Commit 64510496 authored by Lukas-Stingl's avatar Lukas-Stingl
Browse files
parents 48fe31ce 3200adbb
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%% Cell type:code id: tags:
```
import os
import pandas as pd
from matplotlib import pyplot as plt
from pmdarima.arima import ADFTest
from pmdarima import auto_arima
#import sys
#sys.path.append('../utils')
#import prepro
```
%% Cell type:code id: tags:
```
csv_folder = '../data/modeling/train/clustered/clust0/daily/'
csv_files = [csv for csv in os.listdir(csv_folder) if csv.endswith('.csv')]
daily = []
for file in csv_files:
new = pd.DataFrame()
# import DataFrame
df = pd.read_csv('../data/modeling/train/clustered/clust0/daily/' + file)
new = df[['time_stamp', 'inter_pol']]
new.set_index('time_stamp', inplace = True)
daily.append(new)
```
%% Cell type:code id: tags:
```
#test for stionarity
for df in daily:
adf_test = ADFTest(alpha = 0.05)
print(adf_test.should_diff(df))
```
%%%% Output: stream
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
(0.01, False)
%% Cell type:code id: tags:
```
plt.plot(daily[0])
```
%%%% Output: execute_result
[<matplotlib.lines.Line2D at 0x7fbef3a72160>]
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:code id: tags:
```
train = daily[0][:300]
test = daily[0][-66:]
plt.plot(train)
plt.plot(test)
```
%%%% Output: execute_result
[<matplotlib.lines.Line2D at 0x7fbe49e9e700>]
%%%% Output: display_data
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)
%% Cell type:code id: tags:
```
arima_model = auto_arima(train,start_p=5,d=1,start_q=5,
max_p=20,max_d=20,max_q=20, start_P=5,
D=1, start_Q=5, max_P=20,max_D=20,
max_Q=20, m=12, seasonal=True,
error_action='warn',trace=True,
supress_warnings=True,stepwise=True,
random_state=20,n_fits=100)
```
%%%% Output: stream
Performing stepwise search to minimize aic
%%%% Output: error
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-65-ca7c907b264c> in <module>
----> 1 arima_model = auto_arima(train,start_p=5,d=1,start_q=5,
2 max_p=20,max_d=20,max_q=20, start_P=5,
3 D=1, start_Q=5, max_P=20,max_D=20,
4 max_Q=20, m=12, seasonal=True,
5 error_action='warn',trace=True,
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/pmdarima/arima/auto.py in auto_arima(y, X, start_p, d, start_q, max_p, max_d, max_q, start_P, D, start_Q, max_P, max_D, max_Q, max_order, m, seasonal, stationary, information_criterion, alpha, test, seasonal_test, stepwise, n_jobs, start_params, trend, method, maxiter, offset_test_args, seasonal_test_args, suppress_warnings, error_action, trace, random, random_state, n_fits, return_valid_fits, out_of_sample_size, scoring, scoring_args, with_intercept, sarimax_kwargs, **fit_args)
715 )
716
--> 717 sorted_res = search.solve()
718 return _return_wrapper(sorted_res, return_valid_fits, start, trace)
719
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/pmdarima/arima/_auto_solvers.py in solve(self)
284
285 # fit a baseline p, d, q model
--> 286 self._do_fit((p, d, q), (P, D, Q, m))
287
288 # null model with possible constant
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/pmdarima/arima/_auto_solvers.py in _do_fit(self, order, seasonal_order, constant)
231 self.k += 1
232
--> 233 fit, fit_time, new_ic = self._fit_arima(
234 order=order,
235 seasonal_order=seasonal_order,
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/pmdarima/arima/_auto_solvers.py in _fit_candidate_model(y, X, order, seasonal_order, start_params, trend, method, maxiter, fit_params, suppress_warnings, trace, error_action, out_of_sample_size, scoring, scoring_args, with_intercept, information_criterion, **kwargs)
504
505 try:
--> 506 fit.fit(y, X=X, **fit_params)
507
508 # for non-stationarity errors or singular matrices, return None
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/pmdarima/arima/arima.py in fit(self, y, X, **fit_args)
480
481 # Internal call
--> 482 self._fit(y, X, **fit_args)
483
