Type 2: Absolute Time-Series

For these datasets, we can treat all the classes’ variables together and avoid trying to learn class-independent effects. In this sense these datasets can essentially be viewed as a time-series dataset.

Possible actions:

  • Synthesize new times (forecast)

  • Synthesize the same times

 
import pandas as pd
from synthesized import MetaExtractor, HighDimSynthesizer
df = pd.read_csv("bitcoin_price.csv")
print(df)
              Date     Open     High      Low    Close        Volume   Market Cap
0     Jul 31, 2017  2763.24  2889.62  2720.61  2875.34  8.605750e+08  45535800000
1     Jul 30, 2017  2724.39  2758.53  2644.85  2757.18  7.059430e+08  44890700000
2     Jul 29, 2017  2807.02  2808.76  2692.80  2726.45  8.037460e+08  46246700000
3     Jul 28, 2017  2679.73  2897.45  2679.73  2809.01  1.380100e+09  44144400000
4     Jul 27, 2017  2538.71  2693.32  2529.34  2671.78  7.891040e+08  41816500000
               ...      ...      ...      ...      ...           ...          ...
1551  May 02, 2013   116.38   125.60    92.28   105.21           NaN   1292190000
1552  May 01, 2013   139.00   139.89   107.72   116.99           NaN   1542820000
1553  Apr 30, 2013   144.00   146.93   134.05   139.00           NaN   1597780000
1554  Apr 29, 2013   134.44   147.49   134.00   144.54           NaN   1491160000
1555  Apr 28, 2013   135.30   135.98   132.10   134.21           NaN   1500520000

[1556 rows x 7 columns]
df = df.set_index('Date')
df.index = pd.to_datetime(df.index)
df = df.sort_index().reset_index().iloc[248:]
dfb = df.assign(
    d_Open=df["Open"].diff().fillna(df["Open"].iloc[0]),
    d_High=df["High"].diff().fillna(df["High"].iloc[0]),
    d_Low=df["Low"].diff().fillna(df["Low"].iloc[0]),
    d_Close=df["Close"].diff().fillna(df["Close"].iloc[0]),
    d_Market_Cap=df["Market Cap"].diff().fillna(df["Market Cap"].iloc[0]),
).drop(
   columns=["Open", "High", "Low", "Close", "Market Cap"]
).reset_index(drop=True)
print(dfb)
          Date        Volume  d_Open  d_High   d_Low  d_Close  d_Market_Cap
0   2016-01-15  1.533510e+08  430.25  430.25  364.33   364.33  6.489870e+09
1   2016-01-16  1.203520e+08  -65.18  -39.69   -9.42    23.21 -9.820800e+08
2   2016-01-17  4.531960e+07   22.08    0.40   25.18    -5.24  3.344800e+08
3   2016-01-18  5.440390e+07   -5.42   -2.86   -3.42     4.87 -8.033000e+07
4   2016-01-19  4.681980e+07    5.30   -0.37    2.30    -7.02  8.157000e+07
           ...           ...     ...     ...     ...      ...           ...
559 2017-07-27  7.891040e+08  -39.06   82.56   78.54   142.33 -6.385000e+08
560 2017-07-28  1.380100e+09  141.02  204.13  150.39   137.23  2.327900e+09
561 2017-07-29  8.037460e+08  127.29  -88.69   13.07   -82.56  2.102300e+09
562 2017-07-30  7.059430e+08  -82.63  -50.23  -47.95    30.73 -1.356000e+09
563 2017-07-31  8.605750e+08   38.85  131.09   75.76   118.16  6.451000e+08

[564 rows x 7 columns]
df_meta = MetaExtractor.extract(dfb)
from synthesized.model import DataFrameModel
DataFrameModel(df_meta).fit(dfb).plot()
absolute time series 7 0 
synth = HighDimSynthesizer(df_meta)
synth.learn(df_train=dfb)
df_synth = synth.synthesize(num_rows=len(dfb))
df_synth = df_synth.set_index("Date").sort_index().reset_index()
df3_synth = df_synth.assign(
    High=x["d_High"].cumsum()
).drop(
    columns=["BALANCE DIFF"]
).sort_index()