Machine Learning for Time Series Data in Python
Chris Holdgraf
Fellow, Berkeley Institute for Data Science
Datapoint | Datapoint | Datapoint | Datapoint | Datapoint | Datapoint |
---|---|---|---|---|---|
1 | 34 | 12 | 54 | 76 | 40 |
Timepoint | Timepoint | Timepoint | Timepoint | Timepoint | Timepoint |
---|---|---|---|---|---|
2:00 | 2:01 | 2:02 | 2:03 | 2:04 | 2:05 |
Timepoint | Timepoint | Timepoint | Timepoint | Timepoint | Timepoint |
---|---|---|---|---|---|
Jan | Feb | March | April | May | Jun |
Timepoint | Timepoint | Timepoint | Timepoint | Timepoint | Timepoint |
---|---|---|---|---|---|
1e-9 | 2e-9 | 3e-9 | 4e-9 | 5e-9 | 6e-9 |
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('data.csv')
data.head()
date symbol close volume
0 2010-01-04 AAPL 214.009998 123432400.0
46 2010-01-05 AAPL 214.379993 150476200.0
92 2010-01-06 AAPL 210.969995 138040000.0
138 2010-01-07 AAPL 210.580000 119282800.0
184 2010-01-08 AAPL 211.980005 111902700.0
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(12, 6))
data.plot('date', 'close', ax=ax)
ax.set(title="AAPL daily closing price")
We can use really big data and really complicated data
We can...
Machine Learning for Time Series Data in Python