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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from fbprophet import Prophet
file_path = 'market-price.csv'
bitcoin_df = pd.read_csv(file_path, names = ['ds', 'y'], header=0)
# 상한가 설정
bitcoin_df['cap'] = 20000
# 하한가 설정
# bitcoin_df['floor'] = 2000
# growth = logistic 상한설정시 추가, 비선형방식으로 분석
prophet = Prophet(seasonality_mode = 'multiplicative',
growth = 'logistic', # 상하한가 설정할때 , 비선형방식
yearly_seasonality = True, # 연별
weekly_seasonality = True, # 주별
daily_seasonality = True, # 일별
changepoint_prior_scale = 0.5) # 과적합 방지 0.5만큼 만 분석
prophet.fit(bitcoin_df) # 학습하기bitcoin_df.head()
ds y cap
0 2017-08-27 00:00:00 4354.308333 20000
1 2017-08-28 00:00:00 4391.673517 20000
2 2017-08-29 00:00:00 4607.985450 20000
3 2017-08-30 00:00:00 4594.987850 20000
4 2017-08-31 00:00:00 4748.255000 20000
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from fbprophet import Prophet
file_path = 'market-price.csv'
bitcoin_df = pd.read_csv(file_path, names = ['ds', 'y'], header=0)
# 상한가 설정
bitcoin_df['cap'] = 20000
# 하한가 설정
# bitcoin_df['floor'] = 2000
# growth = logistic 상한설정시 추가, 비선형방식으로 분석
prophet = Prophet(seasonality_mode = 'multiplicative',
growth = 'logistic', # 상하한가 설정할때 , 비선형방식
yearly_seasonality = True, # 연별
weekly_seasonality = True, # 주별
daily_seasonality = True, # 일별
changepoint_prior_scale = 0.5) # 과적합 방지 0.5만큼 만 분석
(
growth='linear',
changepoints=None,
n_changepoints=25,
changepoint_range=0.8,
yearly_seasonality='auto',
weekly_seasonality='auto',
daily_seasonality='auto',
holidays=None,
seasonality_mode='additive',
seasonality_prior_scale=10.0,
holidays_prior_scale=10.0,
changepoint_prior_scale=0.05,
mcmc_samples=0,
interval_width=0.8,
uncertainty_samples=1000,
stan_backend=None,
)
# 5일 앞을 예측하기
future_data = prophet.make_future_dataframe(periods=5, freq='d')
# 상한가 설정
future_data['cap'] = 20000
# future_data['floor'] = 2000
# 예측
forecast_data = prophet.predict(future_data)
forecast_data
ds trend cap yhat_lower yhat_upper trend_lower trend_upper daily daily_lower daily_upper ... weekly weekly_lower weekly_upper yearly yearly_lower yearly_upper additive_terms additive_terms_lower additive_terms_upper yhat
0 2017-08-27 5621.085431 20000 4008.821488 5757.019304 5621.085431 5621.085431 0.311474 0.311474 0.311474 ... 0.002289 0.002289 0.002289 -0.440095 -0.440095 -0.440095 0.0 0.0 0.0 4910.962354
1 2017-08-28 5626.023045 20000 3955.585468 5723.896533 5626.023045 5626.023045 0.311474 0.311474 0.311474 ... -0.000562 -0.000562 -0.000562 -0.449330 -0.449330 -0.449330 0.0 0.0 0.0 4847.280361
2 2017-08-29 5630.963297 20000 3911.998721 5680.326522 5630.963297 5630.963297 0.311474 0.311474 0.311474 ... -0.000493 -0.000493 -0.000493 -0.459435 -0.459435 -0.459435 0.0 0.0 0.0 4795.023355
3 2017-08-30 5635.906186 20000 3875.520852 5590.533721 5635.906186 5635.906186 0.311474 0.311474 0.311474 ... -0.007356 -0.007356 -0.007356 -0.470318 -0.470318 -0.470318 0.0 0.0 0.0 4699.215406
4 2017-08-31 5640.851711 20000 3810.130956 5423.302109 5640.851711 5640.851711 0.311474 0.311474 0.311474 ... -0.005606 -0.005606 -0.005606 -0.481860 -0.481860 -0.481860 0.0 0.0 0.0 4648.105600
