Share this page!

David Andrés 🤖📈🐍 Profile picture
Twitter logo
Dec 6 • 7 tweets • 5 min read
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=…
3️⃣ "Time Series Forecasting with XGBoost" by @Rob_Mulla

Highly recommended if you are looking for an ML approach. 100% practical!

There is a second part 😉

4️⃣ "LSTM Time Series Forecasting Tutorial in Python" by @GregHogg5

Great video if you want to apply Neural Networks! 100% practical!

5️⃣ "Time Series Forecasting with Facebook Prophet and Python in 20 Minutes" by @nicholasrenotte

Finally an easy and quick way of getting a time series forecast in Python with FB Prophet. Nicholas also has many other interesting videos about the topic.

@nicholasrenotte Please 🔁Retweet the first tweet if you found it useful to increase the reach!

🔔 Follow me @daansan_ml if you are interested in:

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

Thanks! 😉

• • •

Missing some Tweet in this thread? You can try to force a refresh
　

Keep Current with David Andrés 🤖📈🐍

David Andrés 🤖📈🐍 Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @daansan_ml

David Andrés 🤖📈🐍 Profile picture
Dec 7
What is the main difference between ARCH and GARCH models? 🤔

Find it out here!
🧵 👇

#python #machinelearning #timeseries #datascience Image
🔎 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 👇
Read 6 tweets
David Andrés 🤖📈🐍 Profile picture
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 🤖📈🐍 Profile picture
Dec 4
What is the difference between seasonality and cycle in Time Series? 🤔

🧵 👇

#Python #DataScience #MachineLearning Image
🔴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 🤖📈🐍 Profile picture
Nov 29
Answer these 8️⃣ questions before starting any Time Series project!

👇👇👇👇

#DataScience #MachineLearning #Python Image
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 🤖📈🐍 Profile picture
Nov 28
Build your ARCH model to predict volatility! 🔮

🧵 👇

#TimeSeries #MachineLearning #Python #DataScience Image
First, you need to import the required libraries. Image
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. Image
Read 9 tweets
David Andrés 🤖📈🐍 Profile picture
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 Image
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

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us on Twitter!

Send Email!

:(