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David Andrés 🤖📈🐍

Dec 7 • 6 tweets • 4 min read

What is the main difference between ARCH and GARCH models? 🤔

Find it out here!
🧵 👇

#python #machinelearning #timeseries #datascience

🔎 GARCH stands for "Generalized Autoregressive Conditional Heteroskedasticity".

It is a type of time-series model that is commonly used in finance to model the volatility of asset returns.

It is a generalisation of the ARCH model.

But why a generalisation? 👇 🤔

It extends the capabilities of ARCH models. ⚡

It allows for the inclusion of additional variables and terms in the model.

This can improve its accuracy and ability to capture the dynamics of the time series. 🤩

Let's see how 👇

ARCH models only consider previous observations of the time series.

While GARCH models consider both previous observations and previous volatility values.

@daansan_ml

I'll talk more about this in my upcoming article!

Please 🔁Retweet the first tweet if you found it helpful!

🔔 Follow me @daansan_ml if you are interested in:

🐍 #Python
📊 #DataScience
📈 #TimeSeries
🤖 #MachineLearning

Thanks! 😉

https://twitter.com/daansan_ml/status/1600460182110146560?s=20&t=CI3qSvF0ZL7zvs4r_VrIPw

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David Andrés 🤖📈🐍

@daansan_ml

Dec 6

5️⃣ YouTube playlists / videos to learn Time Series! ▶️

Check them out!

🧵 👇👇

#Python #MachineLearning #DataScience

1️⃣ "Time Series Analysis" by ritvikmath

My personal top 1 recommendation for learning Time Series.

A great combination of theory and code 👌

youtube.com/playlist?list=…

2️⃣ "Time Series" by Aric LaBarr

👍 Good variety of models. Short videos.

👎 Purely theoretical, no code.

youtube.com/playlist?list=…

Read 7 tweets

David Andrés 🤖📈🐍

@daansan_ml

Dec 5

Machine Learning and Deep Learning are key skills for a Data Scientist! 🔑 But also for Time Series! 📈

TOP 5️⃣ COURSES to learn about it 👇

👇👇👇👇👇
#python #datascience #ai

1️⃣ Start by learning the basics of Machine Learning with this fantastic course.

• Learn both supervised and unsupervised algorithms
• Get introduced to Neural Networks
• Find out about XGBoost

coursera.org/specialization…

2️⃣ Learn about Deep Learning:

• Neural Networks
• Convolutional Neural Networks (CNN)
• Sequence models (very useful for Time Series!)

coursera.org/specialization…

Read 7 tweets

David Andrés 🤖📈🐍

@daansan_ml

Dec 4

What is the difference between seasonality and cycle in Time Series? 🤔

🧵 👇

#Python #DataScience #MachineLearning

🔴Seasonality refers to regular patterns that occur at a specific frequency, often at a yearly, monthly or weekly interval.

For example:

• Retail sales tend to increase during the holiday season

• Electricity consumption tends to be higher in the summer months when people use air conditioning more often

Read 9 tweets

David Andrés 🤖📈🐍

@daansan_ml

Nov 29

Answer these 8️⃣ questions before starting any Time Series project!

👇👇👇👇

#DataScience #MachineLearning #Python

1️⃣ What are your inputs and outputs to forecast?

📥 Inputs are the historical data you provide to the model

📤 Outputs are the predictions or forecasts for a future timestep

2️⃣ What are your endogenous or exogenous input variables?

• Endogenous: are influenced by other variables within the system

• Exogenous: are not and can be considered outside the system

E.g., endogenous could be the number of daily purchases and exogenous the bank holidays.

Read 10 tweets

David Andrés 🤖📈🐍

@daansan_ml

Nov 28

Build your ARCH model to predict volatility! 🔮

🧵 👇

#TimeSeries #MachineLearning #Python #DataScience

First, you need to import the required libraries.

Now it is time to download the stock data (S&P500) and format it appropriately.

We need to set the frequency to Business days and the index as Datetime.

Read 9 tweets

David Andrés 🤖📈🐍

@daansan_ml

Nov 27

The wait is over! 🎉

Before moving on to code ARCH models...👨‍💻

I will share the notebook in #Python for ARIMA models! 📓

🚨 Check the end of the thread, there's a present! 🎁

#TimeSeries #DataScience #MachineLearning

First, the steps covered:

1️⃣ Import data (in this case Google stock price) 📚

2️⃣ Format data 🔨

3️⃣ Visualise prices and returns 🔍

4️⃣ Estimate parameters p, d and q 🔬

5️⃣ Build the initial model 🛠️

6️⃣ Find the optimal model 🌟

7️⃣ Forecast! 🔮

Read 5 tweets

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