import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns
plt.rcParams.update({'font.size': 20}) In [2]:
weather_data = pd.read_csv("../data/3/Summary of Weather.csv") c:\users\skd621\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3020: DtypeWarning: Columns (7,8,18,25) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result) In [3]:
weather_data = weather_data.loc[:,["STA","Date","MeanTemp"]] In [4]:
weather_data.head() Out[4]:
STA Date MeanTemp 0 10001 1942-7-1 23.888889 1 10001 1942-7-2 25.555556 2 10001 1942-7-3 24.444444 3 10001 1942-7-4 24.444444 4 10001 1942-7-5 24.444444 In [5]:
weather_data.info()
In [6]:
weather_palmyra = weather_data[weather_data.STA == 33023] weather_palmyra['Date'] = pd.to_datetime(weather_palmyra['Date']) plt.figure(figsize=(16,10)) plt.plot(weather_palmyra.Date,weather_palmyra.MeanTemp) plt.title("Mean Temperature of MAISON BLANCHE") plt.xlabel("Date") plt.ylabel("Mean Temperature") plt.show() c:\users\skd621\anaconda3\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [7]:
timeSeries = weather_palmyra.loc[:, ["Date","MeanTemp"]] timeSeries.index = timeSeries.Date timeSeries = timeSeries.drop("Date",axis=1) In [21]:
rolmean = timeSeries.rolling(6).mean() rolstd = timeSeries.rolling(6).std() plt.figure(figsize=(22,10)) orig = plt.plot(timeSeries, 'r-',label='Original') mean = plt.plot(rolmean, 'b', label='Rolling Mean',marker='+', markersize=12) std = plt.plot(rolstd, 'g--', label = 'Rolling Std') plt.xlabel("Date") plt.ylabel("Mean Temperature") plt.title('Rolling Mean & Standard Deviation') plt.legend() plt.show()
In [22]:
from statsmodels.tsa.stattools import adfuller # res = adfuller(timeSeries.MeanTemp) res = adfuller(timeSeries.MeanTemp, autolag='AIC') print('Test statistic: %.4f; p-value: %.4f'%(res[0], res[1])) print("Critical Values: ",res[4]) Test statistic: -1.9031; p-value: 0.3306 Critical Values: {'1%': -3.4369994990319355, '5%': -2.8644757356011743, '10%': -2.5683331327427803} c:\users\skd621\anaconda3\lib\site-packages\statsmodels\compat\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead. from pandas.core import datetools In [34]:
def check_DF(timeSeries): res = adfuller(timeSeries.MeanTemp, autolag='AIC') print('Test statistic:%.4f;p-value: %.4f'%(res[0],res[1])) print("Critical Values: ",res[4]) def check_mean_std(timeSeries): rolmean = timeSeries.rolling(6).mean() rolstd = timeSeries.rolling(6).std() plt.figure(figsize=(22,10)) orig = plt.plot(timeSeries, 'r-',label='Original') mean = plt.plot(rolmean, 'b', label='Rolling Mean',marker='+', markersize=10) std = plt.plot(rolstd, 'g', label = 'Rolling Std',marker='o', markersize=3) plt.xlabel("Date") plt.ylabel("Mean Temperature") plt.title('Rolling Mean & Standard Deviation') plt.legend() plt.show() In [25]:
timeSeries_diff = timeSeries - timeSeries.shift(periods=1) In [26]:
plt.figure(figsize=(16,12)) plt.plot(timeSeries_diff) plt.title("Differencing method") plt.xlabel("Date") plt.ylabel("Differencing Mean Temperature") plt.show()
In [27]:
timeSeries_diff.dropna(inplace=True) In [28]:
check_DF(timeSeries_diff) Test statistic:-15.4648;p-value: 0.0000 Critical Values: {'1%': -3.4369994990319355, '5%': -2.8644757356011743, '10%': -2.5683331327427803} In [35]:
check_mean_std(timeSeries_diff)
In [38]:
timeSeries_moving_avg = timeSeries.rolling(5).mean() In [47]:
plt.figure(figsize=(16,12)) plt.plot(timeSeries, "r:", label = "Original") plt.plot(timeSeries_moving_avg, color='b', label = "moving_avg_mean") plt.title("Mean Temperature of Maison Blanche") plt.xlabel("Date") plt.ylabel("Mean Temperature") plt.legend() plt.show()
In [48]:
timeSeries_moving_avg_diff = timeSeries - timeSeries_moving_avg In [49]:
timeSeries_moving_avg_diff.dropna(inplace=True) In [50]:
check_DF(timeSeries_moving_avg_diff) Test statistic:-15.6940;p-value: 0.0000 Critical Values: {'1%': -3.4370062675076807, '5%': -2.8644787205542492, '10%': -2.568334722615888} In [51]:
check_mean_std(timeSeries_moving_avg_diff)
In [23]:
from statsmodels.tsa.stattools import acf, pacf _acf = acf(timeSeries_diff, nlags=20) _pacf = pacf(timeSeries_diff, nlags=20, method='ols') plt.figure(figsize=(22,10))
len_ts = len(timeSeries_diff) plt.subplot(121) plt.plot(_acf) plt.axhline(y=0,ls='--',color='gray') plt.axhline(y=-1.96/np.sqrt(len_ts),ls='--',color='gray') plt.axhline(y=1.96/np.sqrt(len_ts),ls='--',color='gray') plt.title('ACF')
plt.subplot(122) plt.plot(_pacf) plt.axhline(y=0,ls='--',color='gray') plt.axhline(y=-1.96/np.sqrt(len_ts),ls='--',color='gray') plt.axhline(y=1.96/np.sqrt(len_ts),ls='--',color='gray') plt.title('PACF') plt.tight_layout()
In [24]:
from statsmodels.tsa.arima_model import ARIMA from pandas import datetime
model = ARIMA(timeSeries, order=(1,0,1)) # (ARMA) = (1,0,1) model_fit = model.fit(disp=0)
# 预测 forecast = model_fit.predict()
# 可视化 plt.figure(figsize=(22,10)) plt.plot(weather_palmyra.Date,weather_palmyra.MeanTemp,label = "original") plt.plot(forecast,label = "predicted") plt.title("Time Series Forecast") plt.xlabel("Date") plt.ylabel("Mean Temperature") plt.legend() plt.show() c:\users\skd621\anaconda3\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:646: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. if issubdtype(paramsdtype, float): c:\users\skd621\anaconda3\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:650: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`. elif issubdtype(paramsdtype, complex): c:\users\skd621\anaconda3\lib\site-packages\statsmodels\tsa\kalmanf\kalmanfilter.py:577: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. if issubdtype(paramsdtype, float):
In [25]:
train_data, test_data = timeSeries[:-5], timeSeries[-5:] In [26]:
model = ARIMA(train_data, order=(1,0,1)) In [27]:
model_fit = model.fit(disp=0) output = model_fit.forecast() In [32]:
data = timeSeries.MeanTemp.values.tolist() train_data, test_data = data[:-5], data[-5:] for t in range(len(test_data)): model = ARIMA(train_data, order=(1,0,1)) model_fit = model.fit(disp=0) output = model_fit.forecast() yhat = output[0] obs = test_data[t] train_data.append(obs) print('predicted=%f, expected=%f' % (yhat, obs)) predicted=14.961205, expected=14.444444 predicted=14.665928, expected=14.444444 predicted=14.616089, expected=13.888889 predicted=14.164753, expected=11.111111 predicted=11.872587, expected=9.444444
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