484 # now make a forecast if we're validating to compute the
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/pmdarima/arima/arima.py in _fit(self, y, X, **fit_args)
401 with warnings.catch_warnings(record=False):
402 warnings.simplefilter('ignore')
--> 403 fit, self.arima_res_ = _fit_wrapper()
404 else:
405 fit, self.arima_res_ = _fit_wrapper()
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/pmdarima/arima/arima.py in _fit_wrapper()
391 disp = fit_args.pop("disp", 0)
392
--> 393 return arima, arima.fit(start_params=start_params,
394 method=method,
395 maxiter=_maxiter,
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/statsmodels/tsa/statespace/mlemodel.py in fit(self, start_params, transformed, includes_fixed, cov_type, cov_kwds, method, maxiter, full_output, disp, callback, return_params, optim_score, optim_complex_step, optim_hessian, flags, low_memory, **kwargs)
688 flags['hessian_method'] = optim_hessian
689 fargs = (flags,)
--> 690 mlefit = super(MLEModel, self).fit(start_params, method=method,
691 fargs=fargs,
692 maxiter=maxiter,
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/statsmodels/base/model.py in fit(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)
517 warn_convergence = kwargs.pop('warn_convergence', True)
518 optimizer = Optimizer()
--> 519 xopt, retvals, optim_settings = optimizer._fit(f, score, start_params,
520 fargs, kwargs,
521 hessian=hess,
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/statsmodels/base/optimizer.py in _fit(self, objective, gradient, start_params, fargs, kwargs, hessian, method, maxiter, full_output, disp, callback, retall)
222
223 func = fit_funcs[method]
--> 224 xopt, retvals = func(objective, gradient, start_params, fargs, kwargs,
225 disp=disp, maxiter=maxiter, callback=callback,
226 retall=retall, full_output=full_output,
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/statsmodels/base/optimizer.py in _fit_lbfgs(f, score, start_params, fargs, kwargs, disp, maxiter, callback, retall, full_output, hess)
627 func = f
628
--> 629 retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter,
630 callback=callback, args=fargs,
631 bounds=bounds, disp=disp,
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/scipy/optimize/lbfgsb.py in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)
195 'maxls': maxls}
196
--> 197 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
198 **opts)
199 d = {'grad': res['jac'],
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/scipy/optimize/lbfgsb.py in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)
358 # until the completion of the current minimization iteration.
359 # Overwrite f and g:
--> 360 f, g = func_and_grad(x)
361 elif task_str.startswith(b'NEW_X'):
362 # new iteration
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py in fun_and_grad(self, x)
259 self._update_x_impl(x)
260 self._update_fun()
--> 261 self._update_grad()
262 return self.f, self.g
263
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py in _update_grad(self)
229 def _update_grad(self):
230 if not self.g_updated:
--> 231 self._update_grad_impl()
232 self.g_updated = True
233
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py in update_grad()
149 self._update_fun()
150 self.ngev += 1
--> 151 self.g = approx_derivative(fun_wrapped, self.x, f0=self.f,
152 **finite_diff_options)
153
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/scipy/optimize/_numdiff.py in approx_derivative(fun, x0, method, rel_step, abs_step, f0, bounds, sparsity, as_linear_operator, args, kwargs)
484
485 if sparsity is None:
--> 486 return _dense_difference(fun_wrapped, x0, f0, h,
487 use_one_sided, method)
488 else:
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/scipy/optimize/_numdiff.py in _dense_difference(fun, x0, f0, h, use_one_sided, method)
555 x = x0 + h_vecs[i]
556 dx = x[i] - x0[i] # Recompute dx as exactly representable number.
