Time Series Transformer
Documentation https://allen-chiang.github.io/Time-Series-Transformer/
import pandas as pd
import numpy as np
from time_series_transform.sklearn import *
import time_series_transform as tst
Introduction
This package provides tools for time series data preprocessing. There are two main components inside the package: Time_Series_Transformer and Stock_Transformer. Time_Series_Transformer is a general class for all type of time series data, while Stock_Transformer is a sub-class of Time_Series_Transformer. Time_Series_Transformer has different functions for data manipulation, io transformation, and making simple plots. This tutorial will take a quick look at the functions for data manipulation and basic io. For the plot functions, there will be other tutorial to explain.
Time_Series_Transformer
Since all the time series data having time data, Time_Series_Transformer is required to specify time index. The basic time series data is time series data with no special category. However, there a lot of cases that a time series data is associating with categories. For example, inventory data is usually associate with product name or stores, or stock data is having different ticker names or brokers. To address this question, Time_Series_Transformer can specify the main category index. Given the main category index, the data can be manipulated in parallel corresponding to its category.
Here is a simple example to create a Time_Series_Transformer without specifying its category.
data = {
'time':[1,2,3,4,5],
'data1':[1,2,3,4,5],
'data2':[6,7,8,9,10]
}
trans = tst.Time_Series_Transformer(data,timeSeriesCol='time')
trans
data column
-----------
time
data1
data2
time length: 5
category: None
There are two ways to manipulate the data. The first way is use the pre-made functions, and the second way is to use the transform function and provide your custom function. There are six pre-made functions including make_lag, make_lead, make_lag_sequence, make_lead_sequence, and make_stack_sequence. In the following demonstration, we will show each of the pre-made functions.
Pre-made functions
make_lag and make_lead functions are going to create lag/lead data for input columns. This type of manipulation could be useful for machine learning.
trans = tst.Time_Series_Transformer(data,timeSeriesCol='time')
trans = trans.make_lag(
inputLabels = ['data1','data2'],
lagNum = 1,
suffix = '_lag_',
fillMissing = np.nan
)
print(trans.to_pandas())
time data1 data2 data1_lag_1 data2_lag_1
0 1 1 6 NaN NaN
1 2 2 7 1.0 6.0
2 3 3 8 2.0 7.0
3 4 4 9 3.0 8.0
4 5 5 10 4.0 9.0
trans = tst.Time_Series_Transformer(data,timeSeriesCol='time')
trans = trans.make_lead(
inputLabels = ['data1','data2'],
leadNum = 1,
suffix = '_lead_',
fillMissing = np.nan
)
print(trans.to_pandas())
time data1 data2 data1_lead_1 data2_lead_1
0 1 1 6 2.0 7.0
1 2 2 7 3.0 8.0
2 3 3 8 4.0 9.0
3 4 4 9 5.0 10.0
4 5 5 10 NaN NaN
make_lag_sequence and make_lead_sequence is to create a sequence for a given window length and lag or lead number. This manipulation could be useful for Deep learning.
trans = tst.Time_Series_Transformer(data,timeSeriesCol='time')
trans = trans.make_lag_sequence(
inputLabels = ['data1','data2'],
windowSize = 2,
lagNum =1,
suffix = '_lag_seq_'
)
print(trans.to_pandas())
time data1 data2 data1_lag_seq_2 data2_lag_seq_2
0 1 1 6 [nan, nan] [nan, nan]
1 2 2 7 [nan, 1.0] [nan, 6.0]
2 3 3 8 [1.0, 2.0] [6.0, 7.0]
3 4 4 9 [2.0, 3.0] [7.0, 8.0]
4 5 5 10 [3.0, 4.0] [8.0, 9.0]
trans = tst.Time_Series_Transformer(data,timeSeriesCol='time')
trans = trans.make_lead_sequence(
inputLabels = ['data1','data2'],
windowSize = 2,
leadNum =1,
suffix = '_lead_seq_'
)
print(trans.to_pandas())
time data1 data2 data1_lead_seq_2 data2_lead_seq_2
0 1 1 6 [2.0, 3.0] [7.0, 8.0]
1 2 2 7 [3.0, 4.0] [8.0, 9.0]
2 3 3 8 [4.0, 5.0] [9.0, 10.0]
3 4 4 9 [nan, nan] [nan, nan]
4 5 5 10 [nan, nan] [nan, nan]
Custom Functions
To use the transform function, you have to create your custom functions. The input data will be passed as dict of list, and the output data should be either pandas DataFrame, pandas Series, numpy ndArray or list. Note, the output length should be in consist with the orignal data length.
