BigML Spring 2017 Release

Introducing
Time Series
BigML Spring 2017 Release

BigML, Inc 2Spring Release Webinar - July 2017
Spring 2017 Release
CHEE SING LEE Ph.D. - Machine Learning Engineer
Enter questions into chat box – We will answer
some via chat; others at the end of the session
https://bigml.com/releases/spring-2017
ATAKAN CETINSOY - VP of Predictive Applications
Resources
Moderator
Speaker
Contact info@bigml.com
Twitter @bigmlcom
Questions

Machine Learning Data
Color Mass PPAP
red 11 pen
green 45 apple
red 53 apple
yellow 0 pen
blue 2 pen
green 422 pineapple
yellow 555 pineapple
blue 7 pen
Discovering patterns within data:
• Color = “red” Mass < 100
• PPAP = “pineapple” Color ≠ “blue”
• Color = “blue” PPAP = “pen”

Color Number PPAP
green 45 apple
blue 2 pen
green 422 pineapple
blue 7 pen
yellow 0 pen
yellow 9 pineapple
red 555 apple
red 11 pen
Patterns still hold when rows
re-arranged:
• Color = “red” Mass < 100
• PPAP = “pineapple” Color ≠ “blue”
• Color = “blue” PPAP = “pen”
Machine Learning Data

Time Series Data
Year Pineapple Harvest
1986 50,74
1987 22,03
1988 50,69
1989 40,38
1990 29,80
1991 9,90
1992 73,93
1993 22,95
1994 139,09
1995 115,17
1996 193,88
1997 175,31
1998 223,41
1999 295,03
2000 450,53
Pineapple Harvest
Tons
0
125
250
375
500
Year
1986 1988 1990 1992 1994 1996 1998 2000
Trend
Error

Pineapple Harvest
Tons
0
125
250
375
500
Year
1986 1988 1990 1992 1994 1996 1998 2000
1986 139,09
1987 175,31
1988 9,91
1989 22,95
1990 450,53
1991 73,93
1992 40,38
1993 22,03
1994 295,03
1995 50,74
1996 29,8
1997 223,41
1998 115,17
1999 193,88
2000 50,69
Rearranging Disrupts Patterns
Time Series Data

if (color = “red” and mass = 11) then
PPAP = ?
Extrapolating learned
patterns to novel inputs
Machine Learning Forecasts

if (color = “red” and mass = 11) then
PPAP = [“apple”;34.24%]
Forecasts accompanied
with measure of uncertainty
or conﬁdence
Machine Learning Forecasts

Time Series Forecasts
Forecasts: Extrapolating learned patterns to future times
Forecasts accompanied with measure of uncertainty or conﬁdence
?

Linear Versus Random Split
1986 139,09
1987 175,31
1988 9,91
1989 22,95
1990 450,53
1991 73,93
1992 40,38
1993 22,03
1994 295,03
1995 115,17
1986 139,09
1987 175,31
1988 9,91
1989 22,95
1990 450,53
1991 73,93
1992 40,38
1993 22,03
1994 295,03
1995 115,17
Random Split Linear Split

Time Series Smoothing
Smoothing techniques remove the
“noise” in a time series, so the
underlying “signal” can be observed

Time Series Smoothing
Weight
0
0,05
0,1
0,15
0,2
Lag
1 2 3 4 5 6 7 8 9 10 11 12 13

Exponential Smoothing
Weight
0
0,05
0,1
0,15
0,2
Lag
1 2 3 4 5 6 7 8 9 10 11 12 13

Smoothing Compared
Moving Average Exponential Smoothing
Sum over ﬁxed-width window Sum over entire series history
Uniformly Weighted Exponentially Weighted
1 integer parameter: window size
2 continuous parameters: initial state and
smoothing coeﬃcient
• Continuous parameters → Fine-tuned optimization
• State-space model allows for error estimation
• Extensions for trend and seasonality

Trend
• Change or slope between
consecutive level values
• Smoothed exponentially like level,
coeﬃcient 𝛽
• Aﬀects overall trajectory of time
series.
y
0
12,5
25
37,5
50
Time
Apr May Jun Jul
Additive Trend

Trend
y
0
50
100
150
200
Time
Apr May Jun Jul
Multiplicative Trend
coeﬃcient 𝛽
series.