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
365 2018-08-27 7108.210766 20000 5313.384266 7048.183238 7108.210766 7108.210766 0.311474 0.311474 0.311474 ... -0.000562 -0.000562 -0.000562 -0.437930 -0.437930 -0.437930 0.0 0.0 0.0 6205.334670
366 2018-08-28 7111.966217 20000 5257.614559 7014.891617 7111.966217 7111.966217 0.311474 0.311474 0.311474 ... -0.000493 -0.000493 -0.000493 -0.446936 -0.446936 -0.446936 0.0 0.0 0.0 6145.052289
367 2018-08-29 7115.722558 20000 5183.980838 6862.843499 7115.722558 7115.722558 0.311474 0.311474 0.311474 ... -0.007356 -0.007356 -0.007356 -0.456831 -0.456831 -0.456831 0.0 0.0 0.0 6029.053882
368 2018-08-30 7119.479786 20000 5142.817816 6755.514784 7119.464525 7119.496318 0.311474 0.311474 0.311474 ... -0.005606 -0.005606 -0.005606 -0.467530 -0.467530 -0.467530 0.0 0.0 0.0 5968.527240
369 2018-08-31 7123.237902 20000 5101.560913 6845.461477 7123.183774 7123.294421 0.311474 0.311474 0.311474 ... 0.000310 0.000310 0.000310 -0.478920 -0.478920 -0.478920 0.0 0.0 0.0 5932.681373
370 rows × 23 columns
# 그래프로 작성
fig = prophet.plot(forecast_data)
# 실제 데이터와 비교하기
# 예측데이터
pred_y = forecast_data.yhat.values[-5:]
pred_y
# array([6205.33466998, 6145.05228906, 6029.05388165, 5968.52723998,
# 5932.68137291])
# 실제데이터
test_file_path = 'market-price-test.csv'
bitcoin_test_df = pd.read_csv(test_file_path, names = ['ds', 'y'], header=0)
test_y = bitcoin_test_df.y.values
# 예측최소데이터
pred_y_lower = forecast_data.yhat_lower.values[-5:]
# 예측최대데이터
pred_y_upper = forecast_data.yhat_upper.values[-5:]
plt.plot(pred_y, color = 'gold') # 모델 예측한 가격그래프
plt.plot(pred_y_lower, color = 'red') # 모델이 예상한 최소 가격 그래프
plt.plot(pred_y_upper, color = 'blue') # 모델이 예상한 최대 가격 그래프
plt.plot(test_y, color = 'green') # 실제 가격 그래프
# 이상치 제거
bitcoin_df = pd.read_csv(file_path, names = ['ds', 'y'], header=0)
bitcoin_df.loc[bitcoin_df['y'] > 18000, 'y'] = None
bitcoin_df.info()
# 3건 제거
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 365 entries, 0 to 364
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ds 365 non-null object
1 y 362 non-null float64
dtypes: float64(1), object(1)
memory usage: 5.8+ KB
prophet = Prophet(seasonality_mode = 'multiplicative',
yearly_seasonality = True, # 연별
weekly_seasonality = True, # 주별
daily_seasonality = True, # 일별
changepoint_prior_scale = 0.5) # 과적합 방지 0.5만큼 만 분석
prophet.fit(bitcoin_df) # 학습하기
# 5일 앞을 예측하기
future_data = prophet.make_future_dataframe(periods=5, freq='d')
# # 상한가 설정
# future_data['cap'] = 20000
# 예측
forecast_data = prophet.predict(future_data)
forecast_data
ds trend yhat_lower yhat_upper trend_lower trend_upper daily daily_lower daily_upper multiplicative_terms ... weekly weekly_lower weekly_upper yearly yearly_lower yearly_upper additive_terms additive_terms_lower additive_terms_upper yhat
0 2017-08-27 528.085585 3766.698129 5075.496722 528.085585 528.085585 9.711762 9.711762 9.711762 7.371717 ... -0.109233 -0.109233 -0.109233 -2.230812 -2.230812 -2.230812 0.0 0.0 0.0 4420.983246
