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<span class="menu-text"><span class="chapter-number">11</span> <span class="chapter-title">Criando modelos customizados com sktime</span></span></a>
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<li><a href="#o-problema-da-tendência" id="toc-o-problema-da-tendência" class="nav-link active" data-scroll-target="#o-problema-da-tendência"><span class="header-section-number">7.1</span> O problema da tendência</a></li>
<li><a href="#usando-modelos-de-ml-com-sktime" id="toc-usando-modelos-de-ml-com-sktime" class="nav-link" data-scroll-target="#usando-modelos-de-ml-com-sktime"><span class="header-section-number">7.2</span> Usando modelos de ML com sktime</a>
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<li><a href="#solução-1-diferenciação" id="toc-solução-1-diferenciação" class="nav-link" data-scroll-target="#solução-1-diferenciação"><span class="header-section-number">7.2.1</span> Solução 1: Diferenciação</a></li>
<li><a href="#solução-2-normalização-por-janela" id="toc-solução-2-normalização-por-janela" class="nav-link" data-scroll-target="#solução-2-normalização-por-janela"><span class="header-section-number">7.2.2</span> Solução 2: Normalização por janela</a></li>
<li><a href="#modelo-direto-e-recursivo" id="toc-modelo-direto-e-recursivo" class="nav-link" data-scroll-target="#modelo-direto-e-recursivo"><span class="header-section-number">7.2.3</span> Modelo direto e recursivo</a></li>
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<div class="quarto-title">
<h1 class="title"><span class="chapter-number">7</span> <span class="chapter-title">Modelos de Machine Learning</span></h1>
</div>
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</div>
</header>
<p>Nesse capítulo vamos ver as maneiras de usar modelos de Machine Learning para forecasting. Aqui é onde mais acontecem erros de novos praticantes, pois muitas vezes tentam aplicar modelos de ML diretamente na série temporal.</p>
<p>Para usar um modelo de ML, precisamos transformar a série temporal em um problema de regressão tradicional. Isso é feito criando janelas deslizantes (sliding windows) da série temporal, onde cada janela é usada como uma amostra de treinamento para o modelo de ML.</p>
<p>Ou seja, se temos uma série temporal <span class="math inline">\((y_t)\)</span>, podemos criar janelas de tamanho <span class="math inline">\(n\)</span> e usar os valores <span class="math inline">\(y_{t-n}, y_{t-n+1}, \ldots, y_{t-1}\)</span> como características (features) para prever o valor <span class="math inline">\(y_t\)</span>.</p>
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img src="img/reduction.png" class="img-fluid figure-img"></p>
<figcaption>img/reduction.png</figcaption>
</figure>
</div>
<p>Para prever mais de um passo à frente, existem duas abordagens:</p>
<ol type="1">
<li><strong>Previsão recursive</strong>: se queremos prever <span class="math inline">\(h\)</span> passos à frente, podemos usar o modelo para prever <span class="math inline">\(y_{t+1}\)</span>, depois usar essa previsão para prever <span class="math inline">\(y_{t+2}\)</span>, e assim por diante, até <span class="math inline">\(y_{t+h}\)</span>. Isso pode levar a erros acumulados, pois cada previsão depende das previsões anteriores.</li>
<li><strong>Previsão direta</strong>: em vez de prever um passo de cada vez, podemos treinar o modelo para prever todos os <span class="math inline">\(h\)</span> passos à frente de uma vez. Isso pode ser feito usando um modelo para cada <span class="math inline">\(h\)</span> ou usando um modelo que prevê um vetor de <span class="math inline">\(h\)</span> valores.</li>
</ol>
<p>A verdade é que as duas abordagens podem ser vistas como uma: a previsão recursiva pode ser vista como uma previsão direta para <span class="math inline">\(h=1\)</span>.</p>
<div id="7a0ed700" class="cell" data-execution_count="2">
<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.datasets.retail <span class="im">import</span> SyntheticRetail</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.utils.plotting <span class="im">import</span> plot_series</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.forecasting.naive <span class="im">import</span> NaiveForecaster</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>dataset <span class="op">=</span> SyntheticRetail(<span class="st">"univariate"</span>)</span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>y_train, X_train, y_test, X_test <span class="op">=</span> dataset.load(</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a> <span class="st">"y_train"</span>, <span class="st">"X_train"</span>, <span class="st">"y_test"</span>, <span class="st">"X_test"</span></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</details>