Trend
y
0
25
50
75
100
Time
Apr May Jun Jul Aug Sep
Multiplicative Damped Trend
coeﬃcient 𝛽
series.

Seasonality
• Seasonality is a recurring pattern of variation that occurs over periods of
consistent length
• Examples:
• Monthly A/C sales: higher in summer months and lower in winter months,
repeats with a period of 12 data points
• Vehicle traffic in an office district: higher during the workweek, lower during
the weekend; period of 7 data points
• May be present in time series with and without trend
• Seasonality is also smoothed exponentially, coefficient 𝛾

Seasonalityy
0
30
60
90
120
Time
1 3 5 7 9 11 13 15 17 19
y
0
35
70
105
140
Time
1 3 5 7 9 11 13 15 17 19
Additive Multiplicative

Error
• Variation in time series values not explained by trend or seasonality
• Can be additive or multiplicative (homoscedastic or heteroscedastic)
• Primarily aﬀects forecast intervals.

Errory
0
150
300
450
600
Time
1 3 5 7 9 11 13 15 17 19
y
0
125
250
375
500
Time
1 3 5 7 9 11 13 15 17 19
Additive Multiplicative

M,Ad,N
We use a shorthand to refer to a speciﬁc error, trend, and seasonality
Multiplicative Error Additive Damped Trend No Seasonality
ETS Nomenclature

Exponential Smoothing Models
None Additive Multiplicative
None A,N,N M,N,N A,N,A M,N,A A,N,M M,N,M
Additive A,A,N M,A,N A,A,A M,A,A A,A,M M,A,M
Additive + Damped A,Ad,N M,Ad,N A,Ad,A M,Ad,A A,Ad,M M,Ad,M
Multiplicative A,M,N M,M,N A,M,A M,M,A A,M,M M,M,M
Multiplicative + Damped A,Md,N M,Md,N A,Md,A M,Md,A A,Md,M M,Md,M
Seasonality
Trend

Exponential Smoothing Models
None Additive Multiplicative
None A,N,N M,N,N A,N,A M,N,A A,N,M M,N,M
Additive A,A,N M,A,N A,A,A M,A,A A,A,M M,A,M
Additive + damped A,Ad,N M,Ad,N A,Ad,A M,Ad,A A,Ad,M M,Ad,M
Multiplicative A,M,N M,M,N A,M,A M,M,A A,M,M M,M,M
Multiplicative + Damped A,Md,N M,Md,N A,Md,A M,Md,A A,Md,M M,Md,M
Seasonality
Trend
• Some model types excluded due to numeric instability
• Multiplicative error models only available when data is strictly positive

Model Performance Metrics
AIC AICc BIC R-squared
• Akaike Information
Criterion
• It measures the trade-
off between the
goodness-of-fit and
the model’s complexity.
• The smaller the AIC,
the better.
• Corrected Akaike
Information Criterion
• It introduces a
correction element for
smaller datasets.
• For bigger datasets, the
AIC and AICc tend to
yield similar results.
• Schwarz Bayesian
Information Criterion
• It penalizes the model’s
complexity more
heavily.
• The lower the BIC the
better.
• R squared
• It measures the
model’s errors
compared to the
objective field actual
values.
• The higher the R
squared the better.

Time Series Workﬂow in BigML
SOURCE
DATASET
EVALUATION
FORECASTTIME SERIES FORECAST
+
TEST
TRAIN

Predictive Maintenance
NASA Battery Prognostics Data
Demo #1

Forecasting with Seasonality
Eurostat Agricultural Production Data
Demo #2

Calendar Correction

Calendar Correction
pounds/month ÷ days/month = pounds/day

Calendar Adjustment with WhizzML

BigML Time Series
• Industry-standard exponential smoothing time series analysis and forecasting
• Familiar BigML workflow: source → dataset → timeseries → forecast
• BigML’s market-leading visualization and collaboration
• Can be trained on any numeric field
• Train multiple objective fields and ETS model types in parallel.
• Combined modeling and medium-horizon forecasting
• Integrated with WhizzML and API bindings

Learn about Time Series
https://bigml.com/releases/spring-2017

Questions?
@bigmlcom info@bigml.com

BigML Spring 2017 Release

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