gdg devfest xiamen 2017

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

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

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



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


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

TensorFlow API for Time Series● Time Series Analysis



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

Deep Learning Applications for Time Series Analysis



Summary

gdg devfest xiamen 2017

Engineering