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Chapter Introduction
NAG Toolbox

# NAG Toolbox: nag_tsa_inhom_iema (g13me)

## Purpose

nag_tsa_inhom_iema (g13me) calculates the iterated exponential moving average for an inhomogeneous time series.

## Syntax

[iema, pn, rcomm, ifail] = g13me(iema, t, tau, m, sinit, inter, 'nb', nb, 'pn', pn, 'rcomm', rcomm)
[iema, pn, rcomm, ifail] = nag_tsa_inhom_iema(iema, t, tau, m, sinit, inter, 'nb', nb, 'pn', pn, 'rcomm', rcomm)

## Description

nag_tsa_inhom_iema (g13me) calculates the iterated exponential moving average for an inhomogeneous time series. The time series is represented by two vectors of length n$n$; a vector of times, t$t$; and a vector of values, z$z$. Each element of the time series is therefore composed of the pair of scalar values (ti,zi)$\left({t}_{\mathit{i}},{z}_{i}\right)$, for i = 1,2,,n$\mathit{i}=1,2,\dots ,n$. Time can be measured in any arbitrary units, as long as all elements of t$t$ use the same units.
The exponential moving average (EMA), with parameter τ$\tau$, is an average operator, with the exponentially decaying kernel given by
 (e − ti / τ)/τ . $e -ti/τ τ .$
The exponential form of this kernel gives rise to the following iterative formula for the EMA operator (see Zumbach and Müller (2001)):
 EMA [τ ; z] (ti) = μ EMA [τ ; z] (ti − 1) + (ν − μ) zi − 1 + (1 − ν) zi $EMA [τ;z] (ti) = μ ⁢ EMA [τ;z] ( ti-1 ) + (ν-μ) ⁢ zi-1 + (1-ν) ⁢ zi$
where
 μ = e − α   and   α = ( ti − ti − 1 )/τ . $μ = e-α and α = ti - ti-1 τ .$
The value of ν$\nu$ depends on the method of interpolation chosen. nag_tsa_inhom_iema (g13me) gives the option of three interpolation methods:
 1 Previous point: ν = 1$\nu =1$; 2 Linear: ν = (1 − μ) / α $\nu =\left(1-\mu \right)/\alpha$; 3 Next point: ν = μ$\nu =\mu$.
The m$m$-iterated exponential moving average, EMA[τ,m ; z](ti)$\text{EMA}\left[\tau ,m;z\right]\left({t}_{i}\right)$, m > 1$m>1$, is defined using the recursive formula:
 EMA [τ,m ; z] = EMA [τ ; EMA[τ,m − 1 ; z]] $EMA [ τ,m;z ] = EMA [ τ ; EMA [ τ,m-1 ; z ] ]$
with
 EMA [τ,1 ; z] = EMA [τ ; z] . $EMA [ τ,1;z ] = EMA [ τ ; z ] .$
For large datasets or where all the data is not available at the same time, z$z$ and t$t$ can be split into arbitrary sized blocks and nag_tsa_inhom_iema (g13me) called multiple times.

## References

Dacorogna M M, Gencay R, Müller U, Olsen R B and Pictet O V (2001) An Introduction to High-frequency Finance Academic Press
Zumbach G O and Müller U A (2001) Operators on inhomogeneous time series International Journal of Theoretical and Applied Finance 4(1) 147–178

