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Thanks for updating the post, this level of fluctuation in the validation set is a lot less dramatic than before and appears to be similar to regular fluctuation I have seen in my experience. Kudos that you have also managed to prevent the model from overfitting.


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I've been fiddling a bit with LSTMs myself to predict windspeeds using inertial drone data and some of my plots had a similar "offset" to yours. Have you scaled your inputs using a MinMax or Standard scalar? I've also have a surprisingly good amount of success implementing a KNN algorithm to predict the windspeeds with mean bias errors oftentimes ...


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I think that if you append a token <EOS> (end of sentence) at the end of each sentence when you merge, this would not be a problem, because the RNN would learn to cut sentences and to generate independently if you shuffle your data and train with several shuffles. However, as you say your data is heterogeneous, you might consider to first run some ...


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Welcome to the site. I think you were right that the prediction lags behind the true value because the series is autoregressive (i.e. a strong way to predict tomorrow’s value is “It will be about the same as today”). Your model therefore corrects itself with the new information when it misses a big jump. In other words, if the price jumps one day and your ...


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Welcome to Data Science on Stack Exchange, This is a common question, predicting future prices or forecasting. The gap you see is due to the random nature of prices such as this, along with the underlying complexity of this topic. Unless there is a time pattern in the data, a LSTM model won't predict well. LSTM will especially perform poorly if the data is ...


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As far as I am aware you also need to save and load the tokenizer you used. The tokenizer is not fitted/trained and therefore is outputting nothing sensible for the model to predict on.


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The short answer is no, a bidirectional architecture will still take in a variable sequence length. To understand why, you should understand how padding works. For example, let's say you are implementing a bidirectional LSTM-RNN in tensorflow on variable length time series data for multiple subjects. The input is a 3D array with shape: [n_subjects, [...


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Time series Analysis refers to the type of problems where we have to analyse an outcome based on time dependent inputs. Time series data is basically a sequence of data, hence time series problems are often referred to as sequence problems. Recurrent Neural Networks (RNN) have been proven to efficiently solve sequence problems. Particularly, Long Short Term ...


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