https://www.meetup.com/GDG-Xiamen/events/243320151/

1. Taegyun Jeon
GDG DevFest Xiamen 2017
Time Series Analysis using
TensorFlow

2. Taegyun Jeon (South Korea)
Google Developer Expert - Machine Learning (2017)
PhD (Machine Learning)
Speaker

3. Deep Learning Applications for TSA
Time Series Analysis (TSA)
TensorFlow API for Time Series
Contents

4. Deep Learning Applications

5. Deep Learning Applications (TSA)
● Finance
● Speech Recognition
● Natural Language Processing / Translation
● Medicine
● Weather Forecasting
● Sales Forecasting

6. Time Series Classification for Finance
https://cloud.google.com/solutions/financial-services/#development_guides

7. Time Series Classification for Finance
https://cloud.google.com/solutions/financial-services/#development_guides

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

10. 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。

11. Natural Language Translation

12. Cardiogram
https://cardiogr.am

13. Cardiogram

14. EEG Classification (Conv-FC)

15. EEG Classification (LSTM)

16. Weather Forecasting
MeteoSWISS

17. Time Series Analysis

18. Time Series
● Time Series Analysis
● Models for Time Series Analysis: AR, MA, ARMA, ARIMA
● TensorFlow TimeSeries API (TFTS)

19. Time Series Analysis
● Time Series Analysis
● Models for Time Series Analysis: AR, MA, ARMA, ARIMA
● TensorFlow TimeSeries API (TFTS)

20. Time Series Analysis

21. Time Series Data

22. Time Series Data
● Stock values
● Economic variables
● Weather
● Sensor: Internet-of-Things
● Energy demand
● Signal processing
● Sales forecasting

25. 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

26. Models for Time Series Analysis
● Time Series Analysis
● Models for Time Series Analysis: AR, MA, ARMA, ARIMA,
Recurrent Neural Networks
● TensorFlow TimeSeries API (TFTS)

27. 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

28. Moving Average (MA)
● MA(q) model
: Linear generative model for noise term on the qth order Markov
assumption
○ : moving average coefficients

29. ARMA Model
● ARMA(p,q) model
: generative linear model that combines AR(p) and MA(q) models

30. Stationarity
● Definition: a sequence of random variables is stationary if its
distribution is invariant to shifting in time.

31. 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.

32. 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

33. 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

34. Recurrent Neural Networks

35. Recurrent Neural Networks

36. Recurrent Neural Networks

37. Recurrent Neural Networks

38. Recurrent Neural Networks

39. Recurrent Neural Networks

40. Recurrent Neural Networks

41. Recurrent Neural Networks

42. Recurrent Neural Networks

43. TensorFlow API for Time Series

44. TensorFlow API for Time Series
● Time Series Analysis
● Models for Time Series Analysis: AR, MA, ARMA, ARIMA
● TensorFlow TimeSeries API (TFTS)

47. TensorFlow TimeSeries
● tf.contrib.timeseries
○ Classic model (state space, autoregressive)
○ Flexible infrastructure
○ Data management
■ Chunking
■ Batching
■ Saving model
■ Truncated backpropagation

48. EXAMPLES
1. Probabilistic Forecasts
2. Known Anomaly
3. Multivariate Forecasting / Anomaly Detection
4. Custom Model Building
TensorFlow TimeSeries

49. EXAMPLES
1. Probabilistic Forecasts
2. Known Anomaly
3. Multivariate Forecasting / Anomaly Detection
4. Custom Model Building
TensorFlow TimeSeries

50. EXAMPLES
1. Probabilistic Forecasts
2. Known Anomaly
3. Multivariate Forecasting / Anomaly Detection
4. Custom Model Building
TensorFlow TimeSeries

51. EXAMPLES
1. Probabilistic Forecasts
2. Known Anomaly
3. Multivariate Forecasting / Anomaly Detection
4. Custom Model Building
TensorFlow TimeSeries

52. EXAMPLES
1. Probabilistic Forecasts
2. Known Anomaly
3. Multivariate Forecasting / Anomaly Detection
4. Custom Model Building
TensorFlow TimeSeries

53. Deep Learning Applications for Time Series Analysis
Time Series Analysis
TensorFlow API for Time Series
Summary