Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
New in nnetsauce v0.16.3:
- add robust scaler to
type_scalingin all models - relatively faster scaling in preprocessing
- Regression-based classifiers (see https://www.researchgate.net/publication/377227280_Regression-based_machine_learning_classifiers)
DeepMTS(multivariate time series forecasting with deep quasi-random layers): see https://thierrymoudiki.github.io/blog/2024/01/15/python/quasirandomizednn/forecasting/DeepMTS- AutoML for
MTS(multivariate time series forecasting): see https://thierrymoudiki.github.io/blog/2023/10/29/python/quasirandomizednn/MTS-LazyPredict - AutoML for
DeepMTS(multivariate time series forecasting): see https://github.com/Techtonique/nnetsauce/blob/master/nnetsauce/demo/thierrymoudiki_20240106_LazyDeepMTS.ipynb - Spaghetti plots for
MTSandDeepMTS(multivariate time series forecasting): see below - Subsample continuous and discrete responses
DeepMTS in nnetsauce v0.16.3 for Multivariate time series (MTS)
Contents
- 1 - Install
- 2 - DeepMTS
1 - Install
!pip uninstall nnetsauce --yes
Found existing installation: nnetsauce 0.16.3
Uninstalling nnetsauce-0.16.3:
Successfully uninstalled nnetsauce-0.16.3
!pip install git+https://github.com/Techtonique/nnetsauce.git --upgrade --no-cache-dir
Collecting git+https://github.com/Techtonique/nnetsauce.git
Cloning https://github.com/Techtonique/nnetsauce.git to /tmp/pip-req-build-2fy08xrz
Running command git clone --filter=blob:none --quiet https://github.com/Techtonique/nnetsauce.git /tmp/pip-req-build-2fy08xrz
Resolved https://github.com/Techtonique/nnetsauce.git to commit e99ea1404604dc282576abc610b44c490cd8b598
Preparing metadata (setup.py) ... [?25l[?25hdone
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Building wheels for collected packages: nnetsauce
Building wheel for nnetsauce (setup.py) ... [?25l[?25hdone
Created wheel for nnetsauce: filename=nnetsauce-0.16.3-py2.py3-none-any.whl size=152402 sha256=10d081174d14ad5b6af07273a895e85fa0ff28527ec2a27db90aff43102e47f5
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Successfully built nnetsauce
Installing collected packages: nnetsauce
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#!pip install nnetsauce==0.16.2 --upgrade --no-cache-dir
!pip install statsmodels
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import nnetsauce as ns
import numpy as np
import pandas as pd
from sklearn.linear_model import ElasticNetCV, LassoCV
from sklearn.metrics import mean_squared_error
import statsmodels.api as sm
from statsmodels.tsa.base.datetools import dates_from_str
2 - DeepMTS
Macro data
# some example data
mdata = sm.datasets.macrodata.load_pandas().data
# prepare the dates index
dates = mdata[['year', 'quarter']].astype(int).astype(str)
quarterly = dates["year"] + "Q" + dates["quarter"]
quarterly = dates_from_str(quarterly)
print(mdata.head())
#mdata = mdata[['realgdp','realcons','realinv', 'realgovt',
# 'realdpi', 'cpi', 'm1', 'tbilrate', 'unemp',
# 'pop']]
mdata = mdata[['realgovt', 'tbilrate', 'cpi']]
mdata.index = pd.DatetimeIndex(quarterly)
data = np.log(mdata).diff().dropna()
#data = mdata
display(data)
year quarter realgdp realcons realinv realgovt realdpi cpi \
0 1959.00 1.00 2710.35 1707.40 286.90 470.05 1886.90 28.98
1 1959.00 2.00 2778.80 1733.70 310.86 481.30 1919.70 29.15
2 1959.00 3.00 2775.49 1751.80 289.23 491.26 1916.40 29.35
3 1959.00 4.00 2785.20 1753.70 299.36 484.05 1931.30 29.37
4 1960.00 1.00 2847.70 1770.50 331.72 462.20 1955.50 29.54
m1 tbilrate unemp pop infl realint
0 139.70 2.82 5.80 177.15 0.00 0.00
1 141.70 3.08 5.10 177.83 2.34 0.74
2 140.50 3.82 5.30 178.66 2.74 1.09
3 140.00 4.33 5.60 179.39 0.27 4.06
4 139.60 3.50 5.20 180.01 2.31 1.19
| realgovt | tbilrate | cpi | |
|---|---|---|---|
| 1959-06-30 | 0.02 | 0.09 | 0.01 |
| 1959-09-30 | 0.02 | 0.22 | 0.01 |
| 1959-12-31 | -0.01 | 0.13 | 0.00 |
| 1960-03-31 | -0.05 | -0.21 | 0.01 |
| 1960-06-30 | -0.00 | -0.27 | 0.00 |
| ... | ... | ... | ... |
| 2008-09-30 | 0.03 | -0.40 | -0.01 |
| 2008-12-31 | 0.02 | -2.28 | -0.02 |
| 2009-03-31 | -0.01 | 0.61 | 0.00 |
| 2009-06-30 | 0.03 | -0.20 | 0.01 |
| 2009-09-30 | 0.02 | -0.41 | 0.01 |
202 rows × 3 columns
n = data.shape[0]
max_idx_train = np.floor(n*0.8)
training_index = np.arange(0, max_idx_train)
testing_index = np.arange(max_idx_train, n)
df_train = data.iloc[training_index,:]
df_test = data.iloc[testing_index,:]
# Adjust ElasticNetCV
regr7 = ElasticNetCV()
obj_MTS2 = ns.DeepMTS(regr7,
n_layers=3,
lags = 4,
n_hidden_features=5,
replications=10,
kernel='gaussian',
verbose = 1)
obj_MTS2.fit(df_train)
res4 = obj_MTS2.predict(h=len(testing_index))
Adjusting DeepRegressor to multivariate time series...
100%|██████████| 3/3 [00:02<00:00, 1.02it/s]
Simulate residuals using gaussian kernel...
Best parameters for gaussian kernel: {'bandwidth': 0.022335377063851233}
100%|██████████| 10/10 [00:00<00:00, 1582.64it/s]
100%|██████████| 10/10 [00:00<00:00, 3664.43it/s]
obj_MTS2.plot("realgovt", type_plot="pi")
obj_MTS2.plot("tbilrate", type_plot="pi")
obj_MTS2.plot("cpi", type_plot="pi")
obj_MTS2.plot("realgovt", type_plot = "spaghetti")
obj_MTS2.plot("tbilrate", type_plot = "spaghetti")
obj_MTS2.plot("cpi", type_plot = "spaghetti")
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