</div>
<section id="o-problema-da-tendência" class="level2" data-number="7.1">
<h2 data-number="7.1" class="anchored" data-anchor-id="o-problema-da-tendência"><span class="header-section-number">7.1</span> O problema da tendência</h2>
<p>A tendência em séries temporais é como um constante problema de data drift:</p>
<div id="5a68b9e2" class="cell" data-execution_count="3">
<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>_X <span class="op">=</span> [y_train.iloc[i : i <span class="op">+</span> <span class="dv">7</span>] <span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(<span class="dv">0</span>, <span class="dv">700</span>)]</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>_X_test <span class="op">=</span> [y_train.iloc[i : i <span class="op">+</span> <span class="dv">7</span>] <span class="cf">for</span> i <span class="kw">in</span> <span class="bu">range</span>(<span class="dv">700</span>, <span class="dv">800</span>)]</span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> set_index(x):</span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> x.index <span class="op">=</span> <span class="bu">range</span>(<span class="bu">len</span>(x))</span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> x</span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-10"><a href="#cb2-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-11"><a href="#cb2-11" aria-hidden="true" tabindex="-1"></a>_X <span class="op">=</span> [set_index(x) <span class="cf">for</span> x <span class="kw">in</span> _X]</span>
<span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a>_X_test <span class="op">=</span> [set_index(x) <span class="cf">for</span> x <span class="kw">in</span> _X_test]</span>
<span id="cb2-13"><a href="#cb2-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-14"><a href="#cb2-14" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
<span id="cb2-15"><a href="#cb2-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-16"><a href="#cb2-16" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots()</span>
<span id="cb2-17"><a href="#cb2-17" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> x <span class="kw">in</span> _X:</span>
<span id="cb2-18"><a href="#cb2-18" aria-hidden="true" tabindex="-1"></a> ax.plot(x, color<span class="op">=</span><span class="st">"gray"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>)</span>
<span id="cb2-19"><a href="#cb2-19" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> x <span class="kw">in</span> _X_test:</span>
<span id="cb2-20"><a href="#cb2-20" aria-hidden="true" tabindex="-1"></a> ax.plot(x, color<span class="op">=</span><span class="st">"red"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>)</span>
<span id="cb2-21"><a href="#cb2-21" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-22"><a href="#cb2-22" aria-hidden="true" tabindex="-1"></a><span class="co"># Add legend, with 1 red line for test and 1 gray for train</span></span>
<span id="cb2-23"><a href="#cb2-23" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> matplotlib.lines <span class="im">import</span> Line2D</span>
<span id="cb2-24"><a href="#cb2-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-25"><a href="#cb2-25" aria-hidden="true" tabindex="-1"></a>legend_handles <span class="op">=</span> [</span>
<span id="cb2-26"><a href="#cb2-26" aria-hidden="true" tabindex="-1"></a> Line2D([<span class="dv">0</span>], [<span class="dv">0</span>], color<span class="op">=</span><span class="st">"gray"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>, lw<span class="op">=</span><span class="dv">2</span>, label<span class="op">=</span><span class="st">"Treino"</span>),</span>
<span id="cb2-27"><a href="#cb2-27" aria-hidden="true" tabindex="-1"></a> Line2D([<span class="dv">0</span>], [<span class="dv">0</span>], color<span class="op">=</span><span class="st">"red"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>, lw<span class="op">=</span><span class="dv">2</span>, label<span class="op">=</span><span class="st">"Teste"</span>),</span>
<span id="cb2-28"><a href="#cb2-28" aria-hidden="true" tabindex="-1"></a>]</span>
<span id="cb2-29"><a href="#cb2-29" aria-hidden="true" tabindex="-1"></a>ax.legend(handles<span class="op">=</span>legend_handles, loc<span class="op">=</span><span class="st">"best"</span>)</span>
<span id="cb2-30"><a href="#cb2-30" aria-hidden="true" tabindex="-1"></a>ax.set_title(<span class="st">"Série original - Magnitudes diferentes para cada janela"</span>)</span>
