When GME goes up, the market goes down. Read on!

**Disclaimer**: This is a short article and does not intent to provide financial advice or to suggest anything whatsoever.

Recently there is a lot of noise around GME, reddit and the stock market.

My **hypothesis** was that there is a significant correlation between **GME** and **S&P 500 **time courses of price.

I did a simple correlation analysis and I found that there is a significant (p=0.05) negative correlation (rho= -0.319) between the GME and S&P500 price.

Just for a reminder this is what happened over the past couple of months:

Regression models are used to predict a numerical value (dependent variable) given a set of input variables (independent variables). The most famous model of the family is the linear regression [2].

Linear regression fits a line (or hyperplane) that best describes the linear relationship between some inputs (X) and the target numeric value (y).

However, if the data contains outlier values, the line can become biased, resulting in worse predictive performance. **Robust regression** refers to a family of algorithms that are robust in the presence of outliers [2].

**Principal Components Analysis** (PCA) is a well-known **unsupervised** **dimensionality** **reduction** technique that constructs **relevant** features/variables through linear (linear PCA) or non-linear (kernel PCA) **combinations** of the original variables (features). In this post, we will only focus on the famous and widely used **linear PCA** method.

The construction of relevant features is achieved by **linearly transforming correlated variables** into a smaller number of **uncorrelated** variables. This is done by **projecting** (dot product) the original data into the **reduced PCA space** using the eigenvectors of the covariance/correlation matrix aka the principal components (PCs).

The **resulting** **projected** **data** are essentially **linear** **combinations** of…

Traditionally most machine learning (ML) models use some observations (samples/examples), but there is no **time** **dimension** in the data.

**Time-series forecasting** models are the models that are capable of **predicting** **future values** based on **previously** **observed** **values**. Time-series forecasting is widely used for **non-stationary data**. **Non-stationary data **are called the data whose statistical properties, e.g., the mean and standard deviation, are not constant over time but instead, these metrics vary over time.

These non-stationary input data (used as input to these models) are usually called **time-series. **Some time-series examples include the temperature values over time, stock price over time, price…

Traditionally most machine learning (ML) models use as input features some observations (samples/examples), but there is no **time** **dimension** in the data.

**Time-series forecasting** models are the models that are capable of **predicting** **future values** based on **previously** **observed** **values**. Time-series forecasting is widely used for **non-stationary data**. **Non-stationary data **are called the data whose statistical properties, e.g., the mean and standard deviation, are not constant over time but instead, these metrics vary over time.

These non-stationary input data (used as input to these models) are usually called **time-series. **Some examples of time-series include the temperature values over time, stock…

Traditionally most machine learning (ML) models use as input features some observations (samples/examples), but there is no **time** **dimension** in the data.

**Time-series forecasting** models are the models that are capable of **predicting** **future values** based on **previously** **observed** **values**. Time-series forecasting is widely used for **non-stationary data**. **Non-stationary data **are called the data whose statistical properties, e.g., the mean and standard deviation, are not constant over time but instead, these metrics vary over time.

These non-stationary input data (used as input to these models) are usually called **time-series. **Some examples of time-series include the temperature values over time, stock…

Regression models are used to predict a numerical value (dependent variable) given a set of input variables (independent variables). The most famous model of the family is the linear regression [2].

Linear regression fits a line (or hyperplane) that best describes the linear relationship between some inputs (X) and the target numeric value (y).

However, if the data contains outlier values, the line can become biased, resulting in worse predictive performance. **Robust regression** refers to a family of algorithms that are robust in the presence of outliers [2].

**Introduction****The Naive Bayes algorithm****Dealing with text data****Working Example in Python (step-by-step guide)****Bonus: Having fun with the model****Conclusions**

**Naive Bayes** classifiers are a collection of classification algorithms based on **Bayes’ Theorem**. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

**Naive Bayes** classifiers have been heavily used for **text classification** and **text** **analysis** machine learning **problems**.

**Text Analysis** is a major application field for machine learning algorithms. However the raw data, a sequence of…

K-means is one of the most widely used unsupervised clustering methods.

The **K-means **algorithm clusters the data at hand by trying to separate samples into **K** groups of equal variance, minimizing a criterion known as the ** inertia** or

The k-means algorithm divides a set of **N **samples (stored in a data matrix **X**) into **K** disjoint clusters **C**, each described by the mean *μj** *of…

**Time-series forecasting** models are the models that are capable to **predict** **future values** based on **previously** **observed** **values**. Time-series forecasting is widely used for **non-stationary data**. **Non-stationary data **are called the data whose statistical properties e.g. the mean and standard deviation are not constant over time but instead, these metrics vary over time.

These non-stationary input data (used as input to these models) are usually called **time-series. **Some examples of time-series include the temperature values over time, stock price over time, price of a house over time etc. …

Diploma of Electrical & Computer Engineering (NTUA). Master of Science in Neuroscience (UNIGE). Currently, I am a PhD student at EPFL.