1 2017-08-28 529.776373 3961.128742 5121.531273 529.776373 529.776373 9.711762 9.711762 9.711762 7.479669 ... -0.054572 -0.054572 -0.054572 -2.177521 -2.177521 -2.177521 0.0 0.0 0.0 4492.328178
2 2017-08-29 531.467162 3978.374754 5172.827221 531.467162 531.467162 9.711762 9.711762 9.711762 7.642893 ... 0.067545 0.067545 0.067545 -2.136414 -2.136414 -2.136414 0.0 0.0 0.0 4593.413651
3 2017-08-30 533.157950 4013.934349 5196.960495 533.157950 533.157950 9.711762 9.711762 9.711762 7.611159 ... 0.009555 0.009555 0.009555 -2.110158 -2.110158 -2.110158 0.0 0.0 0.0 4591.108025
4 2017-08-31 534.848738 4008.172911 5178.939144 534.848738 534.848738 9.711762 9.711762 9.711762 7.647029 ... 0.036189 0.036189 0.036189 -2.100922 -2.100922 -2.100922 0.0 0.0 0.0 4624.852670
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
365 2018-08-27 818.078684 6243.626452 7506.907764 818.078684 818.078684 9.711762 9.711762 9.711762 7.411506 ... -0.054572 -0.054572 -0.054572 -2.245684 -2.245684 -2.245684 0.0 0.0 0.0 6881.273946
366 2018-08-28 822.999668 6453.325229 7704.060396 822.999668 822.999668 9.711762 9.711762 9.711762 7.589491 ... 0.067545 0.067545 0.067545 -2.189816 -2.189816 -2.189816 0.0 0.0 0.0 7069.148295
367 2018-08-29 827.920652 6419.908313 7727.754695 825.682414 827.920652 9.711762 9.711762 9.711762 7.575922 ... 0.009555 0.009555 0.009555 -2.145395 -2.145395 -2.145395 0.0 0.0 0.0 7100.183131
368 2018-08-30 832.841636 6536.153516 7786.172133 823.752948 834.318783 9.711762 9.711762 9.711762 7.632749 ... 0.036189 0.036189 0.036189 -2.115202 -2.115202 -2.115202 0.0 0.0 0.0 7189.713129
369 2018-08-31 837.762619 6550.207854 7952.370851 816.207036 850.803956 9.711762 9.711762 9.711762 7.688080 ... 0.077855 0.077855 0.077855 -2.101537 -2.101537 -2.101537 0.0 0.0 0.0 7278.549033
370 rows × 22 columns
# 그래프로 작성
fig = prophet.plot(forecast_data)
# 실제 데이터와 비교하기
# 예측데이터
pred_y = forecast_data.yhat.values[-5:]
pred_y
# array([6881.2739463 , 7069.14829491, 7100.18313111, 7189.71312892,
# 7278.54903332])
# 실제데이터
test_file_path = 'market-price-test.csv'
bitcoin_test_df = pd.read_csv(test_file_path, names = ['ds', 'y'], header=0)
test_y = bitcoin_test_df.y.values
# 예측최소데이터
pred_y_lower = forecast_data.yhat_lower.values[-5:]
# 예측최대데이터
pred_y_upper = forecast_data.yhat_upper.values[-5:]
plt.plot(pred_y, color = 'gold') # 모델 예측한 가격그래프
plt.plot(pred_y_lower, color = 'red') # 모델이 예상한 최소 가격 그래프
plt.plot(pred_y_upper, color = 'blue') # 모델이 예상한 최대 가격 그래프
plt.plot(test_y, color = 'green') # 실제 가격 그래프
#
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from fbprophet import Prophet
file_path = 'market-price.csv'
bitcoin_df = pd.read_csv(file_path, names = ['ds', 'y'], header=0)
# 상한가 설정
bitcoin_df['cap'] = 20000
# 하한가 설정
bitcoin_df['floor'] = 2000
# growth = logistic 상한설정시 추가, 비선형방식으로 분석
prophet = Prophet(seasonality_mode = 'multiplicative',
growth = 'logistic', # 상하한가 설정할때 , 비선형방식
yearly_seasonality = True, # 연별
weekly_seasonality = True, # 주별
daily_seasonality = True, # 일별
changepoint_prior_scale = 0.5) # 과적합 방지 0.5만큼 만 분석
prophet.fit(bitcoin_df) # 학습하기
# 5일 앞을 예측하기
future_data = prophet.make_future_dataframe(periods=5, freq='d')