<span id="cb2-31"><a href="#cb2-31" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</details>
<div class="cell-output cell-output-display">
<div>
<figure class="figure">
<p><img src="ml_models_files/figure-html/cell-4-output-1.png" width="575" height="432" class="figure-img"></p>
</figure>
</div>
</div>
</div>
<p>Quando criamos nossas janelas e olhamos treine e teste, esse problema fica claro. A informação de uma série em treino não é util para prever a série de teste, pois elas estão em magnitudes diferentes.</p>
<p>Uma possível solução para isso é normalizar cada janela, dividindo pelo valor médio da janela. Assim, todas as janelas ficam na mesma escala:</p>
<div id="ace59291" class="cell" data-execution_count="4">
<details class="code-fold">
<summary>Code</summary>
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>_X <span class="op">=</span> [x <span class="op">/</span> x.mean() <span class="cf">for</span> x <span class="kw">in</span> _X]</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>_X_test <span class="op">=</span> [x <span class="op">/</span> x.mean() <span class="cf">for</span> x <span class="kw">in</span> _X_test]</span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>fig, ax <span class="op">=</span> plt.subplots()</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> x <span class="kw">in</span> _X:</span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a> ax.plot(x, color<span class="op">=</span><span class="st">"gray"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>)</span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> x <span class="kw">in</span> _X_test:</span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a> ax.plot(x, color<span class="op">=</span><span class="st">"red"</span>, alpha<span class="op">=</span><span class="fl">0.3</span>)</span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>ax.legend(handles<span class="op">=</span>legend_handles, loc<span class="op">=</span><span class="st">"best"</span>)</span>
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>ax.set_title(<span class="st">"Série normalizada"</span>)</span>
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a>fig.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
</details>
<div class="cell-output cell-output-display">
<div>
<figure class="figure">
<p><img src="ml_models_files/figure-html/cell-5-output-1.png" width="571" height="431" class="figure-img"></p>
</figure>
</div>
</div>
</div>
<p>e podemos prever sem problemas.</p>
<p>Outra possibilidade é a diferenciação, como já vimos em capítulos anteriores. A diferenciação remove a tendência da série, tornando-a estacionária.</p>
</section>
<section id="usando-modelos-de-ml-com-sktime" class="level2" data-number="7.2">
<h2 data-number="7.2" class="anchored" data-anchor-id="usando-modelos-de-ml-com-sktime"><span class="header-section-number">7.2</span> Usando modelos de ML com sktime</h2>
<p>Primeiro, vamos import <code>ReductionForecaster</code>, que é a classe que implementa a abordagem de janelas deslizantes para usar modelos de ML em séries temporais. Vamos testar um primeiro caso sem nenhum tipo de preprocessamento, apenas criando as janelas:</p>
<div id="64046b80" class="cell" data-execution_count="5">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tsbook.forecasting.reduction <span class="im">import</span> ReductionForecaster</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.ensemble <span class="im">import</span> RandomForestRegressor</span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> ReductionForecaster(</span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a> RandomForestRegressor(n_estimators<span class="op">=</span><span class="dv">100</span>, random_state<span class="op">=</span><span class="dv">42</span>),</span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a> window_length<span class="op">=</span><span class="dv">30</span>,</span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>model.fit(y_train, X<span class="op">=</span>X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-display" data-execution_count="5">
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flex-direction: column;
}
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-parallel-item:first-child::after {
align-self: flex-end;
width: 50%;
}
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-parallel-item:last-child::after {
align-self: flex-start;
width: 50%;
}
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-parallel-item:only-child::after {
width: 0;
}
/* Serial-specific style estimator block */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-serial {
display: flex;
flex-direction: column;
align-items: center;
background-color: var(--sklearn-color-background);
padding-right: 1em;
padding-left: 1em;
}
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
*/
/* Pipeline and ColumnTransformer style (default) */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-toggleable {
/* Default theme specific background. It is overwritten whether we have a
specific estimator or a Pipeline/ColumnTransformer */
background-color: var(--sklearn-color-background);
}
/* Toggleable label */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e label.sk-toggleable__label {
cursor: pointer;
display: block;
width: 100%;
margin-bottom: 0;
padding: 0.5em;
box-sizing: border-box;
text-align: center;
}
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e label.sk-toggleable__label-arrow:before {
/* Arrow on the left of the label */
content: "▸";
float: left;
margin-right: 0.25em;
color: var(--sklearn-color-icon);
}
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e label.sk-toggleable__label-arrow:hover:before {
color: var(--sklearn-color-text);
}
/* Toggleable content - dropdown */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-toggleable__content {
max-height: 0;
max-width: 0;
overflow: hidden;
text-align: left;
background-color: var(--sklearn-color-level-0);
}
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-toggleable__content pre {
margin: 0.2em;
border-radius: 0.25em;
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-level-0);
}
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e input.sk-toggleable__control:checked~div.sk-toggleable__content {
/* Expand drop-down */
max-height: 200px;
max-width: 100%;
overflow: auto;
}
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
content: "▾";
}
/* Pipeline/ColumnTransformer-specific style */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-level-2);
}
/* Estimator-specific style */
/* Colorize estimator box */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
/* unfitted */
background-color: var(--sklearn-color-level-2);
}
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-label label.sk-toggleable__label,
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-label label {
/* The background is the default theme color */
color: var(--sklearn-color-text-on-default-background);
}
/* On hover, darken the color of the background */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-label:hover label.sk-toggleable__label {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-level-2);
}
/* Estimator label */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-label label {
font-family: monospace;
font-weight: bold;
display: inline-block;
line-height: 1.2em;
}
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-label-container {
text-align: center;
}
/* Estimator-specific */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-estimator {
font-family: monospace;
border: 1px dotted var(--sklearn-color-border-box);
border-radius: 0.25em;
box-sizing: border-box;
margin-bottom: 0.5em;
background-color: var(--sklearn-color-level-0);
}
/* on hover */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e div.sk-estimator:hover {
background-color: var(--sklearn-color-level-2);
}
/* Specification for estimator info */
.sk-estimator-doc-link,
a:link.sk-estimator-doc-link,
a:visited.sk-estimator-doc-link {
float: right;
font-size: smaller;
line-height: 1em;
font-family: monospace;
background-color: var(--sklearn-color-background);
border-radius: 1em;
height: 1em;
width: 1em;
text-decoration: none !important;
margin-left: 1ex;
border: var(--sklearn-color-level-1) 1pt solid;
color: var(--sklearn-color-level-1);
}
/* On hover */
div.sk-estimator:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover,
div.sk-label-container:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover {
background-color: var(--sklearn-color-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
/* Span, style for the box shown on hovering the info icon */
.sk-estimator-doc-link span {
display: none;
z-index: 9999;
position: relative;
font-weight: normal;
right: .2ex;
padding: .5ex;
margin: .5ex;
width: min-content;
min-width: 20ex;
max-width: 50ex;
color: var(--sklearn-color-text);
box-shadow: 2pt 2pt 4pt #999;
background: var(--sklearn-color-level-0);
border: .5pt solid var(--sklearn-color-level-3);
}
.sk-estimator-doc-link:hover span {
display: block;
}
/* "?"-specific style due to the `<a>` HTML tag */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e a.estimator_doc_link {
float: right;
font-size: 1rem;
line-height: 1em;
font-family: monospace;
