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diff --git a/_images/f7797c5eddf3448220af9cd76fdc5ec9a9db216c6a213bbf5497c88e7da0269d.png b/_images/f7797c5eddf3448220af9cd76fdc5ec9a9db216c6a213bbf5497c88e7da0269d.png
diff --git a/_images/fbf38a3378b1ab48dc617d0ca543d4198ebd4f142b765647b689fcb43372ea92.png b/_images/fbf38a3378b1ab48dc617d0ca543d4198ebd4f142b765647b689fcb43372ea92.png
diff --git a/_sources/book/topic09/assignment09_time_series.md b/_sources/book/topic09/assignment09_time_series.md
@@ -77,6 +77,7 @@ DATA_PATH = "https://raw.githubusercontent.com/Yorko/mlcourse.ai/main/data/"
 df = pd.read_csv(DATA_PATH + "wiki_machine_learning.csv", sep=" ")
 df = df[df["count"] != 0]
 df.head()
+
 ```
 
 

diff --git a/_sources/book/topic09/assignment09_time_series_solution.md b/_sources/book/topic09/assignment09_time_series_solution.md
@@ -119,7 +119,7 @@ train_df = df[:-predictions].copy()
 
 ```{code-cell} ipython3
 m = Prophet()
-m.fit(train_df);
+m.fit(train_df)
 ```
 
 

diff --git a/_sources/book/topic09/topic9_part1_time_series_python.md b/_sources/book/topic09/topic9_part1_time_series_python.md
@@ -87,7 +87,7 @@ currency = pd.read_csv(
 ```
 
 ```{code-cell} ipython3
-plt.figure(figsize=(12, 6))
+plt.figure(figsize=(12, 8))
 plt.plot(ads.Ads)
 plt.title("Ads watched (hourly data)")
 plt.grid(True)
@@ -1099,7 +1099,6 @@ def plotSARIMA(series, model, n_steps):
 
     # forecasting on n_steps forward
     forecast = model.predict(start=data.shape[0], end=data.shape[0] + n_steps)
-    forecast = data.arima_model.append(forecast)
     # calculate error, again having shifted on s+d steps from the beginning
     error = mean_absolute_percentage_error(
         data["actual"][s + d :], data["arima_model"][s + d :]
@@ -1530,7 +1529,7 @@ plotModelResults(
     X_train=X_train_scaled,
     X_test=X_test_scaled,
     plot_intervals=True,
-    plot_anomalies=True,
+    plot_anomalies=True
 )
 ```
 

diff --git a/_sources/book/topic09/topic9_part2_facebook_prophet.md b/_sources/book/topic09/topic9_part2_facebook_prophet.md
@@ -216,7 +216,7 @@ Let's sort the dataframe by time and take a look at what we've got:
 
 
 ```{code-cell} ipython3
-df.sort_values(by=["published"]).head(n=3)
+df.sort_values(by=["published"]).head(n=2)
 ```
 
 Medium's public release date was August 15, 2012. But, as you can see from the data above, there are at least several rows with much earlier publication dates. They have somehow turned up in our dataset, but they are hardly legitimate ones. We will just trim our time series to keep only those rows that fall onto the period from August 15, 2012 to June 25, 2017:

diff --git a/_static/plotly_htmls/Box–Cox_transformation.html b/_static/plotly_htmls/Box–Cox_transformation.html
diff --git a/_static/plotly_htmls/New_posts_on_Medium.html b/_static/plotly_htmls/New_posts_on_Medium.html
diff --git a/_static/plotly_htmls/No_transformations.html b/_static/plotly_htmls/No_transformations.html
diff --git a/_static/plotly_htmls/Posts_on_Medium_(daily).html b/_static/plotly_htmls/Posts_on_Medium_(daily).html
diff --git a/_static/plotly_htmls/Posts_on_Medium_(weekly).html b/_static/plotly_htmls/Posts_on_Medium_(weekly).html
diff --git a/_static/plotly_htmls/assign9_plot.html b/_static/plotly_htmls/assign9_plot.html
diff --git a/book/.DS_Store b/book/.DS_Store
diff --git a/book/topic09/assignment09_time_series.md b/book/topic09/assignment09_time_series.md
@@ -77,6 +77,7 @@ DATA_PATH = "https://raw.githubusercontent.com/Yorko/mlcourse.ai/main/data/"
 df = pd.read_csv(DATA_PATH + "wiki_machine_learning.csv", sep=" ")
 df = df[df["count"] != 0]
 df.head()
+
 ```
 
 

diff --git a/book/topic09/assignment09_time_series_solution.html b/book/topic09/assignment09_time_series_solution.html
diff --git a/book/topic09/assignment09_time_series_solution.md b/book/topic09/assignment09_time_series_solution.md
@@ -119,7 +119,7 @@ train_df = df[:-predictions].copy()
 
