Stacked lstm architecture

By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. I want to use LSTM network for predicting in time series.

I have a small dictionary variation 8 values but lot of their combinations. How do I know how many memory block assemblies, how many memory cells each, input gates, forget gates, output gates and so on? Unfortunately there are no solid guidelines for RNNs that would work every time. Try different combinations and see what works the best after a couple epochs and then train it more.

stacked lstm architecture

Most often it depends on the pattern so it is mostly impossible to get the right network right away. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Asked 2 years, 11 months ago. Active 2 years, 10 months ago. Viewed times. I am totally newbie, never used LSTM. I am in the very first phase - only research so far.

But I don't get what you are predicting, what do you mean with "I have a small dictionary variation"? Active Oldest Votes. Sign up or log in Sign up using Google.

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Time series forecasting with deep stacked unidirectional and bidirectional LSTMs

Variable 0. If you have a too small GPU, it crashes. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Implementing batch normalisation: this is used out of the residual layers. Do not create extra variables for each time step. Mean and variance normalisation simply crunched over all axes. Learnable extra rescaling. Backward pass is as simple as surrounding the cell with a double inversion:.

Simply concatenating cells' outputs at each timesteps on the innermost. Adding K new residual bidir connections to this first layer:. Exchange dim 1 and dim 0. Stacking LSTM cells, at least one is stacked:. If the config permits it, we stack more lstm cells:. Final fully-connected activation logits. Define parameters for model. Let's get serious and build the neural network.This post assumes the reader has a basic understanding of how LSTMs work.

However, you can get a brief introduction to LSTMs here. Also, if you are an absolute beginner to time series forecasting, I recommend you to check out this Blog. The main objective of this post is to showcase how deep stacked unidirectional and bidirectional LSTMs can be applied to time series data as a SeqSeq based encoder-decoder model. It basically has two parts, the encoder that outputs a context vector encoding of the input sequence, which is then passed to the decoder to decode and predict the targets.

Let us start with the basic encoder-decoder architecture and then we can progressively add new features and layers to it to build more complex architectures.

With unidirectional LSTMs as encoder. This encoding is a vector consisting of the hidden and cell states of all the encoder LSTM cells. The encoding is then passed to the LSTM decoder as initial states along with other decoder inputs to produce our predictions decoder outputs. During model training, we set the target output sequence as the decoder outputs for the model to train against.

Bidirectional LSTMs have two recurrent components, a forward recurrent component and a backward recurrent component. The forward component computes the hidden and cell states similar to a standard unidirectional LSTM whereas the backward component computes them by taking the input sequence in a reverse-chronological order i.

The intuition of using a backward component is that we are creating a way where the network sees future data and learns its weights accordingly. BLSTM is also a go-to starter algorithm for most of the NLP tasks due to its ability to capture dependencies in the input sequence quite well. So, in order to get an encoding, the hidden and cell states of the forward component have to be concatenated with those of the backward component respectively.

With stacked unidirectional LSTMs as encoder.

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When the layers are stacked together, the outputs cell states of the first layer LSTM cells of both encoder and the decoder are passed to the second layer LSTM cells as inputs. It seems that deep LSTM architectures with several hidden layers can learn complex patterns effectively and can progressively build up higher levels of representations of the input sequence data.

Bidirectional LSTMs can also be stacked in a similar fashion. The outputs of the forward and backward components of the first layer are passed to the forward and backward components of the second layer respectively. Note: reshape is called just to convert the univariate 1D array into 2D and need not be called if the data is already 2D. Generate input-output sequence pairs. Note that we are feeding zeros as the decoder inputs and Teacher Forcing where the output of one decoder cell is fed as input to the next decoder cell could also be used not covered here.

All the models were trained only for epochs with the same parameters and bidirectional LSTMs stood apart in learning the complex patterns in the data quite well as compared to unidirectional LSTMs.

