Hello friends! Curious about how to properly predict stock prices?
I’ve now released the third video in this YouTube mini-series.
For those unfamiliar: this is a video series that debunks common mistakes found in nearly all blog articles / Github repos claiming to do “stock price predictions with LSTMs”.
These are typically written by non-experts in the field just looking for clicks, and I have a lot of fun breaking down precisely what they’re doing wrong.
Why is this important?
Beginners are often fooled by such content, wasting money on courses to learn things that don’t work. Even worse, they may end up putting such examples on their own Github accounts or in their portfolios / resumes, worsening their chances of getting a job in the field.
To be clear: the bad part isn’t that they learned something that doesn’t work (although that is pretty bad by itself). The bad part is, they don’t even understand why it doesn’t work. They are confident that it does and will fight me over it! (That is, until I ask them to verify or rebut any of the claims I’ve made).
Thus, not only does this stuff not work (due to all the mistakes I outline in my video series), but it could actually be detrimental to getting a job and working in this area. These are not just harmless mistakes!
The first video discussed why min-max scaling over the train set doesn’t work.
The second video discussed why using prices as inputs (i.e. lagged prices to build an autoregressive model) doesn’t work.
This third video (this video) will discuss why using prices as targets does not work.
Ever come across a machine learning / data science blog demonstrating how to predict stock prices using an autoregressive model, with past stock prices as input?
It’s been awhile, but I am finally continuing this YouTube mini-series I started awhile back, which goes over common mistakes in popular blogs on predicting stock prices with machine learning. This is the 2nd installment.
It is about why you shouldn’t use prices as inputs.
Time series analysis is becoming an increasingly important analytical tool.
With inflation on the rise, many are turning to the stock market and cryptocurrencies in order to ensure their savings do not lose their value.
COVID-19 has shown us how forecasting is an essential tool for driving public health decisions.
Businesses are becoming increasingly efficient, forecasting inventory and operational needs ahead of time.
Let me cut to the chase. This is not your average Time Series Analysis course. This course covers modern developments such as deep learning, time series classification (which can drive user insights from smartphone data, or read your thoughts from electrical activity in the brain), and more.
We will cover techniques such as:
ETS and Exponential Smoothing
Holt’s Linear Trend Model
Holt-Winters Model
ARIMA, SARIMA, SARIMAX, and Auto ARIMA
ACF and PACF
Vector Autoregression and Moving Average Models (VAR, VMA, VARMA)
Machine Learning Models (including Logistic Regression, Support Vector Machines, and Random Forests)
Deep Learning Models (Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks)
GRUs and LSTMs for Time Series Forecasting
We will cover applications such as:
Time series forecasting of sales data
Time series forecasting of stock prices and stock returns
Time series classification of smartphone data to predict user behavior
The VIP version of the course (obtained by purchasing the course NOW during the VIP period) will cover even more exciting topics, such as:
As always, please note that the VIP period may not last forever, and if / when the course becomes “non-VIP”, the VIP contents will be removed. If you purchased the VIP version, you will retain permanent access to the VIP content via my website, simply by letting me know via email you’d like access (you only need to email if I announce the VIP period is ending).
Small note:
I wanted to get this course into your hands early. Some sections are still in the editing stages, particularly:
Convolutional Neural Networks (done, but more to be added later)
Recurrent Neural Networks (done, but more to be added later)
Vector Autoregression
(VIP) GARCH
(VIP) FB Prophet
+MORE VIP CONTENT (it’s a surprise!)
UPDATE: The crossed-out items have since been added. There is no timeline for the remaining “surprise” lectures – it’ll be a surprise! 😉
So what are you waiting for? Get the VIP version of Time Series Analysis NOW:
