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AO, NAO, ENSO: A wavelet analysis example

AO, NAO, ENSO: A wavelet analysis example

AO, NAO, ENSO: A wavelet analysis example

AO, NAO, ENSO: A wavelet analysis example

El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) are atmospheric phenomena of global impact that strongly affect people’s lives. ENSO, first and foremost, brings with it floods, droughts, and ensuing poverty, in developing countries in the Southern Hemisphere. Here, we use the new torchwavelets package to comparatively inspect patterns in the three series

Machine Learning Ai Torch Packages Releases Time Series

torch time series, final episode: Attention

torch time series, final episode: Attention

torch time series, final episode: Attention

torch time series, final episode: Attention

We conclude our mini-series on time-series forecasting with torch by augmenting last time’s sequence-to-sequence architecture with a technique both immensely popular in natural language processing and inspired by human (and animal) cognition: attention

Machine Learning Ai Torch Time Series

torch time series, take three: Sequence-to-sequence prediction

torch time series, take three: Sequence-to-sequence prediction

torch time series, take three: Sequence-to-sequence prediction

torch time series, take three: Sequence-to-sequence prediction

In our overview of techniques for time-series forecasting, we move on to sequence-to-sequence models. Architectures in this family are commonly used in natural language processing (NLP) tasks, such as machine translation. With NLP, however, significant pre-processing is required before proceeding to model definition and training. In staying with our familiar numerical series, we can fully concentrate on the concepts

Machine Learning Ai Torch Time Series

torch time series continued: A first go at multi-step prediction

torch time series continued: A first go at multi-step prediction

torch time series continued: A first go at multi-step prediction

torch time series continued: A first go at multi-step prediction

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

Machine Learning Ai Torch Time Series

Introductory time-series forecasting with torch

Introductory time-series forecasting with torch

Introductory time-series forecasting with torch

Introductory time-series forecasting with torch

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

Machine Learning Ai Torch Time Series

Forecasting El Niño-Southern Oscillation (ENSO)

Forecasting El Niño-Southern Oscillation (ENSO)

Forecasting El Niño-Southern Oscillation (ENSO)

Forecasting El Niño-Southern Oscillation (ENSO)

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

Machine Learning Ai Torch Image Recognition Image Processing Time Series Spatial Data

Convolutional LSTM for spatial forecasting

Convolutional LSTM for spatial forecasting

Convolutional LSTM for spatial forecasting

Convolutional LSTM for spatial forecasting

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 recurrent and convolutional. In this post, we build a convolutional LSTM with torch

Machine Learning Ai Torch Image Recognition Image Processing Time Series Spatial Data

sparklyr.flint 0.2: ASOF Joins, OLS Regression, and additional summarizers

sparklyr.flint 0.2: ASOF Joins, OLS Regression, and additional summarizers

sparklyr.flint 0.2: ASOF Joins, OLS Regression, and additional summarizers

sparklyr.flint 0.2: ASOF Joins, OLS Regression, and additional summarizers

We are excited to announce a number of powerful, new functionalities and improvements which are now part of sparklyr.flint 0.2!

Machine Learning Ai Packages Releases Time Series

Introducing sparklyr.flint: A time-series extension for sparklyr

Introducing sparklyr.flint: A time-series extension for sparklyr

Introducing sparklyr.flint: A time-series extension for sparklyr

Introducing sparklyr.flint: A time-series extension for sparklyr

We are pleased to announce that sparklyr.flint, a sparklyr extension for analyzing time series at scale with Flint, is now available on CRAN. Flint is an open-source library for working with time-series in Apache Spark which supports aggregates and joins on time-series datasets

Machine Learning Ai Time Series

An introduction to weather forecasting with deep learning

An introduction to weather forecasting with deep learning

An introduction to weather forecasting with deep learning

An introduction to weather forecasting with deep learning

A few weeks ago, we showed how to forecast chaotic dynamical systems with deep learning, augmented by a custom constraint derived from domain-specific insight. Global weather is a chaotic system, but of much higher complexity than many tasks commonly addressed with machine and/or deep learning. In this post, we provide a practical introduction featuring a simple deep learning baseline for atmospheric forecasting. While far away from being competitive, it serves to illustrate how more sophisticated and compute-intensive models may approach that formidable task by means of methods situated on the “black-box end” of the continuum

