Time Series Forecasting With RNN(LSTM)| Complete Python Tutorial|
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- čas přidán 29. 08. 2024
- In this video i cover time series prediction/ forecasting project using LSTM(Long short term memory) neural network in python. LSTM are a variant of RNN(recurrent neural network) and are widely used of for time series projects in forecasting and future predictions.
I cover the complete code of the project and this tutorial is intended for beginners in the field of time series forecasting.
Github Code(With data set): github.com/nac...
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You can connect with me on my socials:
Linkedin: / nachiketa-hebbar-86186...
My 2nd CZcams Channel: / @nachitalks
My medium account(I publish blogs here): / nachihebbar
Books to get better at Time Series Analysis and Python:
1)Practical Time Series Analysis: amzn.to/31lsLhq
2)Time Series with Python: amzn.to/2Ez073m
3)Hands-On Time Series Analysis with R: amzn.to/3aUxuKq
After spending hours reading documentation to understand everything... This short video was what I really needed!
Too good brother! The entire LSTM code explained line by line with the underlying concepts within 15 min! Much appreciated. You're a great teacher!
Thanks!
@@NachiketaHebbar Hai
Kindly make a video how to access GitHub programming file , alter the coding for our own dataset
@@NachiketaHebbar
What's the role of generators in time series?
Can u plz explain for LSTM model for exogenous variables
Man, you are already an scientist, keep the great work
The fact that you're making it so clear and simple 👏👏👏
Well explained. Can you please make a tutorial on Multivariate (explanatory variables) Multistep (more than 1 step ahead) time series forecasting using LSTM?
Did you find any good video for LSTM Multivariate Model?
@@SimplytheBest23 No.
I think you can write your custom training and test data generation functions for this, and then just plug it into an LSTM. Don't use the TimeSeriesGenerator provided by keras.
You are the best Brother, Thanks for saving my life. Udemy couldn't explain it better than you
Glad to help, and thanks for such a kind comment!
You have become popular in my college, here in dublin..you are saving our life's here...simple and lucid videos...thanks a ton..
Thanks, this comment made my day!
This video was so helpful. You did a very nice job explaining how the batch training of predictions works. Thank you, Nachiketa!
For the first time, I have found one that helps me follow the whole concept. Thank you.
And that time series generator was new to me. It makes the work quite simple.
Many thanks, Hebbarm!!! you really save my days with this tough one
it is the best video for LSTM on CZcams.
Thanks for the tutorial. Btw, can you provide the tutorials on multi-variate and multi-step method on time series prediction? It's also a popular and useful topics. Thanks!!!
short and to the point. thx a lot.
Multivariate time series...
This is a very well presented and articulated walkthrough. Good work.
Please do a Video on Multivariate Time Series modelling using LSTM. I like the your natural way of explanation..! keep it up!
Thanks Bhai. Got one SCI publication in Q2 based one your video❤❤❤❤❤
This video was help me lot to do my research... thanx brother... please do more content like this. you are awesome
Thanks, very good explanation
Hi,
"Cannot convert a symbolic Tensor (lstm_11/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported"
How to resolve this type of problem?????
Same problem here with:
model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))
Always love your content !!!keep making videos man
Beautifully explained!!! Thanks a lot.
i found this really simple and handy
Thanks !!!!!! i love uuuuuu for this hahaha i use this for my work :)
Haha glad to hear that
I have two questions;
1) How can we make this dataset stationary?
2) How to optimize the hyperparameter of the LTSM algorithm?I have two questions;
Thank you :)
@Nachiketa Hebbar ,
Hai
Kindly make a video how to access GitHub programming file , alter the coding for our own dataset
simple and precise bro! awesome!
All good,but my clg wants a dynamic output,hence I have to use some sensors,webcams,voice input through jupyter etc..😅😅
Yho! I am a new to RNN yet your Video was very informative. I enjoyed your approach and how simplified you made it look.
