Time Series Forecasting with Machine Learning

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INVESTING

TIMESTAMPS
0:00 Introduction
1:51 Defining Problem
2:50 Understanding the Data
3:18 Analyzing Data (Trend, Seasonality)
4:40 Traditional Timeseries Forecasting (ARIMA, Prophet)
6:01 Univariate & Multivariate Time series
8:15 Time series with Machine Learning
9:02 Types of Time series models
11:05 Machine Learning Vs. Traditional Time Series

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Комментарии

I just want to say how much I love that it’s my grandma that has a laptop repair shop. 😍

chinmayeejoshi

This is such a great video, I'm in the middle of writing my master thesis where I use timeseries and it helps a lot to understand the concepts.

krzysztof

Thank you for this awesome video! I'm pretty new to ML and time series and this is so helpful and clear. I'm actually working on an assessment for a Data Analyst role that I'm interviewing for and I'm tasked with forecasting travel bookings. Glad I came across your video and excited to check out your other ones!

myal

I was looking for an introduction to time series forecasting for a personal project (gas price prediction, since gas prices here in Germany are kinda high), and this was the perfect primer for time-series forecasting. Not too dumbed down, and not too complicated. And, obviously a GREAT example.

vincentguttmann

Great explanation. The big challenge of some time series is changing characteristic over time, which makes the prediction impossible. Like some changes appears and disappears randomly, no seasonal features. Plus sudden changes. What grandma could do if all of sudden thing changes, and this happens frequently. All of them makes the problem interesting.

ErturkKadir

One note: you can actually use the machine learning model (the non-traditional model as you call it in the video) to dynamically predict whatever number of future points you want, you just have to implement recursion manually. Train the model to predict one step ahead, then use that prediction to predict 2nd step and so forth. This will very likely become "hard to get right" similarly to what you said about traditional models, as it's a much more complicated problem, but it is doable.

frytka

very well explined.. the issue was also looked at the business front as well.. were as the traditional IT gig would explain in the point unlike this..
it shows the understing of the business is important to adapt to these new tecnologies..

Sanjeevsachin

Don't stop making videos. You have a great teaching video.

jasonsykes

Thank you so much for your nice video. -- From Bangladesh

shakirullah

I loved this video. Such great information easily explained. Thankyou

Shoaibkhan-ojoe

Thanks for the awesome comparison! Very insightful!

joshuabradshaw

Amazing timing! I am just getting started with time series forecasting. Do you have any tools/sources to follow?

Thanks man! Love your videos!

valdrich

This video is great! I loved how you have put down the different methods so clearly and their pros and cons.

Cheers to more videos!! :)

sauravdas

How to build ARIMA models in Python without dates? If I'm estimating a target boats sinusoidal position in the ocean, do I wanna map milliseconds as dates 🤔, nah

tactusxii

Hi, great video! I'm just getting into time series forecasting, and you teached me a lot, thank you :)
Could you make a video about Graph Attention Networks for time series forecasting?

huzifighter

Thank you for the useful explanations!

sayantanghosh

Please correct if wrong: Traditional time series models are not necessarily recursive: MA, IMA models are not regressive

tony

Really good. Any chance of saying in the future how LTSMs and GRUs compare with the aforementioned models for time series.

oxfordsculler

What if you want to output a vector? i.e. you want the model to output laptops you will receive on each day from 1-10 days into the future (output vector of size 1x10). Great video!

chuckcheddar

I worked hard on forex dataseries:
EURUSD tick resolution, compressed with wavelets, passed into LSTM under keras.
Got 73% accuracy on the next minute: not bad for experimental results?
What gives me headaches:
- do I always need to make the timeseries *stationary*?
- How to scale perfectly my timeseries, according to what model im going to use (lstm, mlp, sklearn regressor...)?
- Do I have to use stateless or stateful lstm???
- Does it have soem sense to shuffle sequenses before training lstm?
I could not find clear answer anywhere on the net...

sgrouge

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