background-color: var(--sklearn-color-background);
border-radius: 1rem;
height: 1rem;
width: 1rem;
text-decoration: none;
color: var(--sklearn-color-level-1);
border: var(--sklearn-color-level-1) 1pt solid;
}
/* On hover */
#sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e a.estimator_doc_link:hover {
background-color: var(--sklearn-color-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
</style><div id="sk-6d271bb6-dd3e-4c0b-ae52-a2009c7d4d2e" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>ReductionForecaster(estimator=RandomForestRegressor(random_state=42),
window_length=30)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden=""><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('90a45fbe-c3cd-4173-b11e-e327ff0c462c')" type="checkbox"><label for="UUID('90a45fbe-c3cd-4173-b11e-e327ff0c462c')" class="sk-toggleable__label sk-toggleable__label-arrow">ReductionForecaster</label><div class="sk-toggleable__content"><pre>ReductionForecaster(estimator=RandomForestRegressor(random_state=42),
window_length=30)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('15ba3e0c-7b66-4b74-ae6f-1c8c37f97177')" type="checkbox"><label for="UUID('15ba3e0c-7b66-4b74-ae6f-1c8c37f97177')" class="sk-toggleable__label sk-toggleable__label-arrow">estimator: RandomForestRegressor</label><div class="sk-toggleable__content"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="UUID('80405df2-f98a-4388-8663-0e63b94f37e5')" type="checkbox"><label for="UUID('80405df2-f98a-4388-8663-0e63b94f37e5')" class="sk-toggleable__label sk-toggleable__label-arrow">RandomForestRegressor<a class="sk-estimator-doc-link" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></label><div class="sk-toggleable__content"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>
</div>
</div>
<div id="10c64b0b" class="cell" data-execution_count="6">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>y_pred <span class="op">=</span> model.predict(fh<span class="op">=</span>y_test.index, X<span class="op">=</span>X_test)</span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>plot_series(y_train, y_test, y_pred, labels<span class="op">=</span>[<span class="st">"Treino"</span>, <span class="st">"Teste"</span>, <span class="st">"Previsão com ML"</span>])</span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>plt.show()</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-display">
<div>
<figure class="figure">
<p><img src="ml_models_files/figure-html/cell-7-output-1.png" width="1271" height="337" class="figure-img"></p>
</figure>
</div>
</div>
</div>
<p>Claramente, tivemos o problema que mencionamos anteriormente.</p>
<section id="solução-1-diferenciação" class="level3" data-number="7.2.1">
<h3 data-number="7.2.1" class="anchored" data-anchor-id="solução-1-diferenciação"><span class="header-section-number">7.2.1</span> Solução 1: Diferenciação</h3>
<p>Uma solução é usar a diferenciação para remover a tendência da série.</p>
<div id="54b3feda" class="cell" data-execution_count="7">
<div class="code-copy-outer-scaffold"><div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sktime.transformations.series.difference <span class="im">import</span> Differencer</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>regressor <span class="op">=</span> RandomForestRegressor(n_estimators<span class="op">=</span><span class="dv">100</span>, random_state<span class="op">=</span><span class="dv">42</span>)</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>model <span class="op">=</span> Differencer() <span class="op">*</span> ReductionForecaster(</span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a> regressor,</span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a> window_length<span class="op">=</span><span class="dv">30</span>,</span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a> steps_ahead<span class="op">=</span><span class="dv">1</span>,</span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a>model.fit(y_train, X<span class="op">=</span>X_train)</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<div class="cell-output cell-output-display" data-execution_count="7">
<style>#sk-7ce17bde-d7c2-4d10-971d-23b955150077 {
/* Definition of color scheme common for light and dark mode */
--sklearn-color-text: black;
--sklearn-color-line: gray;
/* Definition of color scheme for objects */
--sklearn-color-level-0: #fff5e6;
--sklearn-color-level-1: #f6e4d2;
--sklearn-color-level-2: #ffe0b3;
--sklearn-color-level-3: chocolate;
/* Specific color for light theme */
--sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, black));
