End to End Multivariate Time Series Modeling using LSTM

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#datascience #deeplearning #LSTM

In this video we will see how we can build a multi variate time series model using Deep learning LSTM sequence model. We will see end to end time series model building process in this video

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The clearest explanation of all multivariate time series LSTM modelling videos. Thank you so much!!!

lukmannurhafizramli

Finally I find such a great tutorial help me figure out inputs of lstm, thank you so much Sir!

xiaofangchen

Sir, really appreciating your quality of content. It helps a lot.
Could you please make detailed content on ARCH and GARCH modelling for volatility in time series.

bhaveshsalvi

Thank you for this - very helpful. My only challenge is that with this examples and others I am working on, I have better performance when I set win_length to 1 (i.e. length for the TimeSeriesGenerator object). I would think a value of something like 720 would help to capture more recent activity and improve the model but it doesn't.

suemetzger

This is so helpful for me as it explained each step very well. I have only one question which is how can we forecast our prediction to the future? Supposedly our data goes from 1st jan 2009 to 31 dec 2022 and we want to forecast on jan 2023 . And We don't know also the values of the features.

mohamedabouelkhir

Thank you so much for this amazing video! I found it super useful.

ruhithomas

Thank you so much for explaining so smoothly. please provide GitHub code of it

PrityKumari-rqgp

Sir, I am getting ValueError at stage 'history = model.fit_generator(train_generator, epochs=50, validation_data=test_generator, shuffle=False, callbacks=[early_stopping])'

ValueError: Failed to find data adapter that can handle input: <class 'keras.preprocessing.sequence.TimeseriesGenerator'>, <class 'NoneType'>

Sir, how can I handle this error?

ipvikas

You shouldnt use test_generator as validation set because your test data leaks into the training, which is why your model performed as well as it did on the test data set

GS-rmfv

Sir, u have just tested the data.. What about the future forecast? We have not done future forecast here

mdfaiz

amazing video.. while i believe the tansform should have happened after the train_test_split just on X_train and X_test. otherwise awesome!

vigneshpadmanabhan

Great tutorial! Can I ask why the number of cells in the LSTM layer is not the same as the number of steps of the input window?

mohamednedal

Hello, Thanks for the content you are providing and it helps me to solve my real world problems as well during my office work...I am looking for the multivariate TS tutorial but everywhere am finding ppl are treating this somewhere as regression problem. Even in this vedio u did the prediction not the forecasting because while we do forecasting for future we won't be having input features values....so basically I don't understand how should we use multivariate time series in real world problem...like I want to forecast the CPU usage and it depends on number of users, which process is running, day of the week and understand we can use day of the week and date but how will I get the details of users and process for future....there is one solution I can think like we can have user and process details for future from different TS models and the we can forecast CPU usage using these values but I don't think this is the good solution as this will increase error....pl help me to understand.

vishey

Please can someone help me after following all the steps and i get to the history part when i run my code i get this error, ValueError Failed to find data adapter that can handle input: <class 'keras_preprocessing.sequence.TimeseriesGenerator'>, <class 'NoneType'> how doi solve this

immadmalik

Thanks for the great video. The coding is user friendly. So we could think this as a supervised regression problem. But in terms of forecasting future, what actions should we take? What if we wanted to forecast (predict) the next 10 days?

erensarnic

Great video! Could you create a video on LSTM multivariate anomaly detection?

mp

in LSTM model architecture you have used 1 dense layer to depicts one period forecast, if i want to return 3 periods values then should i have 3 dense layers?

moulich

I understand this is a supervised time-series problem and uses x_train predictors to train the model by comparing it to y_train. However, when we get to the x_test predictors we accept those as known. But in a problem like wind power forecasting, the predictor - wind speed may be unknown. So would that make the problem unsupervised? And what shape of train/test data we should use in this case to feed the LSTM?

pravdomirdobrev

please share some videos on multivariate multistep LSTM along with all the error metrics(MAE, RMSE, MAPE, r2 score).

sathipatra

If there is no trend followed in data then what I have to do?
which one I have to use? whether time series works good for multi-regression or not?

ramyasrigorle

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