# 상한가 설정
future_data['cap'] = 20000
future_data['floor'] = 2000
# 예측
forecast_data = prophet.predict(future_data)
forecast_data
ds trend cap floor yhat_lower yhat_upper trend_lower trend_upper daily daily_lower ... weekly weekly_lower weekly_upper yearly yearly_lower yearly_upper additive_terms additive_terms_lower additive_terms_upper yhat
0 2017-08-27 5703.063125 20000 2000 3715.115421 5497.027367 5703.063125 5703.063125 0.426516 0.426516 ... 0.003555 0.003555 0.003555 -0.626876 -0.626876 -0.626876 0.0 0.0 0.0 4580.676685
1 2017-08-28 5708.250950 20000 2000 3665.713289 5396.548303 5708.250950 5708.250950 0.426516 0.426516 ... -0.000994 -0.000994 -0.000994 -0.642812 -0.642812 -0.642812 0.0 0.0 0.0 4467.904518
2 2017-08-29 5713.444155 20000 2000 3501.918583 5273.509727 5713.444155 5713.444155 0.426516 0.426516 ... -0.000734 -0.000734 -0.000734 -0.659989 -0.659989 -0.659989 0.0 0.0 0.0 4375.317434
3 2017-08-30 5718.642741 20000 2000 3377.490794 5126.427715 5718.642741 5718.642741 0.426516 0.426516 ... -0.010622 -0.010622 -0.010622 -0.678244 -0.678244 -0.678244 0.0 0.0 0.0 4218.353837
4 2017-08-31 5723.846707 20000 2000 3284.264456 5033.597019 5723.846707 5723.846707 0.426516 0.426516 ... -0.008040 -0.008040 -0.008040 -0.697376 -0.697376 -0.697376 0.0 0.0 0.0 4127.470590
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
365 2018-08-27 7102.808324 20000 2000 4866.755803 6547.711110 7102.808324 7102.808324 0.426516 0.426516 ... -0.000994 -0.000994 -0.000994 -0.623101 -0.623101 -0.623101 0.0 0.0 0.0 5699.443947
366 2018-08-28 7106.076367 20000 2000 4743.519630 6436.151223 7106.076367 7106.076367 0.426516 0.426516 ... -0.000734 -0.000734 -0.000734 -0.638706 -0.638706 -0.638706 0.0 0.0 0.0 5593.025888
367 2018-08-29 7109.345673 20000 2000 4533.463676 6332.987745 7109.336045 7109.345673 0.426516 0.426516 ... -0.010622 -0.010622 -0.010622 -0.655587 -0.655587 -0.655587 0.0 0.0 0.0 5405.285348
368 2018-08-30 7112.616243 20000 2000 4408.976493 6155.807154 7112.547092 7112.630877 0.426516 0.426516 ... -0.008040 -0.008040 -0.008040 -0.673590 -0.673590 -0.673590 0.0 0.0 0.0 5298.091380
369 2018-08-31 7115.888076 20000 2000 4405.810985 6115.617608 7115.744311 7115.953331 0.426516 0.426516 ... 0.000391 0.000391 0.000391 -0.692523 -0.692523 -0.692523 0.0 0.0 0.0 5225.796784
370 rows × 24 columns
# 그래프로 작성
fig = prophet.plot(forecast_data)
# 실제 데이터와 비교하기
# 예측데이터
pred_y = forecast_data.yhat.values[-5:]
pred_y
# array([5699.44394662, 5593.02588819, 5405.28534766, 5298.09137955,
# 5225.79678411])
# 실제데이터
test_file_path = 'market-price-test.csv'
bitcoin_test_df = pd.read_csv(test_file_path, names = ['ds', 'y'], header=0)
test_y = bitcoin_test_df.y.values
# 예측최소데이터
pred_y_lower = forecast_data.yhat_lower.values[-5:]
# 예측최대데이터
pred_y_upper = forecast_data.yhat_upper.values[-5:]
plt.plot(pred_y, color = 'gold') # 모델 예측한 가격그래프
plt.plot(pred_y_lower, color = 'red') # 모델이 예상한 최소 가격 그래프
plt.plot(pred_y_upper, color = 'blue') # 모델이 예상한 최대 가격 그래프
plt.plot(test_y, color = 'green') # 실제 가격 그래프
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