# Book Notes: Introduction to Time Series and Forecasting -- Ch2 Stationary Processes

### Best linear predictor

• Goal: find a function of $$X_n$$ that gives the “best” predictor of $$X_{n+h}$$.

• We mean “best” by achieving minimum mean squared error
• Under joint normality assumption of $$X_n$$ and $$X_{n+h}$$, the best estimator is $m(X_n) = E(X_{n+h} \mid X_n) = \mu + \rho(h)(X_n - \mu)$
• Best linear predictor $\ell(X_n) = a X_n + b$

• For Gaussian processes, $$\ell(X_n)$$ and $$m(X_n)$$ are the same.
• The best linear predictor only depends on the mean and ACF of the series $$\{X_n\}$$

### Properties of ACVF $$\gamma(\cdot)$$ and ACF $$\rho(\cdot)$$

• $$\gamma(0) \geq 0$$

• $$|\gamma(h)| \leq \gamma(0)$$ for all $$h$$

• $$\gamma(h)$$ is a even function, i.e., $$\gamma(h) = \gamma(-h)$$ for all $$h$$

• A function $$\kappa: \mathbb{N} \rightarrow \mathbb{R}$$ is nonnegative definite if $\sum_{i, j= 1}^n a_i \kappa(i - j) a_j \geq 0$ for all $$n \in \mathbb{N}^+$$ and vectors $$\mathbf{a} = (a_1, \ldots, a_n)' \in \mathbb{R}^n$$

• Theorem: a real-value function defined on the integers is the autocovariance function of a stationary time series if and only if it is even and nonnegative definite

• ACF $$\rho(\cdot)$$ has all above properties of ACVF $$\gamma(\cdot)$$

• Plus one more: $$\rho(0) = 1$$

### MA$$(q)$$ process, $$q$$-dependent, and $$q$$-correlated

• A time series $$\{X_t\}$$ is

• $$q$$-dependent: if $$X_s$$ and $$X_t$$ are independent for all $$|t-s| > q$$.

• $$q$$-correlated: if $$\rho(h) = 0$$ for all $$|h| > q$$.

• Moving-average process of order $$q$$: $$\{X_t\}$$ is a MA$$(q)$$ process if $X_t = Z_t + \theta_1 Z_{t-1} + \cdots + \theta_q Z_{t-q}$ where $$\{Z_t\} \sim \textrm{WN}(0, \sigma^2)$$

• A MA$$(q)$$ process is $$q$$-correlated

• Theorem: a stationary $$q$$-correlated time series with mean 0 can be represented as a MA$$(q)$$ process

# Linear Processes

### Linear processes: definitions

• A time series $$\{X_t\}$$ is a linear process if $X_t = \sum_{j = -\infty}^{\infty} \psi_j Z_{t-j}$ where $$\{Z_t\} \sim \textrm{WN}(0, \sigma^2)$$, and the constants $$\{\psi_j\}$$ satisfy $\sum_{j = -\infty}^{\infty} |\psi_j| < \infty$

• Equivalent representation using backward shift operator $$B$$ $X_t = \psi(B) Z_t, \quad \psi(B) = \sum_{j = -\infty}^{\infty} \psi_j B^j$

• Special case: moving average MA$$(\infty)$$ $X_t = \sum_{j = 0}^{\infty} \psi_j Z_{t-j}$

### Linear processes: properties

• In the linear process $$X_t = \sum_{j = -\infty}^{\infty} \psi_j Z_{t-j}$$ definition, the condition $$\sum_{j = -\infty}^{\infty} |\psi_j| < \infty$$ ensures

• The infinite sum $$X_t$$ converges with probability 1
• $$\sum_{j = -\infty}^{\infty} \psi_j^2 < \infty$$, and hence $$X_t$$ converges in mean square, i.e., $$X_t$$ is the mean square limit of the partial sum $$\sum_{j = -n}^{n} \psi_j Z_{t-j}$$

### Apply a linear filter to a stationary time series, then the output series is also stationary

• Theorem: let $$\{Y_t\}$$ be a stationary time series with mean 0 and ACVF $$\gamma_Y$$. If $$\sum_{j = -\infty}^{\infty} |\psi_j| < \infty$$, then the time series $X_t = \sum_{j = -\infty}^{\infty} \psi_j Y_{t-j} = \psi(B) Y_t$ is stationary with mean 0 and ACVF $\gamma_X(h) = \sum_{j = -\infty}^{\infty}\sum_{k = -\infty}^{\infty} \psi_j \psi_k \gamma_Y(h + k - j)$