--> 557 df = fun(x) - f0
558 elif method == '3-point' and use_one_sided[i]:
559 x1 = x0 + h_vecs[i]
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/scipy/optimize/_numdiff.py in fun_wrapped(x)
435
436 def fun_wrapped(x):
--> 437 f = np.atleast_1d(fun(x, *args, **kwargs))
438 if f.ndim > 1:
439 raise RuntimeError("`fun` return value has "
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py in fun_wrapped(x)
128 def fun_wrapped(x):
129 self.nfev += 1
--> 130 return fun(x, *args)
131
132 def update_fun():
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/statsmodels/base/model.py in f(params, *args)
499
500 def f(params, *args):
--> 501 return -self.loglike(params, *args) / nobs
502
503 if method == 'newton':
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/statsmodels/tsa/statespace/mlemodel.py in loglike(self, params, *args, **kwargs)
923 kwargs['inversion_method'] = INVERT_UNIVARIATE | SOLVE_LU
924
--> 925 loglike = self.ssm.loglike(complex_step=complex_step, **kwargs)
926
927 # Koopman, Shephard, and Doornik recommend maximizing the average
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/statsmodels/tsa/statespace/kalman_filter.py in loglike(self, **kwargs)
981 kwargs.setdefault('conserve_memory',
982 MEMORY_CONSERVE ^ MEMORY_NO_LIKELIHOOD)
--> 983 kfilter = self._filter(**kwargs)
984 loglikelihood_burn = kwargs.get('loglikelihood_burn',
985 self.loglikelihood_burn)
~/opt/miniconda3/envs/bda/lib/python3.9/site-packages/statsmodels/tsa/statespace/kalman_filter.py in _filter(self, filter_method, inversion_method, stability_method, conserve_memory, filter_timing, tolerance, loglikelihood_burn, complex_step)
904
905 # Run the filter
--> 906 kfilter()
907
908 return kfilter
KeyboardInterrupt:
%% Cell type:code id: tags:
```
arima_model = auto_
```
%%%% Output: stream
Performing stepwise search to minimize aic
ARIMA(5,1,5)(0,0,0)[0] intercept : AIC=inf, Time=1.10 sec
ARIMA(0,1,0)(0,0,0)[0] intercept : AIC=2493.904, Time=0.02 sec
ARIMA(1,1,0)(0,0,0)[0] intercept : AIC=2400.849, Time=0.06 sec
ARIMA(0,1,1)(0,0,0)[0] intercept : AIC=2275.303, Time=0.07 sec
ARIMA(0,1,0)(0,0,0)[0] : AIC=2491.918, Time=0.01 sec
ARIMA(1,1,1)(0,0,0)[0] intercept : AIC=2270.152, Time=0.14 sec
ARIMA(2,1,1)(0,0,0)[0] intercept : AIC=2261.452, Time=0.16 sec
ARIMA(2,1,0)(0,0,0)[0] intercept : AIC=2291.336, Time=0.15 sec
ARIMA(3,1,1)(0,0,0)[0] intercept : AIC=2252.986, Time=0.20 sec
ARIMA(3,1,0)(0,0,0)[0] intercept : AIC=2293.144, Time=0.18 sec
ARIMA(4,1,1)(0,0,0)[0] intercept : AIC=2229.131, Time=0.25 sec
ARIMA(4,1,0)(0,0,0)[0] intercept : AIC=2237.383, Time=0.16 sec
ARIMA(5,1,1)(0,0,0)[0] intercept : AIC=2224.109, Time=0.45 sec
ARIMA(5,1,0)(0,0,0)[0] intercept : AIC=2235.238, Time=0.22 sec
ARIMA(6,1,1)(0,0,0)[0] intercept : AIC=2226.100, Time=0.64 sec
ARIMA(5,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.78 sec
ARIMA(4,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.66 sec
