finished article

Author: steven-ja

content/posts/finance/stock_prediction/ARIMA/index.md

@@ -218,31 +218,6 @@ plt.show()
 
 ACF looks very similar to PCF for smaller lags. Hence, even in this case a value of 8 can be used as *q*.
 
-### Grid Search
-
-Here's a Python function to perform a grid search:
-
-```python
-def grid_search_arima(ts, p_range, d_range, q_range):
-    best_aic = float('inf')
-    best_order = None
-    for p in p_range:
-        for d in d_range:
-            for q in q_range:
-                try:
-                    model = ARIMA(ts, order=(p, d, q))
-                    results = model.fit()
-                    if results.aic < best_aic:
-                        best_aic = results.aic
-                        best_order = (p, d, q)
-                except:
-                    continue
-    print(f'Best ARIMA order: {best_order}')
-    return best_order
-
-best_order = grid_search_arima(ts_diff, range(3), range(2), range(3))
-```
-
 ## 6. ARIMA model fitting
 
 ### Predict ARIMA model on all data

public/index.json

@@ -447,7 +447,7 @@
         <div class="author-profile ms-auto align-self-lg-center">
           <img class="rounded-circle" src='/images/author/profile_hu8a567cefac8c1a165d433ac0796ac418_3088978_120x120_fit_q75_box.jpg' alt="Author Image">
           <h5 class="author-name">Stefano Giannini</h5>
-          <p class="text-muted">Friday, June 28, 2024 | 9 minutes</p>
+          <p class="text-muted">Friday, June 28, 2024 | 8 minutes</p>
         </div>
 