• Special case of the above result: If $$\{X_t\}$$ is a linear process, then its ACVF is $\gamma_X(h) = \sum_{j = -\infty}^{\infty} \psi_j \psi_{j + h} \sigma^2$

### Combine multiple linear filters

• Linear filters with absoluately summable coefficients $\alpha(B) = \sum_{j = -\infty}^{\infty} \alpha_j B^j, \quad \beta(B) = \sum_{j = -\infty}^{\infty} \beta_j B^j$ can be applied successively to a stationary series $$\{Y_t\}$$ to generate a new stationary series $W_t = \sum_{j = -\infty}^{\infty} \psi_j Y_{t-j}, \quad \psi_j = \sum_{k-\infty}^{\infty} \alpha_k \beta_{j-k} = \sum_{k-\infty}^{\infty} \beta_k \alpha_{j-k}$ or equivalently, $W_t = \psi(B) Y_t, \quad \psi(B) = \alpha(B) \beta(B) = \beta(B)\alpha(B)$

### AR$$(1)$$ process $$X_t - \phi X_{t-1} = Z_t$$, in linear process formats

• If $$|\phi| < 1$$, then $X_t = \sum_{j=0}^{\infty} \phi^j Z_{t-j}$

• Since $$X_t$$ only depends on $$\{Z_s, s \leq t\}$$, we say $$\{X_t\}$$ is causal or future-independent
• If $$|\phi| > 1$$, then $X_t = -\sum_{j=1}^{\infty} \phi^{-j} Z_{t+j}$

• This is because $$X_t = -\phi^{-1} Z_{t+1} + \phi^{-1} X_{t+1}$$
• Since $$X_t$$ depends on $$\{Z_s, s \geq t\}$$, we say $$\{X_t\}$$ is noncausal
• If $$\phi = \pm 1$$, then there is no stationary linear process solution

# Introduction to ARMA Processes

## ARMA$$(1,1)$$ process

### ARMA$$(1,1)$$ process: definitions

• The time series $$\{X_t\}$$ is a ARMA$$(1, 1)$$ process if it is stationary and satisfies $X_t - \phi X_{t-1} = Z_t + \theta Z_{t-1}$ where $$\{Z_t\} \sim \textrm{WN}(0, \sigma^2)$$ and $$\phi + \theta \neq 0$$

• Equivalent represention using the backward shift operator $\phi(B) X_t = \theta(B) Z_t, \quad\text{where } \phi(B) = 1 - \phi B, \ \theta(B) = 1 + \theta B,$

### ARMA$$(1, 1)$$ process in linear process format

• If $$\phi \neq \pm 1$$, by letting $$\chi(z) = 1/\phi(z)$$, we can write an ARMA$$(1, 1)$$ as $X_t = \chi(B) \theta(B) Z_t = \psi(B) Z_t, \quad \text{where } \psi(B) = \sum_{j=-\infty}^{\infty} \psi_j B^j$

• If $$|\phi| < 1$$, then $$\chi(z) = \sum_{j=0}^{\infty} \phi^j z^j$$, and $\psi_j = \begin{cases} 0, & \text{if } j \leq -1,\\ 1, & \text{if } j = 0, \\ (\phi + \theta) \phi^{j-1}, & \text{if } j \geq 1 \end{cases} \quad \text{Causal}$

• If $$|\phi| > 1$$, then $$\chi(z) = -\sum_{j=-\infty}^{-1} \phi^{j} z^{j}$$, and $\psi_j = \begin{cases} -(\theta + \phi) \phi^{j-1}, & \text{if } j \leq -1,\\ -\theta\phi^{-1}, & \text{if } j = 0, \\ 0, & \text{if } j \geq 1 \end{cases} \quad \text{Noncausal}$

• If $$\phi = \pm 1$$, then there is no such stationary ARMA$$(1, 1)$$ process

### Invertibility

• Invertibility is the dual concept of causaility
• Causal: $$X_t$$ can be expressed by $$\{Z_s, s \leq t\}$$
• Invertible: $$Z_t$$ can be expressed by $$\{X_s, s \leq t\}$$
• For an ARMA$$(1, 1)$$ process,
• If $$|\theta|< 1$$, then it is invertible
• If $$|\theta|> 1$$, then it is noninvertible

