins • 1st edition was in 1976 • Often called The Box-Jenkins approach

  14. Transfer function modeling • Yt = (B)Xt where (B) = 0 + 1B + 2B2 + ….. • B is the backshift operator BmXt = Xt - m

  15. The study of process dynamics can achieve: Better control Improved design Methods for estimating transfer function models Classical methods Based on deterministic perturbations Uncontrollable disturbances (“noise”) are not accounted for, and hence, these methods have not always been successful Statistical methods Make allowance for “noise” The Box-Jenkins methodology Transfer function modeling (cont.)

  16. Feed-forward control Feedback control Deviation from target output P P N N t t Deviation from target output - + 1 f 1 - - + d w 1 b 1 f 1 L ( B ) L ( B ) B ( B ) ( B ) B L ( B ) L ( B ) B 1 2 1 2 Compensating Compen sating variable X variable X t+ t+ Control equation Control equation z t Process control

  17. Process control (cont.)

  18. Process control (cont.) • The Box-Jenkins approach to control is to typify the disturbance by a suitable time series or stochastic model and the inertial characteristics of the system by a suitable transfer function model • The “Control equation”, allows the action which should be taken at any given time to be calculated given the present and previous states of the system • Various ways corresponding to various levels of technological sophistication can be used to execute a “control action” called for by the control equation

  19. The Box-Jenkins model building process Model identification Model estimation Is model adequate ? No Modify model Yes Forecasts

  20. The Box-Jenkins model building process (cont.) • Model identification • Autocorrelations • Partial-autocorrelations • Model estimation • The objective is to minimize the sum of squares of errors • Model validation • Certain diagnostics are used to check the validity of the model • Model forecasting • The estimated model is used to generate forecasts and confidence limits of the forecasts

  21. Important Fundamentals • A Normal process • Stationarity • Regular differencing • Autocorrelations (ACs) • The white noise process • The linear filter model • Invertibility

  22. A Normal process (A Gaussian process) • The Box-Jenkins methodology analyze a time series as a realization of a stochastic process. • The observation zt at a given time t can be regarded as a realization ofa random variable zt with probability density function p(zt) • The observations at any two times t1 and t2 may be regarded as realizations of two random variables zt1, zt2 and with joint probability density function p(zt1, zt2) • If the probability distribution associated with any set of times is multivariate Normal distribution, the process is called a normal or Gaussian process

  23. Stationary stochastic processes • In order to model a time series with the Box-Jenkins approach, the series has to be stationary • In practical terms, the series is stationary if tends to wonder more or less uniformly about some fixed level • In statistical terms, a stationary process is assumed to be in a particular state of statistical equilibrium, i.e., p(zt) is the same for all t

  24. Stationary stochastic processes (cont.) • the process is called “strictly stationary” • if the joint probability distribution of any m observations made at times t1, t2, …, tm is the same as that associated with m observations made at times t1 + k, t2 + k, …, tm + k • When m = 1, the stationarity assumption implies that the probability distribution p(zt) is the same for all times t

  25. Stationary stochastic processes (cont.) • In particular, if zt is a stationary process, then the first difference zt = zt - zt-1and higher differences dztare stationary • Most time series are nonstationary

  26. Achieving stationarity • Regular differencing (RD) (1st order) zt = (1 – B)zt = zt – zt-1 (2nd order) 2zt = (1 – B)2zt = zt – 2zt-1 + zt-2 “B” is the backward shift operator • It is unlikely that more than two regular differencing would ever be needed • Sometimes regular differencing by itself is not sufficient and prior transformation is also needed

  27. Some nonstationary series

  28. Some nonstationary series (cont.)

  29. Some nonstationary series (cont.) How can we determine the number of regular differencing ?

  30. Autocorrelations (ACs) • Autocorrelations are statistical measures that indicate how a time series is related to itself over time • The autocorrelation at lag 1 is the correlation between the original series zt and the same series moved forward one period (represented as zt-1)

  31. Autocorrelations (cont.) • The theoretical autocorrelation function • The sample autocorrelation

  32. Autocorrelations (cont.) • A graph of the correlation values is called a “correlogram” • In practice, to obtain a useful estimate of the autocorrelation function, at least 50 observations are needed • The estimated autocorrelations rk would be calculated up to lag no larger than N/4

  33. A “correlogram” of a nonstationary time seies

  34. After one RD

  35. After two RD

  36. The white noise process • The Box-Jenkins models are based on the idea that a time series can be usefully regarded as generated from (driven by) a series of uncorrelated independent “shocks” at • Such a sequence at, at-1, at-2,… is called a “white noise process”

  37. y ( B ) White noise z t Linear filter a t The linear filter model • A “linear filter” is a model that transform the white noise process at to the process that generated the time series zt

  38. The linear filter model (cont.) • (B) is the “transfer function” of the filter

  39. The linear filter model (cont.) • The linear filter can be put in another form • This form can be written

  40. For a linear process to be stationary, If the current observation zt depends on past observations with weights which decrease as we go back in time, the series is called invertible For a linear process to be invertible, Stationarity and invertibility conditions for a linear filter

  41. Model building blocks • Autoregressive (AR) models • Moving-average (MA) models • Mixed ARMA models • Non stationary models (ARIMA models) • The mean parameter • The trend parameter

  42. Autoregressive (AR) models • An autoregressive model of order “p” • The autoregressive process can be thought of as the output from a linear filter with a transfer function -1(B), when the input is white noise at • The equation (B) = 0 is called the “characteristic equation”

  43. Moving-average (MA) models • A moving-average model of order “q” • The moving-average process can be thought of as the output from a linear filter with a transfer function (B), when the input is white noise at • The equation (B) = 0 is called the “characteristic equation”

  44. Mixed AR and MA (ARMA) models • A moving-average process of 1st order can be written as • Hence, if the process were really MA(1), we would obtain a non parsimonious representation in terms of an autoregressive model

  45. Mixed AR and MA (ARMA) models (cont.) • In order to obtain a parsimonious model, sometimes it will be necessary to include both AR and MA terms in the model • An ARMA(p, q) model • The ARMA process can be thought of as the output from a linear filter with a transfer function (B)/(B), when the input is white noise at

  46. The Box-Jenkins model building process • Model identification • Autocorrelations • Partial-autocorrelations • Model estimation • Model validation • Certain diagnostics are used to check the validity of the model • Model forecasting

  47. Partial-autocorrelations (PACs) • Partial-autocorrelations are another set of statistical measures are used to identify time series models • PAC is Similar to AC, except that when calculating it, the ACs with all the elements within the lag are partialled out (Box & Jenkins, 1976)

  48. Partial-autocorrelations (cont.) • PACs can be calculated from the values of the ACs where each PAC is obtained from a different set of linear equations that describe a pure autoregressive model of an order that is equal to the value of the lag of the partial-autocorrelation computed • PAC at lag k is denoted by kk • The double notation kk is to emphasize that kk is the autoregressive parameter k of the autoregressive model of order k

  49. Stationarity and invertibility conditions Theoretical ACs and PACs Model identification • The sample ACs and PACs are computed for the series and compared to theoretical autocorrelation and partial-autocorrelation functions for candidate models investigated

  50. For a linear process to be stationary, For a linear process to be invertible, Stationarity and invertibility conditions