Update app.py

app.py

@@ -295,7 +295,7 @@ if selected == "DTW Pattern Recognition":
     st.markdown("""
     ### DTW Pattern Recognition
 
-    Dynamic Time Warping (DTW) is a method that measures the similarity between two time series that may vary in time or speed. DTW aligns the time series by warping the time axis to minimize the distance between them. This method identifies historical periods that have similar patterns to the current stock price pattern by comparing their shapes, regardless of possible distortions in the time axis.
+    Dynamic Time Warping (DTW) is a method that measures the similarity between two time series that may vary in time or speed. DTW aligns the time series by warping the time axis to minimize the distance between them. This method could potentially identify historical periods that have similar patterns to the current stock price pattern by comparing their shapes, regardless of possible distortions in the time axis.
 
     The DTW distance \( D \) between two time series \( X \) and \( Y \) is calculated as:
 
@@ -312,8 +312,8 @@ if selected == "DTW Pattern Recognition":
 
     **How to use:**
     1. Enter the stock ticker, start date, and end date.
-    2. Select the number of subsequent days to forecast.
-    3. Select the number of days to compare.
+    2. Select the number of subsequent days to observe (forecast). This parameter determines how many days into the future you want to observe after identifying a similar historical pattern. For example, if you set it to 20, the app will show the stock price movements for 20 days following the identified pattern.
+    3. Select the number of days to compare and find patterns. Choose one or more time periods (in days) to compare against the current pattern. For instance, you might select 15, 20, and 40 days. The app will look for historical patterns of these lengths that match the current stock price pattern.
     4. Click the 'Run' button.
 
     **Results:**
@@ -344,9 +344,9 @@ elif selected == "Correlation Pattern Recognition":
 
     **How to use:**
     1. Enter the stock ticker, start date, and end date.
-    2. Select the number of subsequent days to forecast.
-    3. Select the number of days to compare.
-    4. Select the number of days prior to the similar series.
+    2. Select the number of subsequent days to observe (forecast). This parameter determines how many days into the future you want to observe after identifying a similar historical pattern. For example, if you set it to 20, the app will show the stock price movements for 20 days following the identified pattern.
+    3. Select the number of days to compare. 
+    4. Select the number of days prior to the similar series. This parameter is used to set the number of days before the start of the similar pattern for additional context.
     5. Click the 'Run' button.
 
     **Results:**
@@ -378,8 +378,8 @@ elif selected == "TA-Enhanced DTW Pattern Recognition":
 
     **How to use:**
     1. Enter the stock ticker, start date, and end date.
-    2. Select the number of subsequent days to forecast.
-    3. Select the number of days to compare.
+    2. Select the number of subsequent days to observe (forecast). This parameter determines how many days into the future you want to observe after identifying a similar historical pattern. For example, if you set it to 20, the app will show the stock price movements for 20 days following the identified pattern.
+    3. Select the number of days to compare. Choose one or more time periods (in days) to compare against the current pattern. For instance, you might select 15, 20, and 40 days. The app will look for historical patterns of these lengths that match the current stock price pattern.
     4. Click the 'Run' button.
 
     **Results:**