# Properties of the Sample ACVF and Sample ACF

### Estimation of the series mean $$\mu = E(X_t)$$

• The sample mean $$\bar{X}_n = \frac{1}{n} \sum_{i=1}^n X_i$$ is an unbised estimator of $$\mu$$

• Mean squared error $E(\bar{X}_n - \mu)^2 = \frac{1}{n} \sum_{h = -n}^n \left( 1 - \frac{|h|}{n} \right) \gamma(h)$
• Theorem: If $$\{X_t\}$$ is a stationary time series with mean 0 and ACVF $$\gamma(\cdot)$$, then as $$n \rightarrow \infty$$, $V(\bar{X}_t) = E(\bar{X}_n - \mu)^2 \longrightarrow 0, \quad \text{if } \gamma(n) \rightarrow 0,$ $n E(\bar{X}_n - \mu)^2 \longrightarrow \sum_{|h| <\infty} \gamma(h), \quad \text{if } \sum_{h = -\infty}^{\infty} |\gamma(h)| < \infty$

### Confidence bounds of $$\mu$$

• If $$\{X_t\}$$ is Gaussian, then $\sqrt{n} (\bar{X}_n - \mu) \sim \textrm{N} \left( 0, \sum_{|h| < n} \left( 1 - \frac{|h|}{n} \right) \gamma(h) \right)$

• For many common time series, such as linear and ARMA models, when $$n$$ is large, $$\bar{X}_n$$ is approximately normal: $\bar{X}_n \sim \textrm{N}\left(\mu, \frac{v}{n} \right), \quad v = \sum_{|h|<\infty} \gamma(h)$
• An approximate 95% confidence interval for $$\mu$$ is $\left(\bar{X}_n - 1.96 v^{1/2}/\sqrt{n}, \ \bar{X}_n + 1.96 v^{1/2}/\sqrt{n}\right)$
• To estimate $$v$$, we can use $\hat{v} = \sum_{|h|< \sqrt{n}} \left( 1 - \frac{|h|}{\sqrt{n}} \right) \hat{\gamma}(h)$

### Estimation of ACVF $$\gamma(\cdot)$$ and ACF $$\rho(\cdot)$$

• Use sample ACVF $$\hat{\gamma}(\cdot)$$ and sample ACF $$\hat{\rho}(\cdot)$$ $\hat{\gamma}(h) = \frac{1}{n} \sum_{t=1}^{n-|h|} (X_{t + |h|} - \bar{X}_n)(X_t - \bar{X}_n), \quad \hat{\rho}(\cdot) = \hat{\gamma}(h) / \hat{\gamma}(0)$
• Even if the factor $$1/n$$ is replaced by $$1/(n-h)$$, they are still biased
• They are nearly unbiased for large $$n$$
• When $$h$$ is slightly smaller than $$n$$, the estimators $$\hat{\gamma}(\cdot), \hat{\rho}(\cdot)$$ are unreliable since there are only few pairs of $$(X_{t + h}, X_t)$$.

• A useful guide for them to be reliable (by Jenkins): $n \geq 50, \quad h \leq n/4$

## Bartlett’s Formula

### Asymptotic distribution of $$\hat{\rho}(\cdot)$$

• For linear models, esp ARMA models, when $$n$$ is large, $$\hat{\boldsymbol\rho}_k = (\hat{\rho}(1), \ldots, \hat{\rho}(k))'$$ is approximately normal $\hat{\boldsymbol\rho}_k \sim \textrm{N}(\boldsymbol\rho, n^{-1}W)$

• By Bartlett’s formula, $$W$$ is the covariance matrix with entries \begin{align*} w_{ij} = \sum_{k=1}^{\infty} & \left[ \rho(k + i) + \rho(k - i) - 2 \rho(i)\rho(k) \right] \\ & \times \left[ \rho(k + j) + \rho(k - j) - 2 \rho(j)\rho(k) \right] \end{align*}

• Special cases
• Marginally, for any $$j \geq 1$$, $\hat{\rho}(j) \sim \textrm{N}(\rho(j), n^{-1} w_{jj})$

