## Time Series Prediction with LSTMs using TensorFlow 2 and Keras in Python

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📔 Complete tutorial + notebook: https://www.curiousily.com/posts/demand-prediction-with-lstms-using-tensorflow-2-and-keras-in-python/
📖 Read Hacker’s Guide to Machine Learning with Python: http://bit.ly/Hackers-Guide-to-Machine-Learning-with-Python

Learn how to predict demand using Multivariate Time Series Data. Build a Bidirectional LSTM Neural Network in Keras and TensorFlow 2 and use it to make predictions.

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### Comment List

• Venelin Valkov
December 8, 2020

Why did you use 128 units for your lstm? Thanks for the video!

• Venelin Valkov
December 8, 2020

Thanks mate very nice tutorial. Just a small advice please increase the font size next time.

• Venelin Valkov
December 8, 2020

Time Series Prediction uses only time. But you are using multi variate model. Am I wrong?

• Venelin Valkov
December 8, 2020

Nice video, Thanks

• Venelin Valkov
December 8, 2020

Сташионарити, епта)

• Venelin Valkov
December 8, 2020

Hi, where are the concepts of stationarity used that you introduced in the beginning? Also, what if there were no trends or signal in the data, can you just model it with LSTM?

• Venelin Valkov
December 8, 2020

Thanks for sharing. I was looking for a bidirectional layer example.

• Venelin Valkov
December 8, 2020

Very clear and straightforward, I wish all tutorials were like that

• Venelin Valkov
December 8, 2020

At 37:01 you pass train as your X. And as train already consists of count that makes the whole model and training meaningless as the model will only use the count variable from the X to predict the y with near 100% accuracy.

• Venelin Valkov
December 8, 2020

Why was there no activation func here,iam new so can anyone plz explain?

• Venelin Valkov
December 8, 2020

Самое ценное, что узнал – это как задать biderection in lstm ))

• Venelin Valkov
December 8, 2020

Can you make one with stock prices?

• Venelin Valkov
December 8, 2020

can i use the same model for wind speed forecasting, with the data given in the same file?

• Venelin Valkov
December 8, 2020

At 17:39 you were trying to change your data with df.iloc[-200:] but you left your x values as df.index, which has a different length.

• Venelin Valkov
December 8, 2020

Здрасти, пробвал ли си LSTM за предсказване на ETF или акции? Интересно ми е ако да, каква акуратност ти се е получила.
Аз онзи ден пробвах с друг подход да предиктна цената на среброто за следващ ден, обаче като резултат върху тестинг данните ми излезе 50% точност, което си е чиста ези тура :).

• Venelin Valkov
December 8, 2020

Is it possible to create a Real Time Time Series Analysis? (Where the Time Series is updated in Real Time?)

• Venelin Valkov
December 8, 2020

Thanks a lot!!! You Rock!!
And I pretty much like your accent :'D

• Venelin Valkov
December 8, 2020

Hi Venelin, I have a question. During test period, we won't be having the true value. So we can't create sequences. Isn't it correct to append the forecast to the sequences as we predict one value at a time?

• Venelin Valkov
December 8, 2020

it is, really, perfect!

• Venelin Valkov
December 8, 2020

Why all the tutorials say predict, but do it on already known results? It's more like to build a model that as close to KNOWN results as possible. Prediction is different from forecasting I guess.

• Venelin Valkov
December 8, 2020

i was facing probelms in creating time series dataset for lstm ,thank yopu for making it clear .

• Venelin Valkov
December 8, 2020

Another Question (@47:00): Is there any reason, why you did not use 'validation_data=(X_test, y_test)' but rather 'validation_split'?
Ah, I now realized, that you use the test data for prediction later on.

• Venelin Valkov
December 8, 2020

Hey Venelin, thank you very much for your videos! While reconstructing and "copying" your code, I am wondering (@31:00) why you fit the RobustScaler() only on the training data and user that fitted RobustScaler afterwards for the testing data. Shouldn't we fit a new RobustScaler to use with the testing dataset?

• Venelin Valkov
December 8, 2020

In your trainset, you had 13 features, shouldn't they be only 5? cnt t1 t2 hum and wind_speed?

• Venelin Valkov
December 8, 2020

Why didn't you use train_test_split to separate the data?

• Venelin Valkov
December 8, 2020

• Venelin Valkov
December 8, 2020

Thanks, a lot… If we have same type of data, and we are given 'Bike ID' as one of the feature and we have say 10 bikes, so for each unique bike ID there will be data from 2015 till 2017. In that case what should be our approach and how we should aggregate or work on the data?? Please help me..

• Venelin Valkov
December 8, 2020

Great stuff, thanks!

• Venelin Valkov
December 8, 2020

Really cool tutorial! Thanks!

• Venelin Valkov
December 8, 2020

Hi Venelin! Great video! I am working on a project, just for fun because i want

to get better at deep learning, about predicting sales prices on auctions

based on a number of features over time and also the state of the economy,

probably represented by the stock market or GDP. So its a Time Series prediction project.

And i want to use transfer learning, finding a good pretrained model i can use.

Do you have any idea about a model i can use?

• Venelin Valkov
December 8, 2020

T-series prediction

• Venelin Valkov
December 8, 2020

Why do you supply the whole "train" dataset as first argument of "create dataset" function? Should you not be removing the train.cnt from this dataframe first? What am I missing?

• Venelin Valkov
December 8, 2020

This tutorial is excellent.Thank you! However, I think, there's an inconspicuous mistake in the code. If I understand correctly, the arrays X_train and X_test contain not only the features but also the labels? Thus the Y-values (count) to be predicted are already included in the X-data?

• Venelin Valkov
December 8, 2020

how to plotting predict axis x datetime

• Venelin Valkov
December 8, 2020

Hi Venelin, why you have subtracted timesteps from length of training rows? please let me know, I am not able to convince myself 🙂