The models described can, therefore, be applied to many other time series forecasting scenarios even for multivariate input cases wherein you can pass data with multiple features as a 3D tensor. You can find the Jupyter Notebook implementation of this example in my GitHub repository. I hope you liked this article and has given you a good understanding on using deep stacked LSTMs for time series forecasting. Feedback or suggestions for improvement will be highly appreciated.Last Updated on August 5, This is surprising as neural networks are known to be able to learn complex non-linear relationships and the LSTM is perhaps the most successful type of recurrent neural network that is capable of directly supporting multivariate sequence prediction problems.

A recent study performed at Uber AI Labs demonstrates how both the automatic feature learning capabilities of LSTMs and their ability to handle input sequences can be harnessed in an end-to-end model that can be used for drive demand forecasting for rare events like public holidays. In this post, you will discover an approach to developing a scalable end-to-end LSTM model for time series forecasting.

Discover how to build models for multivariate and multi-step time series forecasting with LSTMs and more in my new bookwith 25 step-by-step tutorials and full source code. The goal of the work was to develop an end-to-end forecast model for multi-step time series forecasting that can handle multivariate inputs e. The intent of the model was to forecast driver demand at Uber for ride sharing, specifically to forecast demand on challenging days such as holidays where the uncertainty for classical models was high.

Generally, this type of demand forecasting for holidays belongs to an area of study called extreme event prediction. Extreme event prediction has become a popular topic for estimating peak electricity demand, traffic jam severity and surge pricing for ride sharing and other applications.

stacked lstm architecture

In fact there is a branch of statistics known as extreme value theory EVT that deals directly with this challenge. Further, a model was required that could generalize across locales, specifically across data collected for each city. This means a model trained on some or all cities with data available and used to make forecasts across some or all cities.

We can summarize this as the general need for a model that supports multivariate inputs, makes multi-step forecasts, and generalizes across multiple sites, in this case cities. The model was fit in a propitiatory Uber dataset comprised of five years of anonymized ride sharing data across top cities in the US.

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A five year daily history of completed trips across top US cities in terms of population was used to provide forecasts across all major US holidays. The input to each forecast consisted of both the information about each ride, as well as weather, city, and holiday variables. To circumvent the lack of data we use additional features including weather information e.

A training dataset was created by splitting the historical data into sliding windows of input and output variables. The specific size of the look-back and forecast horizon used in the experiments were not specified in the paper. Time series data was scaled by normalizing observations per batch of samples and each input series was de-trended, but not deseasonalized. Neural networks are sensitive to unscaled data, therefore we normalize every minibatch. Furthermore, we found that de-trending the data, as opposed to de-seasoning, produces better results.

Our initial LSTM implementation did not show superior performance relative to the state of the art approach. Thus, we propose a new architecture, that leverages an autoencoder for feature extraction, achieving superior performance compared to our baseline. When making a forecast, time series data is first provided to the autoencoders, which is compressed to multiple feature vectors that are averaged and concatenated.

The feature vectors are then provided as input to the forecast model in order to make a prediction. The final vector is then concatenated with the new input and fed to LSTM forecaster for prediction. It is not clear what exactly is provided to the autoencoder when making a prediction, although we may guess that it is a multivariate time series for the city being forecasted with observations prior to the interval being forecasted.

A multivariate time series as input to the autoencoder will result in multiple encoded vectors one for each series that could be concatenated. It is not clear what role averaging may take at this point, although we may guess that it is an averaging of multiple models performing the autoencoding process.

The authors comment that it would be possible to make the autoencoder a part of the forecast model, and that this was evaluated, but the separate model resulted in better performance.Last Updated on August 14, Stacking LSTM hidden layers makes the model deeper, more accurately earning the description as a deep learning technique. It is the depth of neural networks that is generally attributed to the success of the approach on a wide range of challenging prediction problems.

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Each layer processes some part of the task we wish to solve, and passes it on to the next. In this sense, the DNN can be seen as a processing pipeline, in which each layer solves a part of the task before passing it on to the next, until finally the last layer provides the output.

Additional hidden layers can be added to a Multilayer Perceptron neural network to make it deeper. The additional hidden layers are understood to recombine the learned representation from prior layers and create new representations at high levels of abstraction.