• iid noise $w_{ij} = \begin{cases} 1, & \text{if } i = j,\\ 0, & \text{otherwise} \end{cases} \Longleftrightarrow \hat{\rho}(k) \sim \textrm{N}(0, 1/n), \ k = 1, \ldots, n$

# Forecast Stationary Time Series

## Best linear predictor: minimizes MSE

### Best linear predictor: definition

• For a stationary time series $$\{X_t\}$$ with known mean $$\mu$$ and ACVF $$\gamma$$, our goal is to find the linear combination of $$1, X_n, X_{n-1}, \ldots, X_1$$ that forecasts $$X_{n+h}$$ with minimum mean squared error

• Best linear predictor: $P_n X_{n + h} = a_0 + a_1 X_n + \cdots + a_n X_1 = a_0 + \sum_{i=1}^n a_i X_{n+1-i}$

• We need to find the coefficients $$a_0, a_1, \ldots, a_n$$ that minimize $E(X_{n + h} - a_0 - a_1 X_n - \cdots - a_n X_1)^2$
• We can take partial derivatives and solve a system of equations \begin{align*} & E\left[ X_{n + h} - a_0 - \sum_{i=1}^n a_i X_{n+1-i}\right] = 0,\\ & E\left[ \left(X_{n + h} - a_0 - \sum_{i=1}^n a_i X_{n+1-i}\right) X_{n+1-j}\right] = 0, \ j = 1, \ldots, n \end{align*}

### Best linear predictor: the solution

• Plugging the solution $$a_0 = \mu \left( 1 - \sum_{i=1}^n a_i \right)$$ in, the linear pedictor becomes $P_n X_{n + h} = \mu + \sum_{i=1}^n a_i (X_{n+1-i} - \mu)$

• The solution of coefficients $\mathbf{a}_n = (a_1, \ldots, a_n)' = \boldsymbol\Gamma_n^{-1} \boldsymbol\gamma_n(h)$
• $$\boldsymbol\Gamma_n = \left[ \gamma(i-j) \right]_{i, j = 1}^n$$ and $$\boldsymbol\gamma_n (h) = \left( \gamma(h), \gamma(h+1), \ldots, \gamma(h + n - 1) \right)'$$

### Best linear predictor $$\hat{X}_{t+h} = P_n X_{n + h}$$: properties

• Unbiasness $E(\hat{X}_{t+h} - X_{t+h}) = 0$

• Mean squared error (MSE) \begin{align*} E(X_{t+h} - \hat{X}_{t+h})^2 & = E(X_{t+h}^2) - E(\hat{X}_{t+h}^2)\\ & = \gamma(0) - \mathbf{a}_n' \boldsymbol\gamma_n (h) % = \gamma(0) - \gamma_n (h)' \boldsymbol\Gamma_n^{-1} \boldsymbol\gamma_n (h) \end{align*}

• Orthogonality $E\left[ (\hat{X}_{t+h} - X_{t+h}) X_j \right] = 0, \quad j = 1, \ldots, n$

• In general, orthogonality means $E\left[ (\textrm{Error}) \times (\textrm{PredictorVariable}) \right] = 0$

### Example: one-step prediction of an AR$$(1)$$ series

• We predict $$X_{n+1}$$ from $$X_1, \ldots, X_n$$ $\hat{X}_{n+1} = \mu + a_1 (X_n - \mu) + \cdots a_n (X_1 - \mu)$

• The coefficients $$\mathbf{a}_n = (a_1, \ldots, a_n)'$$ satisfies $\left[ \begin{array}{ccccc} 1 & \phi & \phi^2 & \cdots & \phi^{n-1} \\ \phi & 1 & \phi & \cdots & \phi^{n-2} \\ \vdots & \vdots & \vdots & \vdots & \vdots \\ \phi^{n-1}& \phi^{n-2}& \phi^{n-3}& \cdots & 1 \\ \end{array} \right] \left[ \begin{array}{c} a_1 \\ a_2 \\ \vdots\\ a_n \\ \end{array} \right] = \left[ \begin{array}{c} \phi_1 \\ \phi^2 \\ \vdots\\ \phi^n \\ \end{array} \right]$

• By guessing, we find a solution $$(a_1, a_2, \ldots, a_n) = (\phi, 0, \ldots, 0)$$, i.e., $\hat{X}_{n+1} = \mu + \phi (X_n - \mu)$