When you get a chance, Could you please do Multivariate Forecasting. Thank you.
thanks this video for make me easy to understanding and i will make reference for my thesis trial :) hehe
This is a great channel with amazing content. Can you please make a video related to the recommendation models and how to deploy them using flask?.
Again Thankyou the amazing videos.
You're welcome. okay, I will try to cover recommendation systems in the future
@@NachiketaHebbar Thanks, it will be great.
Good job Boy!!! Well explained
Very well explained. Thank you so much.!!!
How should I change the code for future predictions? If I am happy with the modell, how do I apply it to the whole dataset to truely predict values in the future?
Best tutorial EVER
Great explanation, thank you!
how do we predict another three months production using this?
Really helpful, keep making such videos
Really good video, well done, subscribed!
This is a great channel with amazing content. Suppose I have data for 100 weeks. Can you please, tell me how to forecasting the data for week 101.
Again Thank You the amazing videos.
thank you so much.this is very help full video
you can add `squared=False` paremeter in mean_squared_error function to get RMSE value instead, cmiiw
Your have explained it with great enthusiasm, really liked your video. I am following your video and notice that if n_input value are increased from 10 to let's say 30, validation loss increases enormously for daily data. Could you explain why is it so?
Hi
Appreciate the effort for explaining the model ..pretty straight forward.
Can you please tell me how to alter the code to get forecast for future 12 month's
Very good explanation, thanks
Thank you for the great video! Just one question, why do we need to scale our series (if we are using only one series)?
some models work better with numbers from 0 to 1, i think
The problem is not about having multiple features and single features in this case. Think of univariate time series as a multi-feature problem where the scale within the time series has a large range. Hence, as we do scaling for traditional models, we also scale it down for time series data. You can try without doing so, and you will see a very large loss value
Thank you so much. This is very help.
Thanks bro, it's help me
Thanks bro.
Nice tutorial on univariate LSTM .
Request you to please make multivariate LSTM time series forecasting similar to ARIMAX using multiple exogenous variables.
such predicting sale using exogenous variables like price, advertising spend, macro economics variable and events (dummy variables).
thank you for this vidéo . iI have a qst , please how should we prepare our data if we have a lot of products ( we will have redondant date )
Hello, great Tutorial! I tried to reconstruct your tutorial and ran into an error in this line:
model.add(LSTM(100, activation='relu', input_shape=(n_input, n_features)))
I get the Error:
NotImplementedError: Cannot convert a symbolic Tensor (lstm/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported
Do you have an Idea whats the problem?
Thanks in advance!
kindly update your numpy version'
So helpful ! brother thanks!
Can you please help on deploying LSTM Model?
Can you pls explain how to forecast for next few months
nice explanation nachiketa, have a small doubt how to deal if there are multiple time series involving various products?
Great explanation bro.
Great explanation man.thank you very much ❤️❤️❤️
You're welcome!
I have one doubt. [1,2,3] is used to predict [4]. Then [2,3,4] is used to predict [5]. In 2,3,4 shouldn't the 4 value be the actual instead of predicted? Why are we appending predicted value. Pls explain.
Thank you!
Bravo 😊
Nice yarr 👍👍
Very helpful. I applied the model on my data, but I have weak result. I need to contact with you If you don't mind.
Well explained, highly impressed by ur explanation... keep up the good work.. I have a request, please can u make a tutorial on ARIMA-LSTM Hybrid model or ARIMA-GRU!!
Thanks in advance!
Hi Nachiketa, thanks for this gem of a video first of all :) Really appreciate
Can you guide me on how can we use grid search to tune hyperparameters like optimizer, #epochs etc.
Thank you so much
Can you recommend some references (videos or articles) on model that receive multiple input and also spit out (predict) multiple output? Like predict unit sales, how many customers, and such things.
n_input = 3 How do I decide the value?