 ```{code-cell} ipython3
 m = Prophet()
-m.fit(train_df);
+m.fit(train_df)
 ```
 
 

diff --git a/book/topic09/topic9_part1_time_series_python.html b/book/topic09/topic9_part1_time_series_python.html
diff --git a/book/topic09/topic9_part1_time_series_python.md b/book/topic09/topic9_part1_time_series_python.md
@@ -87,7 +87,7 @@ currency = pd.read_csv(
 ```
 
 ```{code-cell} ipython3
-plt.figure(figsize=(12, 6))
+plt.figure(figsize=(12, 8))
 plt.plot(ads.Ads)
 plt.title("Ads watched (hourly data)")
 plt.grid(True)
@@ -1099,7 +1099,6 @@ def plotSARIMA(series, model, n_steps):
 
     # forecasting on n_steps forward
     forecast = model.predict(start=data.shape[0], end=data.shape[0] + n_steps)
-    forecast = data.arima_model.append(forecast)
     # calculate error, again having shifted on s+d steps from the beginning
     error = mean_absolute_percentage_error(
         data["actual"][s + d :], data["arima_model"][s + d :]
@@ -1530,7 +1529,7 @@ plotModelResults(
     X_train=X_train_scaled,
     X_test=X_test_scaled,
     plot_intervals=True,
-    plot_anomalies=True,
+    plot_anomalies=True
 )
 ```
 

