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In forecasting spatially-determined phenomena (the weather, say, or the next frame in a movie), we want to model temporal evolution, ideally using recurrence relations. At the same time, we’d like to efficiently extract spatial features, something that is normally done with convolutional filters. Ideally then, we’d have at our disposal an architecture that is both […]
Convolutional LSTM for spatial forecasting Read More »
El Niño-Southern Oscillation (ENSO) is an atmospheric phenomenon, located in the tropical Pacific, that greatly affects ecosystems as well as human well-being on a large portion of the globe. We use the convLSTM introduced in a prior post to predict the Niño 3.4 Index from spatially-ordered sequences of sea surface temperatures.
Forecasting El Niño-Southern Oscillation (ENSO) Read More »
This post is an introduction to time-series forecasting with torch. Central topics are data input, and practical usage of RNNs (GRUs/LSTMs). Upcoming posts will build on this, and introduce increasingly involved architectures.
Introductory time-series forecasting with torch Read More »
We continue our exploration of time-series forecasting with torch, moving on to architectures designed for multi-step prediction. Here, we augment the “workhorse RNN” by a multi-layer perceptron (MLP) to extrapolate multiple timesteps into the future.
torch time series continued: A first go at multi-step prediction Read More »
In a Bayesian neural network, layer weights are distributions, not tensors. Using tfprobability, the R wrapper to TensorFlow Probability, we can build regular Keras models that have probabilistic layers, and thus get uncertainty estimates “for free”. In this post, we show how to define, train and obtain predictions from a probabilistic convolutional neural network.
Variational convnets with tfprobability Read More »
Differential Privacy guarantees that results of a database query are basically independent of the presence in the data of a single individual. Applied to machine learning, we expect that no single training example influences the parameters of the trained model in a substantial way. This post introduces TensorFlow Privacy, a library built on top of
Differential Privacy with TensorFlow Read More »
In nonlinear dynamics, when the state space is thought to be multidimensional but all we have for data is just a univariate time series, one may attempt to reconstruct the true space via delay coordinate embeddings. However, it is not clear a priori how to choose dimensionality and time lag of the reconstruction space. In
Deep attractors: Where deep learning meets chaos Read More »
In a recent post, we showed how an LSTM autoencoder, regularized by false nearest neighbors (FNN) loss, can be used to reconstruct the attractor of a nonlinear, chaotic dynamical system. Here, we explore how that same technique assists in prediction. Matched up with a comparable, capacity-wise, “vanilla LSTM”, FNN-LSTM improves performance on a set of
Time series prediction with FNN-LSTM Read More »
Previous posts featuring tfprobability – the R interface to TensorFlow Probability – have focused on enhancements to deep neural networks (e.g., introducing Bayesian uncertainty estimates) and fitting hierarchical models with Hamiltonian Monte Carlo. This time, we show how to fit time series using dynamic linear models (DLMs), yielding posterior predictive forecasts as well as the
Dynamic linear models with tfprobability Read More »
In this post, we’ll review three advanced techniques for improving the performance and generalization power of recurrent neural networks. We’ll demonstrate all three concepts on a temperature-forecasting problem, where you have access to a time series of data points coming from sensors installed on the roof of a building.
Time Series Forecasting with Recurrent Neural Networks Read More »
