Post on 21-Jan-2018
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
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