keras3 Machine Learning Ai TensorFlow Keras Time Series Spatial Data

FNN-VAE for noisy time series forecasting

FNN-VAE for noisy time series forecasting

FNN-VAE for noisy time series forecasting

FNN-VAE for noisy time series forecasting

In the last part of this mini-series on forecasting with false nearest neighbors (FNN) loss, we replace the LSTM autoencoder from the previous post by a convolutional VAE, resulting in equivalent prediction performance but significantly lower training time. In addition, we find that FNN regularization is of great help when an underlying deterministic process is obscured by substantial noise

keras3 Machine Learning Ai TensorFlow Keras Time Series Generative Models

Time series prediction with FNN-LSTM

Time series prediction with FNN-LSTM

Time series prediction with FNN-LSTM

Time series prediction with FNN-LSTM

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 very different, real-world datasets, especially for the initial steps in a multi-step forecast

keras3 Machine Learning Ai TensorFlow Keras Time Series Generative Models

Deep attractors: Where deep learning meets chaos

Deep attractors: Where deep learning meets chaos

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 this post, we show how to use an autoencoder architecture to circumvent the problem: Given just a scalar series of observations, the autoencoder directly learns to represent attractors of chaotic systems in adequate dimensionality

keras3 Machine Learning Ai TensorFlow Keras Time Series Generative Models

Differential Privacy with TensorFlow

Differential Privacy with TensorFlow

Differential Privacy with TensorFlow

Differential Privacy with TensorFlow

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 TensorFlow, that can be used to train differentially private deep learning models from R

keras3 Machine Learning Ai Privacy Security TensorFlow Keras Time Series

Variational convnets with tfprobability

Variational convnets with tfprobability

Variational convnets with tfprobability

Variational convnets with tfprobability

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

keras3 Machine Learning Ai Probabilistic Machine Learning Probabilistic Deep Learning Time Series TensorFlow Keras

Earo Wang | Melt the clock Tidy time series analysis | RStudio (2019)

Earo Wang | Melt the clock Tidy time series analysis | RStudio (2019)

Earo Wang | Melt the clock Tidy time series analysis | RStudio (2019)

Earo Wang | Melt the clock Tidy time series analysis | RStudio (2019)

Time series can be frustrating to work with, particularly when processing raw data into model-ready data. This work presents two new packages that address a gap in existing methodology for time series analysis (raised in rstudio::conf 2018). The tsibble package supports organizing and manipulating modern time series, leveraging tidy data principles along with contextual semantics: index and key. The tsibble data structure seamlessly flows into forecasting routines. The fable package is a tidy renovation of the forecast package. It promotes transparent forecasting practices and concise model representations, to empower analysts tackling a broad domain of forecasting problems. This collection of packages form the tidyverts, which facilitates a fluent and fluid workflow for analyzing time series.

VIEW MATERIALS https://slides.earo.me/rstudioconf19

About the Author Earo Wang I’m currently doing my Ph.D. on statistical visualisation of temporal-context data at Monash University, supervised by Professor Di Cook and Professor Rob J Hyndman. I enjoy developing open-source tools with R, and is the (co)author of some widely-used R packages including anomalous, hts, sugrrants, rwalkr and tsibble. My research areas invovle data visualisation, time series analysis, and computational statistics

Aug 6, 2019

20 min

3.5k views

rstudio Earo Wang Time Series RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss

Dynamic linear models with tfprobability

Dynamic linear models with tfprobability

Dynamic linear models with tfprobability

Dynamic linear models with tfprobability

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 smoothed and filtered estimates from the Kálmán filter

tfprobability tensorflow Machine Learning Ai Probabilistic Machine Learning Probabilistic Deep Learning Time Series

Predicting Sunspot Frequency with Keras

Predicting Sunspot Frequency with Keras

Predicting Sunspot Frequency with Keras

Predicting Sunspot Frequency with Keras

In this post we will examine making time series predictions using the sunspots dataset that ships with base R. Sunspots are dark spots on the sun, associated with lower temperature. Our post will focus on both how to apply deep learning to time series forecasting, and how to properly apply cross validation in this domain

Matt Dancho, Sigrid Keydana

keras3 Machine Learning Ai TensorFlow Keras Time Series

Time Series Forecasting with Recurrent Neural Networks

Time Series Forecasting with Recurrent Neural Networks

Time Series Forecasting with Recurrent Neural Networks

Time Series Forecasting with Recurrent Neural Networks

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

François Chollet, J.J. Allaire

keras3 Machine Learning Ai TensorFlow Keras Time Series

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