For exmaple, this function takes input dictionary data and sum them up. The final output is a list.
import copy
def list_output (dataDict):
res = []
for i in dataDict:
if len(res) == 0:
res = copy.deepcopy(dataDict[i])
continue
for ix,v in enumerate(dataDict[i]):
res[ix] += v
return res
trans = tst.Time_Series_Transformer(data,timeSeriesCol='time')
trans = trans.transform(
inputLabels = ['data1','data2'],
newName = 'sumCol',
func = list_output
)
print(trans.to_pandas())
time data1 data2 sumCol
0 1 1 6 7
1 2 2 7 9
2 3 3 8 11
3 4 4 9 13
4 5 5 10 15
The following example will output as pandas DataFrame and also takes additional parameters. Note: since pandas already has column name, the new name will automatically beocme suffix.
def pandas_output(dataDict, pandasColName):
res = []
for i in dataDict:
if len(res) == 0:
res = copy.deepcopy(dataDict[i])
continue
for ix,v in enumerate(dataDict[i]):
res[ix] += v
return pd.DataFrame({pandasColName:res})
trans = tst.Time_Series_Transformer(data,timeSeriesCol='time')
trans = trans.transform(
inputLabels = ['data1','data2'],
newName = 'sumCol',
func = pandas_output,
pandasColName = "pandasName"
)
print(trans.to_pandas())
time data1 data2 sumCol_pandasName
0 1 1 6 7
1 2 2 7 9
2 3 3 8 11
3 4 4 9 13
4 5 5 10 15
Data with Category
Since time series data could be associated with different category, Time_Series_Transformer can specify the mainCategoryCol parameter to point out the main category. This class only provide one columns for main category because multiple dimensions can be aggregated into a new column as main category.
The following example has one category with two type a and b. Each of them has some overlaped and different timestamp.
data = {
"time":[1,2,3,4,5,1,3,4,5],
'data':[1,2,3,4,5,1,2,3,4],
"category":['a','a','a','a','a','b','b','b','b']
}
trans = tst.Time_Series_Transformer(data,'time','category')
trans
data column
-----------
time
data
time length: 5
category: a
data column
-----------
time
data
time length: 4
category: b
main category column: category
Since we specify the main category column, data manipulation functions can use n_jobs to execute the function in parallel. The parallel execution is with joblib implmentation (https://joblib.readthedocs.io/en/latest/).
trans = trans.make_lag(
inputLabels = ['data'],
lagNum = 1,
suffix = '_lag_',
fillMissing = np.nan,
n_jobs = 2,
verbose = 10
)
print(trans.to_pandas())
[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
time data data_lag_1 category
0 1 1 NaN a
1 2 2 1.0 a
2 3 3 2.0 a
3 4 4 3.0 a
4 5 5 4.0 a
5 1 1 NaN b
6 3 2 1.0 b
7 4 3 2.0 b
8 5 4 3.0 b
[Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 3.6s remaining: 0.0s
[Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 3.6s finished
To further support the category, there are two functions to deal with different time length data: pad_different_category_time and remove_different_category_time. The first function is padding the different length into same length, while the other is remove different timestamp.
trans = tst.Time_Series_Transformer(data,'time','category')
trans = trans.pad_different_category_time(fillMissing = np.nan
)
print(trans.to_pandas())
time data category
0 1 1.0 a
1 2 2.0 a
2 3 3.0 a
3 4 4.0 a
4 5 5.0 a
5 1 1.0 b
6 2 NaN b
7 3 2.0 b
8 4 3.0 b
9 5 4.0 b
trans = tst.Time_Series_Transformer(data,'time','category')
trans = trans.remove_different_category_time()
print(trans.to_pandas())
time data category
0 1 1 a
1 3 3 a
2 4 4 a
3 5 5 a
4 1 1 b
5 3 2 b
6 4 3 b
7 5 4 b
IO
IO is a huge component for this package. The current version support pandas DataFrame, numpy ndArray, Apache Arrow Table, Apache Feather, and Apache Parquet. All those io can specify whether to expand category or time for the export format. In this demo, we will show numpy and pandas. Also, Transformer can combine make_label function and sepLabel parameter inside of export to seperate data and label.
pandas
data = {
"time":[1,2,3,4,5,1,3,4,5],
'data':[1,2,3,4,5,1,2,3,4],
"category":['a','a','a','a','a','b','b','b','b']
}
df = pd.DataFrame(data)
trans = tst.Time_Series_Transformer.from_pandas(
pandasFrame = df,
timeSeriesCol = 'time',
mainCategoryCol= 'category'
)
trans
data column
-----------
time
data
time length: 5
category: a
data column
-----------
time
data
time length: 4
category: b
main category column: category
To expand the data, all category should be in consist. Besides the pad and remove function, we can use preprocessType parameter to achive that.