Thank you 😀
can you make another video for multi feature time series forecasting?i couldnt figure out what to do for that
Thanks bro, this is a great and easy way of description ; if it is possible, would you mind to prepare multivariate LSTM based time series model? With much respect🙏🙏🙏
Sure, will try to make a video on it
If possible please create video for multivariate time series forecasting(without LSTM) with Graph Neural Networks
@@NachiketaHebbar did you manage to do the multivariate LSTM? :) Great explanations
but still you didn't bro🤥
thanks, well explained 👏
Thanks for making such learning video. Can you make one more video ON LSTM which predict future data
Nope, harder than most tech fields.
if you could have explained why you have taken as 100 neurons as input..i mean any logic behind of 100 only....please reply it.
Hi , i'm getting an error when i try to change the frequency to Day, the Alias im trying to use is "D" instead of "MS" but i'm getting an error and i'm still getting an error.
its monthly data so he explicitly defined it as MS . Its not daywise data so it wont convert to days for u
Well Explained. My question is
1. What i want to mention instead of parse_date = True and df.index.freq = ' ' .if my Index column is YYYY-DD-MM Hr:Min:Sec format.
2. Is possible to consider epoch time stamp as index_col. if Yes what modification can i do to perform.
I think you should use standard scaler in order to fit better
Thanks for the video. So let's say that i have 120 days in my training set and 20 days in my test set. What should be the n_input in this case? Thank you!
Great work
Wonderful Bro!
thank u so much......
Great Work Bro
My Data has hourly records for dates. It doesn't have all the hours. I can't view the Seasonal_Decompose because the freq can't be set.
Hi i'm interested in deep learning . I fond this vidéo interesting but i've a l some confusions on predicting the wind speed using LSTM. Thé windowgenerator is a bit confusion on defining the parameters
at line model.predict(last_train_batch) my output is array([[nan]],dtype=float32) i dont know whats wrong in program
What does basically mean of trend , seasonal and residual . How all of them is diffrent though?
Thanks a lots Bro! But How to compute an accuracy measure based on RMSE? foreexample on your case RMSR is 26.04. so what is the accuracy of the model in %?? please help me ! please ! I am comfused!
Here is the answer:
import numpy as np
import pandas as pd
from sklearn.metrics import mean_absolute_percentage_erro
# Assuming you have the true test values in a 'TrueValues
# test['TrueValues'] = true_values
# Calculate the MAPE (Mean Absolute Percentage Error) bet
mape = mean_absolute_percentage_error(test['Production'],
# Convert MAPE to percentage format (0-100)
percentage_accuracy = (1 - mape) * 100
# Display the percentage accuracy
print(f"Percentage Accuracy: {percentage_accuracy:.2f}%")
Love it!
Thank you
Thank you so much, just have one question why are you using the relu activation function and not the sigmoid or the tanh?
can i have request can you do video for forecasting inflation rate with RNN(LSTM) i like all your videos i can easily learn
Nice video man, now I do have a question. How do you perform a forecast out of sample for the next... let's say 12 periods ahead?
Really liked your video. I have a small doubt on the prediction: is it an in-sample forecast or out-of-sample forecast?
Man ...u r awesome... understood each and every part ..🤩🤩
Can we also scale -ve numbers by the same method?
All -ve numbers will be mapped to 0 by Minmax scale by default. If you want to keep negative values, you can mention feature range in Minmax scale as (-1 to 1).
Thank you. How to print Accuracy like MSE
How to decide the number of neurons in the input layer like you have taken 100
Nice job
amazing video!
Hi. I have a doubt. I exactly followed the same code but my predictions are straight pls could you help as where I had gone wrong.?
One quick question, I saw you remove the seasonality but you still used the original df in the model training. So can I understand that in this video you jut used the original dataset to train the RNN without removing the seasonality? TAHNKS!!
Hey
I'm currently working on data which contain 19 values how i can make a code to forecast next 10 years values