--sklearn-color-background: var(--theme-background, var(--jp-layout-color0, white));
--sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, black));
--sklearn-color-icon: #696969;
@media (prefers-color-scheme: dark) {
/* Redefinition of color scheme for dark theme */
--sklearn-color-text-on-default-background: var(--theme-code-foreground, var(--jp-content-font-color1, white));
--sklearn-color-background: var(--theme-background, var(--jp-layout-color0, #111));
--sklearn-color-border-box: var(--theme-code-foreground, var(--jp-content-font-color1, white));
--sklearn-color-icon: #878787;
}
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 {
color: var(--sklearn-color-text);
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 pre {
padding: 0;
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 input.sk-hidden--visually {
border: 0;
clip: rect(1px 1px 1px 1px);
clip: rect(1px, 1px, 1px, 1px);
height: 1px;
margin: -1px;
overflow: hidden;
padding: 0;
position: absolute;
width: 1px;
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-dashed-wrapped {
border: 1px dashed var(--sklearn-color-line);
margin: 0 0.4em 0.5em 0.4em;
box-sizing: border-box;
padding-bottom: 0.4em;
background-color: var(--sklearn-color-background);
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-container {
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
but bootstrap.min.css set `[hidden] { display: none !important; }`
so we also need the `!important` here to be able to override the
default hidden behavior on the sphinx rendered scikit-learn.org.
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
display: inline-block !important;
position: relative;
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-text-repr-fallback {
display: none;
}
div.sk-parallel-item,
div.sk-serial,
div.sk-item {
/* draw centered vertical line to link estimators */
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
background-size: 2px 100%;
background-repeat: no-repeat;
background-position: center center;
}
/* Parallel-specific style estimator block */
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-parallel-item::after {
content: "";
width: 100%;
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
flex-grow: 1;
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-parallel {
display: flex;
align-items: stretch;
justify-content: center;
background-color: var(--sklearn-color-background);
position: relative;
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-parallel-item {
display: flex;
flex-direction: column;
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-parallel-item:first-child::after {
align-self: flex-end;
width: 50%;
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-parallel-item:last-child::after {
align-self: flex-start;
width: 50%;
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-parallel-item:only-child::after {
width: 0;
}
/* Serial-specific style estimator block */
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-serial {
display: flex;
flex-direction: column;
align-items: center;
background-color: var(--sklearn-color-background);
padding-right: 1em;
padding-left: 1em;
}
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
*/
/* Pipeline and ColumnTransformer style (default) */
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-toggleable {
/* Default theme specific background. It is overwritten whether we have a
specific estimator or a Pipeline/ColumnTransformer */
background-color: var(--sklearn-color-background);
}
/* Toggleable label */
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 label.sk-toggleable__label {
cursor: pointer;
display: block;
width: 100%;
margin-bottom: 0;
padding: 0.5em;
box-sizing: border-box;
text-align: center;
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 label.sk-toggleable__label-arrow:before {
/* Arrow on the left of the label */
content: "▸";
float: left;
margin-right: 0.25em;
color: var(--sklearn-color-icon);
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 label.sk-toggleable__label-arrow:hover:before {
color: var(--sklearn-color-text);
}
/* Toggleable content - dropdown */
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-toggleable__content {
max-height: 0;
max-width: 0;
overflow: hidden;
text-align: left;
background-color: var(--sklearn-color-level-0);
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 div.sk-toggleable__content pre {
margin: 0.2em;
border-radius: 0.25em;
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-level-0);
}
#sk-7ce17bde-d7c2-4d10-971d-23b955150077 input.sk-toggleable__control:checked~div.sk-toggleable__content {
/* Expand drop-down */
max-height: 200px;
max-width: 100%;
overflow: auto;