181 - Multivariate time series forecasting using LSTM

For a dataset just search online for 'yahoo finance GE' or any other stock of your interest. Then select history and download csv for the dates you are interested.

Code generated in the video can be downloaded from here:

[DigitalSreeni]

I should apologize for misspeaking towards the end when it comes to forecasting. I said you can forecast the future 90 days when you do This is not true; you'd be predicting the past and not the future. This is a multivariate time series which means you need multiple variables as input to predict a single output. The input size shall be (n, 14, 5) where 14 was the number of days we look back and 5 was the number of variables. We only have multiple variables available until the last day in our input. So we can only predict one day into the future. In a single variate time series, you can predict multiple days into the future as you can use your predicted values as input to the model. You cannot do that here as we are not predicting multiple variables. Sorry again for making a rookie mistake. The code on github will be updated accordingly. Thanks to all who alerted me about the mistake.

[chenqu]

Figuring out the data input is indeed a crutial part of the whole training pipeline. Thank you very much for such a clear explanation and a great tutorial !

[joelteixeira]

I have been struggling with the input-shape for multivariate time series for such a long time. And finally I found a great explanation for how to prepare my data. Thanks for this.

[Wysumay]

I am french and I understand this video better than any papers or videos that are actually in my own language. Thanks for this amazing explanation !

[raphaelguay]

You explained it really well. I was stuck in the datashape things. Now it's crystal clear. Thanks

[nazaramin]

Many thanks, this is the first time I understand multivariate time series forecasting using lstm.
You cannt imagine how much I need it, appreciated..

[russwedemeyer]

Thanks for this great video. It has been extremely helpful.
At the end of the video you asked for ideas for future videos that would help people get through a difficult subject.
I have been struggling with implementing Feature Importance for Multivariate Time Series prediction using LSTM.
I believe this is very valuable for better understanding the predicting model and helping give it credibility. Please consider for a future video.
Thanks again!

[roxennh]

Good day, Sir.! I'm here to express my gratitude. This video has really saved my life. I can't express how grateful I am! I wish you the best of luck in the future. <3

[TheHarpanOnly]

I just started a project similar to this, and every single second in this video is an answer of of my questions during this project. Thank you and GBU.

[davidperry]

The video was very clear and easy to follow, certainly in comparison to your peers. I like how you zoomed in to focus on the important part that you were currently talking about.

[arhamtaseer]

This has been a huge help for a new project i am working on. I still need to optimize a lot of inputs to the model and implement feature importance methods but this basic framework has been an excellent start!

[JonCianci]

This was excellent! Great work with explaining the input shapes! Your predictions look pretty good also… and we got through Covid… 👍

[reidgwn]

Fantastic content! The part that you believed you dwelled on was actually the best.

[Absfor]

I love the "I'm done with Covid" tone... again great content! Keep it up!

[cristhianfernandez]

Remember in this example, the first column is the predictable variable.
Thank you very much for the knowledge you share, greetings from Colombia.

[CodeCult]

After searching many places, finally I got it.
Very well explained by you sir.
Please make a video on Object localization of medical x-ray of chest and brain.

[lltaha]

Your explanations are absolutely spot on! Please don't think "dwelling" on a topic is a waste; it really helps with understanding! I would have preferred if you could also explain (or trace) the somewhat head scratching logic in those for loops for trainX and trainY but after starring at it for quite some time I was able to understand. Nonetheless, 11/10!! Thank you for these videos.

[Jason-wxtv]

Thank you so much for taking the time to put this together. This has been very helpful.

[vijilakshmi]

The concept I am looking for was clearly explained in this video. Thank you so much for sharing your knowledge. even small small things you explain in your videos even though you had already explained in previous videos. thanks a lot.

[rashmi]

Thank you so much for great explanation! I have couple of questions: 1. If I have a monthly data and I would like to forecast next 5 years, then what would be the best size for n_past. Would it be 60 (5 years) or just 12 (1 year). How do we gauge this size. 2. How do I compare validation loss with training loss and what is the good range to consider the validation loss is considerable. Any help in understanding these 2 questions would be appreciated. Thanks!