ARIMA(6,1,0)(0,0,0)[0] intercept : AIC=2227.380, Time=0.32 sec
ARIMA(6,1,2)(0,0,0)[0] intercept : AIC=inf, Time=0.91 sec
ARIMA(5,1,1)(0,0,0)[0] : AIC=2222.143, Time=0.24 sec
ARIMA(4,1,1)(0,0,0)[0] : AIC=2227.153, Time=0.12 sec
ARIMA(5,1,0)(0,0,0)[0] : AIC=2233.261, Time=0.12 sec
ARIMA(6,1,1)(0,0,0)[0] : AIC=2224.133, Time=0.45 sec
ARIMA(5,1,2)(0,0,0)[0] : AIC=2193.221, Time=0.59 sec
ARIMA(4,1,2)(0,0,0)[0] : AIC=2193.016, Time=0.32 sec
ARIMA(3,1,2)(0,0,0)[0] : AIC=2192.071, Time=0.18 sec
ARIMA(2,1,2)(0,0,0)[0] : AIC=2192.551, Time=0.54 sec
ARIMA(3,1,1)(0,0,0)[0] : AIC=2250.992, Time=0.10 sec
ARIMA(3,1,3)(0,0,0)[0] : AIC=2193.665, Time=0.29 sec
ARIMA(2,1,1)(0,0,0)[0] : AIC=2259.454, Time=0.10 sec
ARIMA(2,1,3)(0,0,0)[0] : AIC=2191.677, Time=0.24 sec
ARIMA(1,1,3)(0,0,0)[0] : AIC=inf, Time=0.31 sec
ARIMA(2,1,4)(0,0,0)[0] : AIC=2193.645, Time=0.32 sec
ARIMA(1,1,2)(0,0,0)[0] : AIC=inf, Time=0.20 sec
ARIMA(1,1,4)(0,0,0)[0] : AIC=inf, Time=0.33 sec
ARIMA(3,1,4)(0,0,0)[0] : AIC=2195.340, Time=0.75 sec
ARIMA(2,1,3)(0,0,0)[0] intercept : AIC=inf, Time=0.72 sec
Best model: ARIMA(2,1,3)(0,0,0)[0]
Total fit time: 12.479 seconds
%% Cell type:code id: tags:
```
arima_model.summary()
```
%%%% Output: execute_result
<class 'statsmodels.iolib.summary.Summary'>
"""
SARIMAX Results
==============================================================================
Dep. Variable: y No. Observations: 290
Model: SARIMAX(5, 1, 1) Log Likelihood -1062.448
Date: Tue, 13 Jul 2021 AIC 2138.897
Time: 00:57:34 BIC 2164.562
Sample: 0 HQIC 2149.181
- 290
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 0.0239 0.082 0.293 0.769 -0.136 0.184
ar.L2 -0.1874 0.080 -2.351 0.019 -0.344 -0.031
ar.L3 -0.2115 0.087 -2.434 0.015 -0.382 -0.041
ar.L4 -0.2139 0.077 -2.778 0.005 -0.365 -0.063
ar.L5 -0.2423 0.069 -3.512 0.000 -0.378 -0.107
ma.L1 0.9020 0.059 15.346 0.000 0.787 1.017
sigma2 90.5831 6.429 14.091 0.000 77.983 103.183
===================================================================================
Ljung-Box (L1) (Q): 0.10 Jarque-Bera (JB): 20.58
Prob(Q): 0.75 Prob(JB): 0.00
Heteroskedasticity (H): 1.17 Skew: 0.48
Prob(H) (two-sided): 0.44 Kurtosis: 3.89
===================================================================================
Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
"""
%% Cell type:code id: tags:
```
pred = pd.DataFrame(arima_model.predict(n_periods = 75), index = test.index)
pred.columns = ['predicted_heigth']
pred.head(5)
```
%%%% Output: execute_result
predicted_heigth
time_stamp
2021-02-23 94.674403
2021-02-24 88.454877
2021-02-25 88.645007
2021-02-26 92.813250
2021-02-27 98.118809
%% Cell type:code id: tags:
```
plt.figure(figsize=(8,5))
plt.plot(train, label = 'Training')
plt.plot(pred, label = 'Predicted')
plt.legend(loc = 'upper left')
plt.show()
```
%%%% Output: display_data
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)
%% Cell type:code id: tags:
```
for t
```
......
This diff is collapsed.
# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: osx-64
appnope=0.1.2=py39hecd8cb5_1001
backcall=0.2.0=py_0
beautifulsoup4=4.9.3=pypi_0
ca-certificates=2020.10.14=0
certifi=2021.5.30=py39hecd8cb5_0
cycler=0.10.0=pypi_0
cython=0.29.23=pypi_0
decorator=4.4.2=py_0
ipykernel=5.3.4=py39h01d92e1_0
ipython=7.22.0=py39h01d92e1_0
ipython_genutils=0.2.0=pyhd3eb1b0_1
jedi=0.17.2=py39hecd8cb5_1
joblib=1.0.1=pypi_0
jupyter_client=5.3.3=py_0
jupyter_core=4.5.0=py_0
kiwisolver=1.3.1=pypi_0
libcxx=10.0.0=1
libffi=3.3=hb1e8313_2
libsodium=1.0.18=h1de35cc_0
llvmlite=0.36.0=pypi_0
mat4py=0.5.0=pypi_0
matplotlib=3.4.2=pypi_0
ncurses=6.2=h0a44026_1
numba=0.53.1=pypi_0
numpy=1.20.3=pypi_0
openssl=1.1.1k=h9ed2024_0
pandas=1.2.4=pypi_0
parso=0.7.0=py_0
pexpect=4.8.0=pyhd3eb1b0_3
pickleshare=0.7.5=pyhd3eb1b0_1003
pillow=8.2.0=pypi_0
pip=21.1.2=py39hecd8cb5_0
prompt-toolkit=3.0.8=py_0
ptyprocess=0.7.0=pyhd3eb1b0_2
pygments=2.7.1=py_0
pyparsing=2.4.7=pypi_0
python=3.9.5=h88f2d9e_3
python-dateutil=2.8.1=py_0
pytz=2021.1=pypi_0
pyzmq=20.0.0=py39h23ab428_1
readline=8.1=h9ed2024_0
scikit-learn=0.24.2=pypi_0
scipy=1.6.3=pypi_0
seaborn=0.11.1=pypi_0
setuptools=52.0.0=py39hecd8cb5_0
six=1.15.0=py_0
soupsieve=2.2.1=pypi_0
sqlite=3.35.4=hce871da_0
threadpoolctl=2.1.0=pypi_0
tk=8.6.10=hb0a8c7a_0
tornado=6.1=py39h9ed2024_0
traitlets=5.0.5=py_0
tslearn=0.5.0.5=pypi_0
tzdata=2020f=h52ac0ba_0
wcwidth=0.2.5=py_0
wheel=0.36.2=pyhd3eb1b0_0
xz=5.2.5=h1de35cc_0
zeromq=4.3.3=hb1e8313_3
zlib=1.2.11=h1de35cc_3
#pip install requirements.txt
ipykernel
numpy
pandas
scipy
scikit-learn
tslearn
matplotlib
seaborn
tensorflow
keras
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from scipy.signal import lfilter
from tslearn.clustering import TimeSeriesKMeans
from tslearn.preprocessing import TimeSeriesScalerMeanVariance
from tslearn.utils import to_time_series_dataset
#define functions
'''brauchbare Werte werden in Integer umgewandelt, Messfehler [Height > 190 & Temperatur > 100] werden bereineigt.
Wobei Messfehler in Bezug auf die Höhe gelöscht werden und Temperatur Messfehler mit NaN Werten überschrieben werden '''
def cleaning_del(df):
new = pd.DataFrame()
#setting device id to join multiple bins later in the process
new['device_id'] = df['deveui']
#seeting time stamp
new['time_stamp'] = pd.to_datetime(df['created_at'], format='%Y-%m-%d')
#deleting rows with values height > 190 -> Measurement errors
new['Height'] = df['Height'].str.replace('cm', '').astype(int)
new = new[new.Height < 190]
#casting temperature to int and replace values > 100 with NaN
new['Temperature'] = df['Temperature'].str.replace('C', '').astype(int)
new.loc[(new.Temperature > 100), 'Temperature'] = np.nan
#casting Tilt to int
new['Tilt'] = df['Tilt'].str.replace('Degree', '').astype(int)
#sorting values acording to time_stamp
new.sort_values(by=['time_stamp'], ascending=True, inplace = True)
return new
''' unterschiedliche Smoothing verfahren werden angewandt - einmal auf die ungruppierten Daten und einmal auf die gruppierten Daten. Als aggregationsfunktion wird .mean() verwendet. Als Level wird auf eintägig guppiert.
NaN Temperaturwerte werden mit dem durchschnittlichen Temperaturwert überschrieben - gleiches gilt für Tilt.
beim mov_avg auf height werden NaN durch Interpolation gefüllt
'''
def smoothing_fillingNaN(df):
#smooth with lfilter
n = 60 # the larger n is, the smoother curve will be
b = [1.0 / n] * n
a = 1
df['lfilter'] = lfilter(b,a, df['Height'])
#moving average smoothing
df['mov_avg'] = df['Height'].rolling(30).mean()
#minimum moving average
df['min_avg'] = df['Height'].rolling(30).min()
#creating new DataFrame on Level Daily with aggregation max()
daily = df.groupby(pd.Grouper(key='time_stamp', axis=0,
freq='1D', sort=True)).mean()
#add device_id
daily['device_id'] = df.iloc[1, 0]
#further smoothing on daily level with rolling mean window 2
daily['mov_avg'] = daily['Height'].rolling(2).mean()