## Parameters

### Compulsory Input Parameters

1:     iema(nb) – double array
nb, the dimension of the array, must satisfy the constraint nb0${\mathbf{nb}}\ge 0$.
zi${z}_{\mathit{i}}$, the current block of observations, for i = k + 1,,k + b$\mathit{i}=k+1,\dots ,k+b$, where k$k$ is the number of observations processed so far, i.e., the value supplied in pn on entry.
2:     t(nb) – double array
nb, the dimension of the array, must satisfy the constraint nb0${\mathbf{nb}}\ge 0$.
ti${t}_{i}$, the times for the current block of observations, for i = k + 1,,k + b$\mathit{i}=k+1,\dots ,k+b$, where k$k$ is the number of observations processed so far, i.e., the value supplied in pn on entry.
If titi1${t}_{i}\le {t}_{i-1}$, ${\mathbf{ifail}}={\mathbf{31}}$ will be returned, but nag_tsa_inhom_iema (g13me) will continue as if t$t$ was strictly increasing by using the absolute value.
3:     tau – double scalar
τ$\tau$, the parameter controlling the rate of decay, which must be sufficiently large that eα${e}^{-\alpha }$, α = (titi1) / τ$\alpha =\left({t}_{i}-{t}_{i-1}\right)/\tau$ can be calculated without overflowing, for all i$i$.
Constraint: tau > 0.0${\mathbf{tau}}>0.0$.
4:     m – int64int32nag_int scalar
m$m$, the number of times the EMA operator is to be iterated.
Constraint: m1${\mathbf{m}}\ge 1$.
5:     sinit(m + 2${\mathbf{m}}+2$) – double array
If pn = 0${\mathbf{pn}}=0$, the values used to start the iterative process, with
• sinit(1) = t0${\mathbf{sinit}}\left(1\right)={t}_{0}$,
• sinit(2) = z0${\mathbf{sinit}}\left(2\right)={z}_{0}$,
• sinit(j + 2) = EMA [τ,j ; z] (t0) ${\mathbf{sinit}}\left(\mathit{j}+2\right)=\text{EMA}\left[\tau ,\mathit{j};z\right]\left({t}_{0}\right)$, for j = 1,2,,m$\mathit{j}=1,2,\dots ,{\mathbf{m}}$.
If pn0${\mathbf{pn}}\ne 0$, sinit is not referenced.
6:     inter(2$2$) – int64int32nag_int array
The type of interpolation used with inter(1)${\mathbf{inter}}\left(1\right)$ indicating the interpolation method to use when calculating EMA[τ,1 ; z]$\text{EMA}\left[\tau ,1;z\right]$ and inter(2)${\mathbf{inter}}\left(2\right)$ the interpolation method to use when calculating EMA[τ,j ; z]$\text{EMA}\left[\tau ,j;z\right]$, j > 1$j>1$.
Three types of interpolation are possible:
inter(i) = 1${\mathbf{inter}}\left(i\right)=1$
Previous point, with ν = 1$\nu =1$.
inter(i) = 2${\mathbf{inter}}\left(i\right)=2$
Linear, with ν = (1μ) / α$\nu =\left(1-\mu \right)/\alpha$.
inter(i) = 3${\mathbf{inter}}\left(i\right)=3$
Next point, ν = μ$\nu =\mu$.
Zumbach and Müller (2001) recommend that linear interpolation is used in second and subsequent iterations, i.e., inter(2) = 2${\mathbf{inter}}\left(2\right)=2$, irrespective of the interpolation method used at the first iteration, i.e., the value of inter(1)${\mathbf{inter}}\left(1\right)$.
Constraint: inter(i) = 1${\mathbf{inter}}\left(\mathit{i}\right)=1$, 2$2$ or 3$3$, for i = 1,2$\mathit{i}=1,2$.

### Optional Input Parameters

1:     nb – int64int32nag_int scalar
Default: The dimension of the arrays iema, t. (An error is raised if these dimensions are not equal.)
b$b$, the number of observations in the current block of data. The size of the block of data supplied in iema and t can vary; therefore nb can change between calls to nag_tsa_inhom_iema (g13me).
Constraint: nb0${\mathbf{nb}}\ge 0$.
2:     pn – int64int32nag_int scalar
k$k$, the number of observations processed so far. On the first call to nag_tsa_inhom_iema (g13me), or when starting to summarise a new dataset, pn must be set to 0$0$. On subsequent calls it must be the same value as returned by the last call to nag_tsa_inhom_iema (g13me).
Default: 0$0$
Constraint: pn0${\mathbf{pn}}\ge 0$.
3:     rcomm(m + 20${\mathbf{m}}+20$) – double array
Communication array, used to store information between calls to nag_tsa_inhom_iema (g13me). On the first call to nag_tsa_inhom_iema (g13me), or if all the data is provided in one go, rcomm need not be provided.

lrcomm

### Output Parameters

1:     iema(nb) – double array
The iterated EMA, with iema(i) = EMA [τ,m ; z] (ti) ${\mathbf{iema}}\left(i\right)=\text{EMA}\left[\tau ,m;z\right]\left({t}_{i}\right)$.
2:     pn – int64int32nag_int scalar
Default: 0$0$
k + b$k+b$, the updated number of observations processed so far.
3:     rcomm(m + 20${\mathbf{m}}+20$) – double array
Communication array, used to store information between calls to nag_tsa_inhom_iema (g13me).
4:     ifail – int64int32nag_int scalar
${\mathrm{ifail}}={\mathbf{0}}$ unless the function detects an error (see [Error Indicators and Warnings]).