print(trans.to_pandas(
expandCategory = True,
expandTime = False,
preprocessType = 'pad'
))
time data_a data_b
0 1 1 1.0
1 2 2 NaN
2 3 3 2.0
3 4 4 3.0
4 5 5 4.0
print(trans.to_pandas(
expandCategory = False,
expandTime = True,
preprocessType = 'pad'
))
data_1 data_2 data_3 data_4 data_5 category
0 1 2.0 3 4 5 a
1 1 NaN 2 3 4 b
print(trans.to_pandas(
expandCategory = True,
expandTime = True,
preprocessType = 'pad'
))
data_a_1 data_b_1 data_a_2 data_b_2 data_a_3 data_b_3 data_a_4 \
0 1 1.0 2 NaN 3 2.0 4
data_b_4 data_a_5 data_b_5
0 3.0 5 4.0
make_label function can be used with sepLabel parameter. This function can be used for seperating X and y for machine learning cases.
trans = trans.make_lead('data',leadNum = 1,suffix = '_lead_')
trans = trans.make_label("data_lead_1")
data, label = trans.to_pandas(
expandCategory = False,
expandTime = False,
preprocessType = 'pad',
sepLabel = True
)
print(data)
time data category
0 1 1.0 a
1 2 2.0 a
2 3 3.0 a
3 4 4.0 a
4 5 5.0 a
5 1 1.0 b
6 2 NaN b
7 3 2.0 b
8 4 3.0 b
9 5 4.0 b
print(label)
data_lead_1
0 2.0
1 3.0
2 4.0
3 5.0
4 NaN
5 2.0
6 NaN
7 3.0
8 4.0
9 NaN
numpy
Since numpy has no column name, it has to use index number to specify column.
data = {
"time":[1,2,3,4,5,1,3,4,5],
'data':[1,2,3,4,5,1,2,3,4],
"category":['a','a','a','a','a','b','b','b','b']
}
npArray = pd.DataFrame(data).values
trans = tst.Time_Series_Transformer.from_numpy(
numpyData= npArray,
timeSeriesCol = 0,
mainCategoryCol = 2)
trans
data column
-----------
0
1
time length: 5
category: a
data column
-----------
0
1
time length: 4
category: b
main category column: 2
trans = trans.make_lead(1,leadNum = 1,suffix = '_lead_')
trans = trans.make_label("1_lead_1")
X,y = trans.to_pandas(
expandCategory = False,
expandTime = False,
preprocessType = 'pad',
sepLabel = True
)
print(X)
0 1 2
0 1 1.0 a
1 2 2.0 a
2 3 3.0 a
3 4 4.0 a
4 5 5.0 a
5 1 1.0 b
6 2 NaN b
7 3 2.0 b
8 4 3.0 b
9 5 4.0 b
print(y)
1_lead_1
0 2.0
1 3.0
2 4.0
3 5.0
4 NaN
5 2.0
6 NaN
7 3.0
8 4.0
9 NaN
Stock_Transformer
Stock_Transformer is a subclass of Time_Series_Transformer. Hence, all the function demonstrated in Time_Series_Transformer canbe used in Stock_Transformer. The differences for Stock_Transformer is that it is required to specify High, Low, Open, Close, Volume columns. Besides these information, it has pandas-ta strategy implmentation to create technical indicator (https://github.com/twopirllc/pandas-ta). Moreover, the io class for Stock_Transformer support yfinance and investpy. We can directly extract data from these api.
create technical indicator
stock = tst.Stock_Transformer.from_stock_engine_period(
symbols = 'GOOGL',period ='1y', engine = 'yahoo'
)
stock
data column
-----------
Date
Open
High
Low
Close
Volume
Dividends
Stock Splits
time length: 253
category: None
import pandas_ta as ta
MyStrategy = ta.Strategy(
name="DCSMA10",
ta=[
{"kind": "ohlc4"},
{"kind": "sma", "length": 10},
{"kind": "donchian", "lower_length": 10, "upper_length": 15},
{"kind": "ema", "close": "OHLC4", "length": 10, "suffix": "OHLC4"},
]
)
stock = stock.get_technial_indicator(MyStrategy)
print(stock.to_pandas().head())
Date Open High Low Close Volume \
0 2020-01-06 1351.630005 1398.319946 1351.000000 1397.810059 2338400
1 2020-01-07 1400.459961 1403.500000 1391.560059 1395.109985 1716500
2 2020-01-08 1394.819946 1411.849976 1392.630005 1405.040039 1765700
3 2020-01-09 1421.930054 1428.680054 1410.209961 1419.790039 1660000
4 2020-01-10 1429.469971 1434.939941 1419.599976 1428.959961 1312900
Dividends Stock Splits OHLC4 SMA_10 DCL_10_15 DCM_10_15 \
0 0 0 1374.690002 NaN NaN NaN
1 0 0 1397.657501 NaN NaN NaN
2 0 0 1401.084991 NaN NaN NaN
3 0 0 1420.152527 NaN NaN NaN
4 0 0 1428.242462 NaN NaN NaN
DCU_10_15 EMA_10_OHLC4
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
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