• Does not depend on $$X_{n-1}, \ldots, X_1$$
• MSE $$E(X_{t+1} - \hat{X}_{t+1})^2 = \sigma^2$$

### WOLG, we can assume $$\mu=0$$ while predicting

• A stationary time series $$\{X_t\}$$ has mean $$\mu$$

• To predict its future values, we can first create another time series $Y_t = X_t - \mu$ and predict $$\hat{Y}_{n+h} = P_n(\hat{Y}_{n+h})$$ by $\hat{Y}_{n+h} = a_1 Y_n + \cdots + a_n Y_1$

• Since ACVF $$\gamma_Y(h) = \gamma_X(h)$$, the coefficients $$a_1, \ldots, a_n$$ are the same for $$\{X_t\}$$ and $$\{Y_t\}$$

• The best linear predictor for $$\hat{X}_{n+h} = P_n(\hat{X}_{n+h})$$ is $\hat{X}_{n+h} - \mu = a_1 (X_n - \mu) + \cdots + a_n (X_1 - \mu)$

### Prediction operator $$P(\cdot \mid \mathbf{W})$$

• $$X$$ and $$W_1, \ldots, W_n$$ are random variables with finte 2nd moments

• Note: $$W_1, \ldots, W_n$$ does not need to be stationary
• Best linear predictor: $\hat{X} = P(X \mid \mathbf{W}) = E(X) + a_1 \left[W_n - E(W_n)\right] + \cdots + a_n \left[W_1- E(W_1)\right]$

• Coefficients $$\mathbf{a} = (a_1, \ldots, a_n)'$$ satisfies $\boldsymbol\Gamma \mathbf{a} = \boldsymbol\gamma$ where $$\boldsymbol\Gamma = \left[ Cov(W_{n+1-i}, W_{n+1-j}) \right]^n_{i,j=1}$$ and $$\boldsymbol\gamma = \left[ Cov(X, W_n), \ldots, Cov(X, W_1) \right]'$$

### Properties of $$\hat{X} = P(X \mid \mathbf{W})$$

• Unbiased $$E(\hat{X} - X) = 0$$

• Orthogonal $$E[(\hat{X} - X) W_i] = 0$$ for $$i = 1, \ldots n$$

• MSE $E(\hat{X} - X)^2 = Var(X) - (a_1, \ldots, a_n) \left[ \begin{array}{c} Cov(X, W_n) \\ \vdots \\ Cov(X, W_1) \\ \end{array} \right]$

• Linear $P(\alpha_1 X_1 + \alpha_2 X_2 + \beta \mid \mathbf{W}) = \alpha_1 P(X_1 \mid \mathbf{W}) + \alpha_2 P(X_2 \mid \mathbf{W}) + \beta$

• Extreme cases
• Perfect prediction $P(\sum_{i=1}^n \alpha_i W_j + \beta\mid \mathbf{W}) = \sum_{i=1}^n \alpha_i W_j + \beta$
• Uncorrelated: if $$Cov(X, W_i) = 0$$ for all $$i = 1, \ldots, n$$, then $P(X \mid \mathbf{W}) = E(X)$

### Examples: predictions of AR$$(p)$$ series

• A time series $$\{X_t\}$$ is an autoregression of order $$p$$, i.e., AR$$(p)$$, if it is stationary and satisfies $X_t = \phi_1 X_{t-1} + \phi_2 X_{t-2} + \cdots + \phi_p X_{t-p} + Z_t$ where $$\{Z_t\} \sim \textrm{WN}(0, \sigma^2)$$, and $$Cov(X_s, Z_t) = 0$$ for all $$s < t$$

• When $$n>p$$, the one-step prediction of an AR$$(p)$$ series is $P_n X_{n+1} = \phi_1 X_{n} + \phi_2 X_{n-1} + \cdots + \phi_p X_{n+1-p}$ with MSE $$E\left(X_{n+1} - P_n X_{n+1} \right)^2 = E(Z_{n+1})^2 = \sigma^2$$

• $$h$$-step prediction of an AR$$(1)$$ series (proof by recursions) $P_n X_{n+h} = \phi^h X_n, \quad \textrm{MSE} = \sigma^2\frac{1-\phi^{2h}}{1-\phi^2}$

