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Created 2 years ago
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")df=pd.read_csv('bitcoinfinal+(4).csv', header=None)
df.columns=['Month','Price']
df=df.set_index('Month')
df.head()df.info()<class 'pandas.core.frame.DataFrame'>
Int64Index: 32 entries, 0 to 31
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Price 32 non-null float64
dtypes: float64(1)
memory usage: 512.0 bytes
train_len=29
train=df['Price'][:train_len]
test=df['Price'][train_len:]from statsmodels.tsa.holtwinters import SimpleExpSmoothing
model = SimpleExpSmoothing(train)
model_fit = model.fit(optimized=True)
print(model_fit.params)
y_hat_ses = test.copy()
y_hat_ses['ses_forecast'] = model_fit.forecast(3){'smoothing_level': 0.995, 'smoothing_trend': nan, 'smoothing_seasonal': nan, 'damping_trend': nan, 'initial_level': 217.4, 'initial_trend': nan, 'initial_seasons': array([], dtype=float64), 'use_boxcox': False, 'lamda': None, 'remove_bias': False}