Multivariate Time Series Forecasting Using LSTM, GRU & 1d CNNs

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I just wanted to let you know that I went through many LSTM tutorials over the Internet and yours is the one that got me understanding it all. Thank you! And let me give you a compliment you probably didn't get yet: the way you talk is very easy to understand for a non-native english speaker like me!

felipegiacomel

Greg thanks for sticking with this even to the point of how tired you were getting at the end of it. You are a gifted lecturer.

If I were to add anything it would be more about the need for and the technique of pre-processing the data.

AlanDeikman

After scourging through the entire Youtube, I finally landed on a great video that helps me get the idea of how to implement RNNs. I am trying to tackle a problem in my side project that involves RNNs and this video is exactly what I needed. Thanks for the lovely explanation, Greg!

dakshbhatnagar

Greg you really explain so nice. Finally I landed up to a solution to problems in my project. Thank you so much for providing this knowledge for free. God bless you, your family and your YouTube channel. Lots of love from India.

SanjeevSingh-kthp

Thank you so much! This video is by far the best one to show how to fit multivariate LSTM.

TheWalkingAliveAnd

Best dl time series tutorial by far.. well done Greg!

ifeanyi_okpala

Thank you Greg for this awesome video. To think that i have watched this video severally and i just got my "A ha moment". I am currently working on a project on Forex prediction using LSTM and this will help me alot. Thank you again.

folashadeolaitan

Thank you so much i was very confused about the multivariate ( multiple features ) to use as input. You cleared my doubts. Thank you

kashishrajput

I learn a lot just from this video, thanks mentioning all the details as they are always the confusing parts!

peymanhj

Great video Greg! could you please share some ideas on how to adapt the models to forecast several values (hours) ahead, not only just one? thank you.

jaimeperalta

Please continue the series, amazing explanation

ashraf_isb

Fantastic video, Greg! I found your video to be highly educational. However, I would like to seek clarification on a particular point. It appears that you're utilizing the present temperature as an input in order to predict the current temperature. Is my understanding accurate? If this is indeed the case, I wonder if such an approach might be considered less sophisticated. Perhaps the model's performance could be enhanced if the input values were temporally shifted and not directly included as features.

Additionally, it seems to me that you might be utilizing the prior prediction outcome as an input to forecast subsequent outcomes. This is just my interpretation, and I'm open to correction if I've misunderstood your methodology.

pratikbhadane

Very simple yet comprehensive. Thank you!

MohammadYs

This is the best video I’ve seen on this topic - well done. One question: why didn’t you also standardize your validation data in the example of temp and day/year sun/cos?

whiptips

This was awesome. I got through both the previous video on LSTM time series and this one using data from Hugging Face. Thanks for the great content

jenniferamhajduk

Just coming back to reference some stuff. great vid!
btw if you're dealing with large datasets your df_to_X_y2 function might be a bit slow. This should speed it up significantly and give the same result.

"""
def split_data(data, n_steps):
x, y = list(), list()
for i in range(len(data)):
end_ix = i + n_steps
if end_ix > len(data)-1:
break
seq_x, seq_y = data[i:end_ix, :-1], data[end_ix-1, -1]
x.append(seq_x)
y.append(seq_y)
return np.array(x), np.array(y)
"""

takes in a numpy array as data. if you're dealing with a dataframe just input df.values, n_steps is the window size. this is more for a classification task as it uses the last column of the input as the target (y=target) and thus wont include the target in the output x array. can tweak as you'd need ofc.

oliver

Thank you so much for your this tutorial! God bless you! Regards!

Bobolicx

That is awesome video! Thanks for sharing this wonderful material.

rafasimionato

Great video! It relly helped me understand how to use LSTM and CNN models for time series forecasting.

I just have one question: How would you go about to forecast several time steps? Let say your forecast is on hourly resolution, and you want to forecast the temperature for the next 24 hours (24 values).
Would you:
1) Keep the same model with 1 ouput and iterate using the predicted values for hour 1 to forecast hour 2 and so on... or would you...
2) Change the model output to be 24 values?
And if you think option 2) is better, how youconstruct the feature data? I am guessing the input for the output t1 that is just one hour away shouldn't be the same as for t24?

maansjacobsson

This is cool, but I'm not really that impressed. While the agreement seems impressive at first glance when looking at the comparison plot, what we are doing here is plotting our estimate of the *next* temperature based on a recent historical window of data. That means that at the next time step, our model is getting the values from the previous step. In other words, the model is always making a prediction only one time-step in the future. A more helpful evaluation of its performance would be to plot and compare the difference between the last and current temperature data between the actual and estimation. Another way to understand why I'm skeptical is to consider doing a control experiment using a much simpler estimate of the temperature: a linear interpolation of the last two temperatures. If we plotted the results the same way, I suspect my linear interpolation method would also look extremely good. Really, the valuable kinds of questions aren't just whether the estimate at the next time is close to the actual value, but rather if the change in temperature from the previous time-step to the current time-step is predicted accurately -- does this difference have the same sign between the prediction and actual values? Does it have a similar magnitude?

danielwalsh

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