feat: new article (#315)

* feat: new article

* feat: new article

* feat: new article

* feat: new article

* feat: new article

Author: DiogoRibeiro7

.github/workflows/merge-schedule.yml

@@ -8,7 +8,7 @@ on:
       - synchronize
   schedule:
     # https://crontab.guru/every-hour
-    - cron: '55 2 * * 6'
+    - cron: '55 2 * * *'
   # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch: 
 

_posts/-_ideas/2030-01-01-data_model_drift.md

@@ -13,9 +13,6 @@ tags: []
 
 ## Article Ideas on Data Drift and Model Drift
 
-### 6. **Data Drift vs. Concept Drift: Understanding the Differences and Implications**
-   - **Overview**: Differentiate between **data drift** (changes in the input data distribution) and **concept drift** (changes in the underlying relationships between inputs and outputs).
-   - **Focus**: Provide real-world examples to illustrate how each type of drift affects model performance and decision-making.
 
 ### 7. **Using Unsupervised Learning for Early Detection of Data Drift**
    - **Overview**: Explore how unsupervised learning techniques like **clustering** and **autoencoders** can detect anomalies in data that signal data drift.

_posts/-_ideas/2030-01-01-elderly_care.md

@@ -11,9 +11,6 @@ seo_type: article
 tags: []
 ---
 
-### 3. Improving Elderly Mental Health with Machine Learning and Data Analytics
-   - **Overview**: Discuss the role of data analytics and machine learning in understanding and treating mental health conditions like depression, anxiety, and dementia in the elderly.
-   - **Focus**: Use cases of AI-powered mood tracking and early detection of cognitive decline based on behavioral and health data.
 
 ### 4. Big Data in Geriatric Medicine: Enhancing Care for Aging Populations
    - **Overview**: Explain how big data analytics is being used to improve geriatric care by analyzing trends in elderly health, treatment outcomes, and care patterns.

_posts/-_ideas/2030-01-01-future_articles_time_series.md

@@ -24,15 +24,6 @@ Here are several article ideas that would complement the ARIMAX time series mode
    - Discuss the advantages and limitations of each approach.
    - Provide examples and code implementation in Python. -->
 
-### 5. **"Multivariate Time Series Forecasting: VAR and VECM Models Explained"**
-   - Dive into the Vector AutoRegressive (VAR) model and Vector Error Correction Model (VECM) for multivariate time series data.
-   - Discuss how these models handle interdependencies between multiple time series.
-   - Provide examples of applications in economics, finance, and weather forecasting.
-
-### 6. **"Handling Non-Stationarity in Time Series Data: Techniques and Best Practices"**
-   - Discuss why stationarity is crucial for time series forecasting models like ARIMA and ARIMAX.
-   - Explain techniques to make a time series stationary (differencing, transformations, detrending).
-   - Introduce tests like ADF and KPSS, with practical examples in R or Python.
 
 ### 7. **"Prophet: A Modern Approach to Time Series Forecasting Developed by Facebook"**
    - Introduce the Prophet model developed by Facebook, which is designed to handle seasonality and holidays with ease.