 
@@ -648,27 +648,7 @@ <h3 id="finding-q-parameter-from-plots">Finding q parameter from plots</h3>
 </span></span><span style="display:flex;"><span>plt<span style="color:#f92672">.</span>show()
 </span></span></code></pre></div><p><img alt="png" src="/posts/finance/stock_prediction/arima/images/find_q.png"></p>
 <p>ACF looks very similar to PCF for smaller lags. Hence, even in this case a value of 8 can be used as <em>q</em>.</p>
-<h3 id="grid-search">Grid Search</h3>
-<p>Here&rsquo;s a Python function to perform a grid search:</p>
-<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:#66d9ef">def</span> <span style="color:#a6e22e">grid_search_arima</span>(ts, p_range, d_range, q_range):
-</span></span><span style="display:flex;"><span>    best_aic <span style="color:#f92672">=</span> float(<span style="color:#e6db74">&#39;inf&#39;</span>)
-</span></span><span style="display:flex;"><span>    best_order <span style="color:#f92672">=</span> <span style="color:#66d9ef">None</span>
-</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">for</span> p <span style="color:#f92672">in</span> p_range:
-</span></span><span style="display:flex;"><span>        <span style="color:#66d9ef">for</span> d <span style="color:#f92672">in</span> d_range:
-</span></span><span style="display:flex;"><span>            <span style="color:#66d9ef">for</span> q <span style="color:#f92672">in</span> q_range:
-</span></span><span style="display:flex;"><span>                <span style="color:#66d9ef">try</span>:
-</span></span><span style="display:flex;"><span>                    model <span style="color:#f92672">=</span> ARIMA(ts, order<span style="color:#f92672">=</span>(p, d, q))
-</span></span><span style="display:flex;"><span>                    results <span style="color:#f92672">=</span> model<span style="color:#f92672">.</span>fit()
-</span></span><span style="display:flex;"><span>                    <span style="color:#66d9ef">if</span> results<span style="color:#f92672">.</span>aic <span style="color:#f92672">&lt;</span> best_aic:
-</span></span><span style="display:flex;"><span>                        best_aic <span style="color:#f92672">=</span> results<span style="color:#f92672">.</span>aic
-</span></span><span style="display:flex;"><span>                        best_order <span style="color:#f92672">=</span> (p, d, q)
-</span></span><span style="display:flex;"><span>                <span style="color:#66d9ef">except</span>:
-</span></span><span style="display:flex;"><span>                    <span style="color:#66d9ef">continue</span>
-</span></span><span style="display:flex;"><span>    print(<span style="color:#e6db74">f</span><span style="color:#e6db74">&#39;Best ARIMA order: </span><span style="color:#e6db74">{</span>best_order<span style="color:#e6db74">}</span><span style="color:#e6db74">&#39;</span>)
-</span></span><span style="display:flex;"><span>    <span style="color:#66d9ef">return</span> best_order
-</span></span><span style="display:flex;"><span>
-</span></span><span style="display:flex;"><span>best_order <span style="color:#f92672">=</span> grid_search_arima(ts_diff, range(<span style="color:#ae81ff">3</span>), range(<span style="color:#ae81ff">2</span>), range(<span style="color:#ae81ff">3</span>))
-</span></span></code></pre></div><h2 id="6-arima-model-fitting">6. ARIMA model fitting</h2>
+<h2 id="6-arima-model-fitting">6. ARIMA model fitting</h2>
 <h3 id="predict-arima-model-on-all-data">Predict ARIMA model on all data</h3>
 <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>model <span style="color:#f92672">=</span> ARIMA(df<span style="color:#f92672">.</span>Close, order<span style="color:#f92672">=</span>(<span style="color:#ae81ff">8</span>,<span style="color:#ae81ff">2</span>,<span style="color:#ae81ff">8</span>)) <span style="color:#75715e"># p,d,q</span>
 </span></span><span style="display:flex;"><span>results <span style="color:#f92672">=</span> model<span style="color:#f92672">.</span>fit()
@@ -744,7 +724,7 @@ <h3 id="train-test-split">Train/ Test split</h3>
 <p>&lsquo;corr&rsquo;: 0.4484875181364141,</p>
 <p>&lsquo;minmax&rsquo;: 0.07810488835602647}</p>
 </blockquote>
-<h3 id="grid-search-1">Grid Search</h3>
+<h3 id="grid-search">Grid Search</h3>
 <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:#66d9ef">def</span> <span style="color:#a6e22e">grid_search_arima</span>(train, test, p_range, d_range, q_range):
 </span></span><span style="display:flex;"><span>    best_aic <span style="color:#f92672">=</span> float(<span style="color:#e6db74">&#39;inf&#39;</span>)
 </span></span><span style="display:flex;"><span>    best_mape <span style="color:#f92672">=</span> float(<span style="color:#e6db74">&#39;inf&#39;</span>)
@@ -784,6 +764,7 @@ <h3 id="grid-search-1">Grid Search</h3>
 <p>temp best: (3, 2, 4) 0.07647187068962996</p>
 <p>Best ARIMA order based on grid search: (3, 2, 4)</p>
 </blockquote>
+<p>In g</p>
 <h2 id="7-limitations-and-considerations">7. Limitations and Considerations</h2>
 <p>While ARIMA models can be powerful for time series prediction, they have limitations:</p>
 <ol>
@@ -982,14 +963,13 @@ <h5 class="text-center ps-3">Table of Contents</h5>
         <li><a href="#finding-d-parameter-from-plots">Finding d parameter from plots</a></li>
         <li><a href="#finding-p-parameter-from-plots">Finding p parameter from plots</a></li>
         <li><a href="#finding-q-parameter-from-plots">Finding q parameter from plots</a></li>
-        <li><a href="#grid-search">Grid Search</a></li>
       </ul>
     </li>
     <li><a href="#6-arima-model-fitting">6. ARIMA model fitting</a>
       <ul>
         <li><a href="#predict-arima-model-on-all-data">Predict ARIMA model on all data</a></li>
         <li><a href="#train-test-split">Train/ Test split</a></li>
-        <li><a href="#grid-search-1">Grid Search</a></li>
+        <li><a href="#grid-search">Grid Search</a></li>
       </ul>
     </li>
     <li><a href="#7-limitations-and-considerations">7. Limitations and Considerations</a></li>

public/posts/finance/stock_prediction/arima/index.html

@@ -460,7 +460,7 @@ <h5 class="card-title">Time Series Analysis and ARIMA Models for Stock Price Pre
     <div class="card-footer">
       <span class="float-start">
           Friday, June 28, 2024
-           | 9 minutes </span>
+           | 8 minutes </span>
       <a
       href="/posts/finance/stock_prediction/arima/"
       class="float-end btn btn-outline-info btn-sm">Read</a>