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@@ -758,7 +758,8 @@ <h2 id="4-sarima-model-implementation">4. SARIMA Model Implementation</h2>
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</span></span><span style="display:flex;"><span>plt<span style="color:#f92672">.</span>legend()
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</span></span><span style="display:flex;"><span>plt<span style="color:#f92672">.</span>show()
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</span></span></code></pre></div><p><img alt="png" src="/posts/finance/stock_prediction/sarima/images/sarima_example_9_1.png"></p>
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<p>As the previous plot shows, SARIMA model takes into account stagionality with respect to the <a href="%28/posts/finance/stock_prediction/arima/%29">ARIMA model</a> and provides better accuracy and <em>mape</em> score.</p>
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<p>As the previous plot shows, SARIMA model takes into account stagionality with respect to the <a href="%28/posts/finance/stock_prediction/arima/%29">ARIMA model</a> and provides better accuracy and <em>mape</em> score (5% vs. 7%). Moreover, the stagionality can deal with temporary detachment from the mean value.
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Nonetheless, SARIMA model is slower to train and to interfer (respectively $\approx 100%$ and $50%$ more). It still does not involve exogenous variables (possible with SARIMAX) and potential outbreaks (like Covid-19 or holidays).</p>
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<h2 id="5-model-diagnostics">5. Model Diagnostics</h2>
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<p>After fitting the model, it&rsquo;s important to check its adequacy:</p>
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<div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span><span style="color:#75715e"># Model diagnostics</span>

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