For example, from lines to shapes to objects.

stacked lstm architecture

A sufficiently large single hidden layer Multilayer Perceptron can be used to approximate most functions. Increasing the depth of the network provides an alternate solution that requires fewer neurons and trains faster. Ultimately, adding depth it is a type of representational optimization.

Deep learning is built around a hypothesis that a deep, hierarchical model can be exponentially more efficient at representing some functions than a shallow one. Given that LSTMs operate on sequence data, it means that the addition of layers adds levels of abstraction of input observations over time.

In effect, chunking observations over time or representing the problem at different time scales. This approach potentially allows the hidden state at each level to operate at different timescale.

RNNs are inherently deep in time, since their hidden state is a function of all previous hidden states. The question that inspired this paper was whether RNNs could also benefit from depth in space; that is from stacking multiple recurrent hidden layers on top of each other, just as feedforward layers are stacked in conventional deep networks.

In the same work, they found that the depth of the network was more important than the number of memory cells in a given layer to model skill. Stacked LSTMs are now a stable technique for challenging sequence prediction problems. Specifically, one output per input time step, rather than one output time step for all input time steps. When an LSTM processes one input sequence of time steps, each memory cell will output a single value for the whole sequence as a 2D array.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am new to deep learning and currently working on using LSTMs for language modeling. I was looking at the pytorch documentation and was confused by it.

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I think the network architecture will look exactly like above. Am I wrong? And if yes, what is the difference between these two?

Here, the input is feed into the lowest layer of LSTM and then the output of the lowest layer is forwarded to the next layer and so on so forth. The reason people sometimes use the above approach is that if you create a stacked LSTM using the first two approaches, you can't get the hidden states of each individual layer. For example:.

In the end, outputs will contain all the hidden states of each individual LSTM layer. Learn more. Asked 2 years, 1 month ago. Active 2 years, 1 month ago. Viewed 7k times. If I create a nn. If I do something like nn.

stacked lstm architecture

Wasi Ahmad Active Oldest Votes. Your understanding is correct. The following two definitions of stacked LSTM are same. Wasi Ahmad Wasi Ahmad Thanks for the clarification. Do you know what could be the advantages and disadvantages of each approach? I can answer it then. Just wondered if you could clarify the term outputs?Machine learning and deep learning have found their place in financial institution for their power in predicting time series data with high degrees of accuracy.

There is a lot of research going on to improve models so that they can predict data will higher degree of accuracy. This post is a write up about my project AIAlphawhich is a stacked neural network architecture that predicts the stock prices of various companies.

This project is also one of the finalists at iNTUtiona hackathon for undergraduates here in Singapore. The workflow for this project is essentially in these steps:. In this post, I will go through the specifics of each step and why I choose to make certain decisions. Due to the complexity of the stock market dynamics, stock price data is often filled with noise that might distract the machine learning from learning the trend and structure.

Hence, it is in our interest to remove some of the noise, while preserving the trends and structure in the data. At first, I wanted to use the Fourier Transform those unfamiliar should read this articlebut I thought Wavelet Transforms may be a better choice to preserve the time factor of the data, instead of producing a merely frequency based output.

The wavelet transform is very closely related to the Fourier Transform, just that the function used to transform is different and the way this transformation occurs is also slightly varied. The process is as follows: the data is transformed using Wavelet transform, then the remove coefficients that more than a full standard deviation away out of all the coefficientsand inverse transform the new coefficients to get the denoised data.

Here is an example of how wavelet transform denoises time series data:. As you can see, the random noise that is present in the initial signal is absent in the denoised versions. This is exactly what we are looking to do with our stock price data.

Stacked Neural Networks for Prediction

Here is the code for how to denoise data:. The library pywt is excellent for wavelet transforms are has lessened my load tremendously. In an usual machine learning context, extracting features will require expert domain knowledge. This is a luxury that I do not have. I could perhaps try using some form of technical indicators such as moving average or moving average convergence divergence MACDor momentum measures, but I felt that using it blindly might not be optimal.

However, automated feature extraction can be achieved by using stacked autoencoders or other machine learning algorithms like restricted Boltzmann machines. I have chosen to use stacked autoencoders due to the interpretability of the encoding as compared to the probabilities from the restricted Boltzmann machines.

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