Taegyun JeonGDG DevFest Xiamen 2017

Time Series Analysis using TensorFlow

Taegyun Jeon (South Korea)

Google Developer Expert - Machine Learning (2017)

PhD (Machine Learning)

Speaker

Deep Learning Applications for TSA

Time Series Analysis (TSA)

TensorFlow API for Time Series

Contents

Deep Learning Applications

Deep Learning Applications (TSA)● Finance

● Speech Recognition

● Natural Language Processing / Translation

● Medicine

● Weather Forecasting

● Sales Forecasting

Time Series Classification for Finance

https://cloud.google.com/solutions/financial-services/#development_guides

Time Series Classification for Finance

https://cloud.google.com/solutions/financial-services/#development_guides

Speech Recognition

● Speech Recognition API● Google Home, Assistant, Nest and Cast

Natural Language Translation

如果要建造一艘船，不要一起鼓勵人們收集木材，不要分配任務和工作，

而應該教他們漫長的海洋無限遠。

Natural Language Translation

Cardiogram

https://cardiogr.am

Cardiogram

EEG Classification (Conv-FC)

EEG Classification (LSTM)

Weather Forecasting

MeteoSWISS

Time Series Analysis

Time Series● Time Series Analysis

● Models for Time Series Analysis: AR, MA, ARMA, ARIMA

● TensorFlow TimeSeries API (TFTS)

Time Series Analysis● Time Series Analysis

● Models for Time Series Analysis: AR, MA, ARMA, ARIMA

● TensorFlow TimeSeries API (TFTS)

Time Series Analysis

Time Series Data

Time Series Data● Stock values

● Economic variables

● Weather

● Sensor: Internet-of-Things

● Energy demand

● Signal processing

● Sales forecasting

Problems on Time Series Data● Standard Supervised Learning

○ IID assumption

○ Same distribution for training and test data

○ Distributions fixed over time (stationarity)

● Time Series

○ Not applicable

Models for Time Series Analysis● Time Series Analysis

● Models for Time Series Analysis: AR, MA, ARMA, ARIMA,

Recurrent Neural Networks

● TensorFlow TimeSeries API (TFTS)

Autoregressive (AR) Models

● AR(p) model

: Linear generative model based on the pth order Markov assumption

○ : zero mean uncorrelated random variables with variance

○ : autoregressive coefficients

○ : observed stochastic process

Moving Average (MA)

● MA(q) model

: Linear generative model for noise term on the qth order Markov

assumption

○ : moving average coefficients

ARMA Model

● ARMA(p,q) model

: generative linear model that combines AR(p) and MA(q) models

Stationarity

● Definition: a sequence of random variables is stationary if its

distribution is invariant to shifting in time.

Lag Operator● Definition: Lag operator is defined by

● ARMA model in terms of the lag operator:

● Characteristic polynomial

can be used to study properties of this stochastic process.

ARIMA Model

● Definition: Non-stationary processes can be modeled using processes

whose characteristic polynomial has unit roots.

● Characteristic polynomial with unit roots can be factored:

● ARIMA(p, D, q) model is an ARMA(p,q) model for

Other Extensions● Further variants:

○ Models with seasonal components (SARIMA)

○ Models with side information (ARIMAX)

○ Models with long-memory (ARFIMA)

○ Multi-variate time series model (VAR)

○ Models with time-varing coefficients

○ other non-linear models

Recurrent Neural Networks

Recurrent Neural Networks

Recurrent Neural Networks

Recurrent Neural Networks

Recurrent Neural Networks

Recurrent Neural Networks

Recurrent Neural Networks

Recurrent Neural Networks

Recurrent Neural Networks

TensorFlow API for Time Series

TensorFlow API for Time Series● Time Series Analysis

● Models for Time Series Analysis: AR, MA, ARMA, ARIMA

● TensorFlow TimeSeries API (TFTS)

TensorFlow TimeSeries● tf.contrib.timeseries

○ Classic model (state space, autoregressive)

○ Flexible infrastructure

○ Data management

■ Chunking

■ Batching

■ Saving model

■ Truncated backpropagation

EXAMPLES

1. Probabilistic Forecasts2. Known Anomaly3. Multivariate Forecasting / Anomaly Detection4. Custom Model Building

TensorFlow TimeSeries

EXAMPLES

1. Probabilistic Forecasts2. Known Anomaly3. Multivariate Forecasting / Anomaly Detection4. Custom Model Building

TensorFlow TimeSeries

EXAMPLES

1. Probabilistic Forecasts2. Known Anomaly3. Multivariate Forecasting / Anomaly Detection4. Custom Model Building

TensorFlow TimeSeries

EXAMPLES

1. Probabilistic Forecasts2. Known Anomaly3. Multivariate Forecasting / Anomaly Detection4. Custom Model Building

TensorFlow TimeSeries

EXAMPLES

1. Probabilistic Forecasts2. Known Anomaly3. Multivariate Forecasting / Anomaly Detection4. Custom Model Building

TensorFlow TimeSeries

Deep Learning Applications for Time Series Analysis

Time Series Analysis

TensorFlow API for Time Series

Summary