Learn how to analyze time-ordered historical data to forecast future behavior using BigML's Time Series.

1. BigML Education
Time Series
July 2017

2. BigML Education Program 2Time Series
In This Video
• Definition and discussion of time series data
• Outlining different ways to model time series data
• Exploration of the BigML time series model
• Evaluation and comparison of time series models

3. BigML Education Program 3Time Series
Machine Learning Data
Color Mass Type
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
• Type = “pineapple” Color ≠ “blue”
• Color = “blue” PPAP = “pen”

4. BigML Education Program 4Time Series
Machine Learning Data
Color Mass Type
red 53 apple
blue 2 pen
red 11 pen
blue 7 pen
green 45 apple
yellow 555 pineapple
green 422 pineapple
yellow 0 pen
Patterns valid despite reshuffling
• Color = “red” Mass < 100
• Type = “pineapple” Color ≠ “blue”
• Color = “blue” PPAP = “pen”

5. BigML Education Program 5Time Series
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

6. BigML Education Program 6Time Series
Time Series Forecasts
Use the data from the past to predict the future

7. BigML Education Program 7Time Series
Time Series Data
Year Pineapple Harvest
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
Pineapple Harvest
Tons
0
125
250
375
500
Year
1986 1988 1990 1992 1994 1996 1998 2000
Patterns invalid after shuffling

8. BigML Education Program 8Time Series
Exponential Smoothing

9. BigML Education Program 9Time Series
Exponential Smoothing
Weight 0
0,05
0,1
0,15
0,2
Lag
1 3 5 7 9 11 13

10. BigML Education Program 10Time Series
Trendy
0
12,5
25
37,5
50
Time
Apr May Jun Jul
y
0
50
100
150
200
Time
Apr May Jun Jul
Additive Multiplicative

11. BigML Education Program 11Time Series
Seasonalityy
0
30
60
90
120
Time
1 4 7 10 13 16 19
y
0
35
70
105
140
Time
1 4 7 10 13 16 19
Additive Multiplicative

12. BigML Education Program 12Time Series
Errory
0
150
300
450
600
Time
1 4 7 10 13 16 19
y
0
125
250
375
500
Time
1 4 7 10 13 16 19
Additive Multiplicative

13. BigML Education Program 13Time Series
Model Types
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
M,N,A
Multiplicative Error
No Trend
Additive Seasonality

14. BigML Education Program 14Time Series
Linear Splitting
Year Pineapple Harvest
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
Year Pineapple Harvest
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
Linear Split

15. BigML Education Program 15Time Series
Review
• Time series prediction is a distinct type of prediction
problem, with data that is assumed to have a certain
structure
• BigML takes into account a variety of factors when
doing time series modeling, including trend and
seasonality
• You can create a collection of time series models,
select the best one, and do forecasting in one click via
the BigML dashboard
• With linear training/testing splits, you can perform a
reasonable out-of-sample evaluation of your time series
model