## Error Indicators and Warnings

Errors or warnings detected by the function:

Cases prefixed with W are classified as warnings and do not generate an error of type NAG:error_n. See nag_issue_warnings.

ifail = 11${\mathbf{ifail}}=11$
Constraint: nb0${\mathbf{nb}}\ge 0$.
W ifail = 31${\mathbf{ifail}}=31$
Constraint: t should be strictly increasing.
ifail = 32${\mathbf{ifail}}=32$
Constraint: t(i)t(i1)${\mathbf{t}}\left(i\right)\ne {\mathbf{t}}\left(i-1\right)$ if linear interpolation is being used.
ifail = 41${\mathbf{ifail}}=41$
Constraint: tau > 0.0${\mathbf{tau}}>0.0$.
ifail = 42${\mathbf{ifail}}=42$
Constraint: if pn > 0${\mathbf{pn}}>0$ then tau must be unchanged since previous call.
ifail = 51${\mathbf{ifail}}=51$
Constraint: m1${\mathbf{m}}\ge 1$.
ifail = 52${\mathbf{ifail}}=52$
Constraint: if pn > 0${\mathbf{pn}}>0$ then m must be unchanged since previous call.
ifail = 71${\mathbf{ifail}}=71$
On entry, inter(1) = _${\mathbf{inter}}\left(1\right)=_$.
Constraint: inter(1) = 1${\mathbf{inter}}\left(1\right)=1$, 2$2$ or 3$3$.
ifail = 72${\mathbf{ifail}}=72$
On entry, inter(2) = _${\mathbf{inter}}\left(2\right)=_$.
Constraint: inter(2) = 1${\mathbf{inter}}\left(2\right)=1$, 2$2$ or 3$3$.
ifail = 73${\mathbf{ifail}}=73$
Constraint: if pn0${\mathbf{pn}}\ne 0$, inter must be unchanged since the previous call.
ifail = 81${\mathbf{ifail}}=81$
Constraint: pn0${\mathbf{pn}}\ge 0$.
ifail = 82${\mathbf{ifail}}=82$
Constraint: if pn > 0${\mathbf{pn}}>0$ then pn must be unchanged since previous call.
ifail = 91${\mathbf{ifail}}=91$
rcomm has been corrupted between calls.
ifail = 101${\mathbf{ifail}}=101$
Constraint: if pn = 0${\mathbf{pn}}=0$, lrcomm = 0$\mathit{lrcomm}=0$ or lrcommm + 20$\mathit{lrcomm}\ge {\mathbf{m}}+20$.
ifail = 102${\mathbf{ifail}}=102$
Constraint: if pn0${\mathbf{pn}}\ne 0$, lrcommm + 20$\mathit{lrcomm}\ge {\mathbf{m}}+20$.
W ifail = 301${\mathbf{ifail}}=301$
Truncation occurred to avoid overflow, check for extreme values in t, iema or for tau. Results are returned using the truncated values.
ifail = 999${\mathbf{ifail}}=-999$
Dynamic memory allocation failed.

## Accuracy

Not applicable.

Approximately 4m$4m$ real elements are internally allocated by nag_tsa_inhom_iema (g13me).
The more data you supply to nag_tsa_inhom_iema (g13me) in one call, i.e., the larger nb is, the more efficient the function will be, particularly if the function is being run using more than one thread.
Checks are made during the calculation of α$\alpha$ to avoid overflow. If a potential overflow is detected the offending value is replaced with a large positive or negative value, as appropriate, and the calculations performed based on the replacement values. In such cases ${\mathbf{ifail}}={\mathbf{301}}$ is returned. This should not occur in standard usage and will only occur if extreme values of iema, t or tau are supplied.