diff --git a/book/topic09/topic9_part2_facebook_prophet.html b/book/topic09/topic9_part2_facebook_prophet.html
@@ -766,7 +766,7 @@ <h3><a class="toc-backref" href="#id12" role="doc-backlink">3.2 Dataset</a><a cl
 <p>Let’s sort the dataframe by time and take a look at what we’ve got:</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;published&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;published&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -804,11 +804,6 @@ <h3><a class="toc-backref" href="#id12" role="doc-backlink">3.2 Dataset</a><a cl
       <td>1970-01-01 00:00:00.001000+00:00</td>
       <td>https://medium.com/@ikaella/melon-rebranding-b...</td>
     </tr>
-    <tr>
-      <th>37395</th>
-      <td>1970-01-18 05:11:46.500000+00:00</td>
-      <td>http://www.novosti.rs/%D0%B2%D0%B5%D1%81%D1%82...</td>
-    </tr>
   </tbody>
 </table>
 </div></div></div>
@@ -1321,10 +1316,10 @@ <h3><a class="toc-backref" href="#id14" role="doc-backlink">3.4 Making a forecas
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>16:18:21 - cmdstanpy - INFO - Chain [1] start processing
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>16:55:27 - cmdstanpy - INFO - Chain [1] start processing
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>16:18:21 - cmdstanpy - INFO - Chain [1] done processing
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>16:55:27 - cmdstanpy - INFO - Chain [1] done processing
 </pre></div>
 </div>
 </div>
@@ -1429,10 +1424,10 @@ <h3><a class="toc-backref" href="#id14" role="doc-backlink">3.4 Making a forecas
       <th>904</th>
       <td>2017-06-23</td>
       <td>276.682485</td>
-      <td>255.508448</td>
-      <td>305.337762</td>
-      <td>276.267743</td>
-      <td>277.057474</td>
+      <td>254.201087</td>
+      <td>304.016830</td>
+      <td>276.267332</td>
+      <td>277.098510</td>
       <td>2.316748</td>
       <td>2.316748</td>
       <td>2.316748</td>
@@ -1451,10 +1446,10 @@ <h3><a class="toc-backref" href="#id14" role="doc-backlink">3.4 Making a forecas
       <th>905</th>
       <td>2017-06-24</td>
       <td>277.388317</td>
-      <td>217.021728</td>
-      <td>269.092764</td>
-      <td>276.939924</td>
-      <td>277.794113</td>
+      <td>218.637265</td>
+      <td>267.275922</td>
+      <td>276.948748</td>
+      <td>277.834694</td>
       <td>-34.992533</td>
       <td>-34.992533</td>
       <td>-34.992533</td>
@@ -1473,10 +1468,10 @@ <h3><a class="toc-backref" href="#id14" role="doc-backlink">3.4 Making a forecas
       <th>906</th>
       <td>2017-06-25</td>
       <td>278.094149</td>
-      <td>221.166044</td>
-      <td>271.685007</td>
-      <td>277.611047</td>
-      <td>278.543160</td>
+      <td>222.470485</td>
+      <td>272.170684</td>
+      <td>277.624320</td>
+      <td>278.567768</td>
       <td>-31.355580</td>
       <td>-31.355580</td>
       <td>-31.355580</td>
@@ -1505,7 +1500,7 @@ <h3><a class="toc-backref" href="#id14" role="doc-backlink">3.4 Making a forecas
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/74099c2a0f73e222fdae2229fb78ab58a40e1808d8fc1d483af6e798e5545fcc.png" src="../../_images/74099c2a0f73e222fdae2229fb78ab58a40e1808d8fc1d483af6e798e5545fcc.png" />
+<img alt="../../_images/eed11a86ee6c8c5f919dd459341bcbd8441930b310c3c7f57354bb3ca87c425f.png" src="../../_images/eed11a86ee6c8c5f919dd459341bcbd8441930b310c3c7f57354bb3ca87c425f.png" />
 </div>
 </div>
 <p>This chart doesn’t look very informative. The only definitive conclusion that we can draw here is that the model treated many of the data points as outliers.</p>
@@ -1518,7 +1513,7 @@ <h3><a class="toc-backref" href="#id14" role="doc-backlink">3.4 Making a forecas
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/df5cccb8c6a20b18ecb21d4482c2fb521dd000f64d384e65b5cb27f7d8e82b7a.png" src="../../_images/df5cccb8c6a20b18ecb21d4482c2fb521dd000f64d384e65b5cb27f7d8e82b7a.png" />
+<img alt="../../_images/a86af3d5e44dd070a80826fd921fb8fa299ff15fba87989baafe5ccb7e454697.png" src="../../_images/a86af3d5e44dd070a80826fd921fb8fa299ff15fba87989baafe5ccb7e454697.png" />
 </div>
 </div>
 <p>As you can see from the trend graph, Prophet did a good job by fitting the accelerated growth of new posts at the end of 2016. The graph of weekly seasonality leads to the conclusion that usually there are less new posts on Saturdays and Sundays than on the other days of the week. In the yearly seasonality graph there is a prominent dip on Christmas Day.</p>
@@ -1598,22 +1593,22 @@ <h3><a class="toc-backref" href="#id15" role="doc-backlink">3.5 Forecast quality
     <tr>
       <th>2017-06-23</th>
       <td>278.999233</td>
-      <td>255.508448</td>
-      <td>305.337762</td>
+      <td>254.201087</td>
+      <td>304.016830</td>
       <td>421</td>
     </tr>
     <tr>
       <th>2017-06-24</th>
       <td>242.395784</td>
-      <td>217.021728</td>
-      <td>269.092764</td>
+      <td>218.637265</td>
+      <td>267.275922</td>
       <td>277</td>
     </tr>
     <tr>
       <th>2017-06-25</th>
       <td>246.738569</td>
-      <td>221.166044</td>
-      <td>271.685007</td>
+      <td>222.470485</td>
+      <td>272.170684</td>
       <td>253</td>
     </tr>
   </tbody>
@@ -1803,10 +1798,10 @@ <h2><a class="toc-backref" href="#id17" role="doc-backlink">4. Box-Cox Transform
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>16:18:22 - cmdstanpy - INFO - Chain [1] start processing
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>16:55:27 - cmdstanpy - INFO - Chain [1] start processing
 </pre></div>
 </div>
-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>16:18:22 - cmdstanpy - INFO - Chain [1] done processing
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>16:55:27 - cmdstanpy - INFO - Chain [1] done processing
 </pre></div>
 </div>
 </div>

diff --git a/book/topic09/topic9_part2_facebook_prophet.md b/book/topic09/topic9_part2_facebook_prophet.md
@@ -216,7 +216,7 @@ Let's sort the dataframe by time and take a look at what we've got:
 
 
 ```{code-cell} ipython3
-df.sort_values(by=["published"]).head(n=3)
+df.sort_values(by=["published"]).head(n=2)
 ```
 
 Medium's public release date was August 15, 2012. But, as you can see from the data above, there are at least several rows with much earlier publication dates. They have somehow turned up in our dataset, but they are hardly legitimate ones. We will just trim our time series to keep only those rows that fall onto the period from August 15, 2012 to June 25, 2017:
-Original file line number
+Diff line change
@@ Expand Up @@
     df = pd.read_csv(DATA_PATH + "wiki_machine_learning.csv", sep=" ")
     df = df[df["count"] != 0]
     df.head()
     ```
@@ Expand Down @@