## Recursive methods: the Durbin-Levinson and Innovation Algorithms

### Recursive methods for one-step prediction

• The best linear predictor solution $$\mathbf{a} = \boldsymbol\Gamma^{-1} \boldsymbol\gamma$$ needs matrix inversion

• Alternatively, we can use recursion to simplify one-step prediction of $$P_n X_{n + 1}$$, based on $$P_j X_{j+1}$$ for $$j = 1, \ldots, n-1$$

• We will introduce
• Durbin-Levinson algorithms: good for AR$$(p)$$
• Innovation algorithm: good for MA$$(q)$$; innovations are uncorrelated

### Durbin-Levinson algorithm

• Assume $$\{X_t\}$$ is mean zero, stationary, with ACVF $$\gamma(h)$$ $\hat{X}_{n+1} = \phi_{n,1} X_n + \cdots \phi_{n,n} X_1, \quad \textrm{with MSE } v_n = E(\hat{X}_{n+1} - X_{n+1})^2$
1. Start with $$\hat{X}_1 = 0$$ and $$v_0 = \gamma(0)$$

For $$n = 1,2, \ldots$$, compute step 2-4 successively

1. Compute $$\phi_{n,n}$$ (partial autocorrelation function (PACF) at lag $$n$$) $\phi_{n,n} = \left[ \gamma(n) - \sum_{j=1}^{n-1} \phi_{n-1, j} \gamma(n-j) \right]/v_{n-1}$

2. Compute $$\phi_{n, 1}, \ldots, \phi_{n, n-1}$$ $\left[ \begin{array}{c} \phi_{n,1} \\ \vdots \\ \phi_{n, n-1} \\ \end{array} \right] = \left[ \begin{array}{c} \phi_{n-1, 1} \\ \vdots \\ \phi_{n-1, n-1} \\ \end{array} \right]- \phi_{n,n} \left[ \begin{array}{c} \phi_{n-1, n-1} \\ \vdots \\ \phi_{n-1, 1} \\ \end{array} \right]$

3. Compute $$v_n$$ $v_n = v_{n-1}(1 - \phi_{n, n}^2)$

### Innovation algorithm

• Assume $$\{X_t\}$$ is any mean zero (not necessarily stationary) time series with covariance $$\kappa(i,j) = Cov(X_i, X_j)$$

• Predict $$\hat{X}_{n+1} = P_n X_{n+1}$$ based on innovations, or one-step prediction errors $$X_j - \hat{X}_j$$, $$j = 1, \ldots, n$$ $\hat{X}_{n+1} = \theta_{n,1} (X_n - \hat{X}_n) + \cdots + \theta_{n,n} (X_1 - \hat{X}_1)\quad \textrm{with MSE } v_n$

1. Start with $$\hat{X}_1 = 0$$ and $$v_0 = \kappa(1, 1)$$

For $$n = 1,2, \ldots$$, compute step 2-3 successively

1. For $$k = 0, 1, \ldots, n-1$$, compute coefficients $\theta_{n, n-k} = \left[ \kappa(n+1, k+1) - \sum_{j=0}^{k-1} \theta_{k, k-j} \theta_{n, n-j} v_j \right]/v_k$

2. Compute the MSE $v_n = \kappa(n+1, n+1) - \sum_{j=0}^{n-1} \theta_{n, n-j}^2 v_j$

### $$h$$-step predictors using innovations

• For any $$k \geq 1$$, orthoganlity ensures $E\left[ \left(X_{n+k} - P_{n+k-1} X_{n+k}\right) X_j \right] = 0, \quad j = 1, \ldots, n$ Thus, we have $P_n(X_{n+k} - P_{n+k-1} X_{n+k}) = 0$

• The $$h$$-step prediction: \begin{align*} P_n X_{n+h} &= P_n P_{n+h-1} X_{n+h}\\ &= P_n \left[ \sum_{j=1}^{n+h-1} \theta_{n+h-1, j} \left(X_{n+h-j}- \hat{X}_{n+h-j} \right) \right]\\ &= \sum_{j=h}^{n+h-1} \theta_{n+h-1, j} \left(X_{n+h-j}- \hat{X}_{n+h-j} \right) \end{align*}

### References

• Brockwell, Peter J. and Davis, Richard A. (2016), Introduction to Time Series and Forecasting, Third Edition. New York: Springer