## Example

```function nag_tsa_inhom_iema_example
m = int64(2);
inter = [int64(3); 2];
tau = 2;
sinit = [5; 0.5; 0.5; 0.5];
nb = [5, 10, 15];
t = cell(3, 1);
iema = cell(3, 1);
t{1} = [7.5; 8.2; 18.1; 22.8; 25.8];
iema{1} = [0.6; 0.6; 0.8; 0.1; 0.2];
t{2} = [26.8; 31.1; 38.4; 45.9; 48.2; 48.9; 57.9; 58.5; 63.9; 65.2];
iema{2} = [0.2;  0.5;  0.7;  0.1;  0.4;  0.7;  0.8;  0.3;  0.2;  0.5];
t{3} = [66.6; 67.4; 69.3; 69.9; 73.0; 75.6; 77.0; 84.7; 86.8; 88.0; ...
88.5; 91.0; 93.0; 93.7; 94.0];
iema{3} = [0.2;  0.3;  0.8;  0.6;  0.1;  0.7;  0.9;  0.6;  0.3;  0.1;  ...
0.1;  0.4;  1.0;  1.0;  0.1];
fprintf('\n             Time       Iterated EMA\n');

% Loop over each block of data.
for i = 1:numel(nb)
if i == 1
% process first block and create pn and rcomm
[iema{i}, pn, rcomm, ifail] = ...
nag_tsa_inhom_iema(iema{i}, t{i}, tau, m, sinit, inter);
else
% Update the iterated EMA for this block of data, overwriting the input
% data with the iterated EMA.
[iema{i}, pn, rcomm, ifail] = ...
nag_tsa_inhom_iema(iema{i}, t{i}, tau, m, sinit, inter, ...
'pn', pn, 'rcomm', rcomm);
end

% Display the results for this block of data
for j=1:nb(i)
fprintf('%3d    %10.1f    %10.3f\n', pn-nb(i)+j, t{i}(j), iema{i}(j));
end
fprintf('\n');
end
```
```

Time       Iterated EMA
1           7.5         0.531
2           8.2         0.544
3          18.1         0.754
4          22.8         0.406
5          25.8         0.232

6          26.8         0.217
7          31.1         0.357
8          38.4         0.630
9          45.9         0.263
10          48.2         0.241
11          48.9         0.279
12          57.9         0.713
13          58.5         0.717
14          63.9         0.385
15          65.2         0.346

16          66.6         0.330
17          67.4         0.315
18          69.3         0.409
19          69.9         0.459
20          73.0         0.377
21          75.6         0.411
22          77.0         0.536
23          84.7         0.632
24          86.8         0.538
25          88.0         0.444
26          88.5         0.401
27          91.0         0.331
28          93.0         0.495
29          93.7         0.585
30          94.0         0.612

```
```function g13me_example
m = int64(2);
inter = [int64(3); 2];
tau = 2;
sinit = [5; 0.5; 0.5; 0.5];
nb = [5, 10, 15];
t = cell(3, 1);
iema = cell(3, 1);
t{1} = [7.5; 8.2; 18.1; 22.8; 25.8];
iema{1} = [0.6; 0.6; 0.8; 0.1; 0.2];
t{2} = [26.8; 31.1; 38.4; 45.9; 48.2; 48.9; 57.9; 58.5; 63.9; 65.2];
iema{2} = [0.2;  0.5;  0.7;  0.1;  0.4;  0.7;  0.8;  0.3;  0.2;  0.5];
t{3} = [66.6; 67.4; 69.3; 69.9; 73.0; 75.6; 77.0; 84.7; 86.8; 88.0; ...
88.5; 91.0; 93.0; 93.7; 94.0];
iema{3} = [0.2;  0.3;  0.8;  0.6;  0.1;  0.7;  0.9;  0.6;  0.3;  0.1;  ...
0.1;  0.4;  1.0;  1.0;  0.1];
fprintf('\n             Time       Iterated EMA\n');

% Loop over each block of data.
for i = 1:numel(nb)
if i == 1
% process first block and create pn and rcomm
[iema{i}, pn, rcomm, ifail] = ...
g13me(iema{i}, t{i}, tau, m, sinit, inter);
else
% Update the iterated EMA for this block of data, overwriting the input
% data with the iterated EMA.
[iema{i}, pn, rcomm, ifail] = ...
g13me(iema{i}, t{i}, tau, m, sinit, inter, 'pn', pn, 'rcomm', rcomm);
end

% Display the results for this block of data
for j=1:nb(i)
fprintf('%3d    %10.1f    %10.3f\n', pn-nb(i)+j, t{i}(j), iema{i}(j));
end
fprintf('\n');
end
```
```

Time       Iterated EMA
1           7.5         0.531
2           8.2         0.544
3          18.1         0.754
4          22.8         0.406
5          25.8         0.232

6          26.8         0.217
7          31.1         0.357
8          38.4         0.630
9          45.9         0.263
10          48.2         0.241
11          48.9         0.279
12          57.9         0.713
13          58.5         0.717
14          63.9         0.385
15          65.2         0.346

16          66.6         0.330
17          67.4         0.315
18          69.3         0.409
19          69.9         0.459
20          73.0         0.377
21          75.6         0.411
22          77.0         0.536
23          84.7         0.632
24          86.8         0.538
25          88.0         0.444
26          88.5         0.401
27          91.0         0.331
28          93.0         0.495
29          93.7         0.585
30          94.0         0.612

```