GeoMAN: Multi-level Attention Networks for

Geo-sensory Time Series Prediction

Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, Yu Zheng

yuxliang@outlook.com

Geo-sensory Time Series

• Massive sensors deployed in physical world

Sensors

Roads

Volume: 32

Speed: 50km/h

• Properties

• Each sensor has a unique geospatial location

• Constantly reporting time series readings

• With geospatial correlation between readings

• Prediction on geo-sensory time series

• Motivation: traffic control, air quality forecast…

• Goal: predict target series at a sensor

SensorsPipelines

RC: 0.84

pH: 7.1

Turbidity: 0.54

Sensors

PM2.5: 74

PM10: 90

NO2: 57

𝑡1

𝑡2

𝑡3

Challenges

• Affected by many factors• Readings of previous time interval

• Readings of other sensors in nearby regions

• External factors: weather, time and land use

• Dynamic Inter-sensor correlations

• Dynamic temporal correlation

DAY 1DAY 2

t1

t2

tT

t3

Tim

e

Historical Readings

TimeWeather

POIs Sensor Network

LSTM

Spatial Attn

LSTM

Spatial Attn

LSTM

Spatial Attn

h0

Encoder

Fram

ewo

rk

Spatial Attention

Local Global

Concat

LSTMDecoder

1tc1

ˆ i

ty

Temporal Attn

LSTMtc ˆ i

ty

Co

nca

t

Time Features Embed

Weather Forecasts Embed

SensorID Embed

POIs & Sensor Networks

External Factor Fusion Multi-level Attention Network

LSTMc ˆ iy

𝑿1 𝑿2 𝑿𝑇

t1 t2 tTtT-1

O3

PM10

PM2.5

SO2

CO

???

??

Local spatial attention Global spatial attention External factors fusion

t1

t2

tT

t3

Tim

e

Historical Readings

TimeWeather

POIs Sensor Network

Spatial Attention

𝒉𝑡−1

Local features of a given sensor

Importance of each

local feature at 𝑡

New local features at 𝑡

tanh tanh tanh

softmax

𝒙𝑖,1 𝒙𝑖,𝑘

... ...

𝒙𝑖,𝑁𝑙

𝑎𝑡1 𝑎𝑡

𝑘 𝑎𝑡𝑁𝑙

𝑥𝑡𝑖,1

𝑥𝑡𝑖,𝑘

... ...

... ...

𝑥𝑡𝑖,𝑁𝑙

concat

𝒙𝑡𝑙𝑜𝑐𝑎𝑙

tanh tanh tanh

softmax

𝑿1

... ...

𝛽𝑡1 𝛽𝑡

𝑘 𝛽𝑡𝑁𝑙

𝒉𝑡−1

𝑦𝑡1 𝑦𝑡

𝑘

... ...

... ...

𝑦𝑡𝑁𝑔

concat

𝒙𝑡𝑔𝑙𝑜𝑏𝑎𝑙

𝑿𝑘 𝑿𝑵𝒈

Similarity

matrix

Historical readings of each sensor

• Local spatial attention• Local features target series

• Global spatial attention• Select relevant sensors

Importance of

each sensor at 𝑡

New global features at 𝑡

Local features

at time 𝑡Target series of

all sensors at 𝑡

Temporal Attention

• Sequence-to-sequence architecture

• Select relevant previous time slots to make predictions

𝑿1

𝒉1

𝑿2

𝒉2

𝑿3

𝒉3

𝑿5

𝒉5

𝑿6

𝒉6

𝑿7

𝒉7

𝑿8

𝒉8

𝑿4

𝒉4

𝑿9

𝒉9

Encoder Decoder

Evaluation

• Task 1 - water quality prediction

• Water quality data

• Residual chlorine

• 10 kinds of time series

• From 14 sensors in Shenzhen

• Update each 5 minutes

• Meteorology data

• POIs data

• Task 2 - air quality prediction

• Air quality data

• PM2.5

• 19 kinds of time series

• From 35 sensors in Beijing

• Hourly updates

• Meteorology data

• POIs data

Results

Results

Visualization: Dynamic Correlation

• Case study over air quality dataset

• Discuss on sensor 𝑆0

• 4:00 to 16:00 on Feb. 28, 2017

(a) Air quality stations in Beijing

S0

S1

S11

S6

S13

S17 S32

S23

S27 S26

S3

S4S16

Target sensor Discussed sensor

(b) Plot of local spatial attention weights

0

3

6

9

Wind speed towards different directionsAir pollutants

Southeast

wind

Hu

mid

ity

NO

2

Tem

per

ature

Enco

der

Ste

p

0

3

6

9

S6 S11 S16 S23S1

(c) Plot of global spatial attention weights

Remote sensors

En

cod

er

Ste

pS13

Conclusion

• A very fundamental but challenging task• Dynamic inter-sensor correlation

• Dynamic temporal correlation

• External factors

• Our method• Multi-level attention network

• Spatial attention: captures the dynamic inter-sensor correlation

• Temporal attention: captures the dynamic temporal correlation

• External factor fusion

• Results• More accurate

• Easily interpreted

Data & Code

Thanks!

We are Hiring!

Mail to: icity@jd.com

Liang, Yuxuan, et al. “GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction." IJCAI. 2018.