Unlocking the Future: How to Predict Weather with LSTM
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- Опубликовано: 22 июл 2024
- LSTM, which stands for Long Short-Term Memory, is a type of recurrent neural network (RNN) architecture designed for handling sequences of data, making it particularly well-suited for time series analysis.
Weather Data: www.kaggle.com/code/hritik708...
Notebook: github.com/iamtekson/deep-lea...
Timestamps:
0:00 Intro
0:22 Intro to LSTM model
1:45 About dataset
3:31 Workflow and result of this video
4:45 Data preprocessing
20:44 Model training and temperature prediction
33:29 Outro
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Finally understood multivariate Time series in LSTM. Thanks. Very nice and informative video 👏
Glad it was helpful!
the only video that makes me understand this concept; thanks for sharing :)
You're very welcome!
Thanks! Very good video 💯
Glad it was helpful!
Welldone Tek 👏
Thank you 🙌
Thank you for sharing this good piece of materials with us dai
You are welcome vai❤️
Can I use the R2 value to measure the accuracy of the LSTM model in time series prediction?
Nice Explanation about the multivariable input LSTM. I have an enquiry if given multiple numbers rows including target variable (or every feature has multiple values) at a particular time (t1). Then how to handle these cases in sequences and labels
Can anyone say, where is best weight function
Can you share any code/video for multivariate and multistep forecast using LSTM?
In your code temperature is in both dataset( input and output), but I have a doubt , how we get temperature as input while future prediction?
love thiss tysm!!
So glad!
In the MLP network, data from independent variables from date t are used to predict a future value t+n. In the LSTM network, instead of using only data from time t of the independent variables, it uses data from time t, t-1, t-2, ..., t-n as desired by the programmer, and after that, generates the prediction for a future time t+n? Is this reasoning correct? Thank you very much!
Yes, that's correct.
Hello teacher! Do you have in mind to record a video teaching how to make forecast about drought using Google Earth Engine?
I have problem when creating sequence showing keyError. How to slove it?
Without the full error log, I can't say anything. Also, from which line are you getting the error?
Hello...
Can I have private chat with you on LSTM and CNN... Am comparing the two in predicting Cassava yield
It looks like the prediction is worse than a simple model that says "the temperature tomorrow will be the same as the temperature today". In both cases, when there is a sudden temperature change, there is a one day lag between the actual and predicted temp. In other words, it is not really much of a prediction! Or have I missed something?
you can predict further into the future than one day
can you provide the best model weight?
by the very helpful video❤
Hi, I am sorry, I already deleted the best model weight. But definitely you can train this model in Google Colab since it doesn't take much time to train the model.
Quá hay
ao you have 9 features included temperature. and the target feature is temperature ?
Yes that's correct. The idea of lstm weather prediction is, based on the historical weather pattern, we can predict the current or future weather
@@geodev Where is the code that shows the target value of the temperature variable?, I tried to change the actual values from true_temp to true fog, the graph results are still the same. Thank you
Could u tell me what is the best Library for deep learning to learn
Tensorflow and pytorch are the most popular libraries in Python.
@@geodev could u plz make a video about ML project from extract values in arc pro till end
Could you send the code
You can get the code in the video description!
Dai aba ML and Data Science for Atmospheric Remote Sensing ...ma mentoring garnu peryo :D
It is on my list vai, stay tuned! For now, I am creating content related to data preparation and image segmentation!
I am very grateful for this comprehensive explanation. I have some questions. Could you please get your email?
You can get my email in my channel description.