我想预测当 X(天)代表时间时变量 Y(c_start)的下一个值。 正如您在图片中看到的,我有属性“c_start”的值,并且我想预测接下来 7 天的下一个“c_start”值(例如)。 有人可以帮助我吗?
检查样本组中的 ARMA 模型:
import pandas as pd
from pandas.tseries.offsets import *
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
csv_file = '/home/Jian/Downloads/analyse vtr.csv'
df = pd.read_csv(csv_file, index_col=[0], sep='\t')
grouped = df.groupby('adserver_id')
group = list(grouped)[0][1]
ts_data = pd.TimeSeries(group.c_start.values, index=pd.to_datetime(group.day))
# positive-valued process, looks non-stationary
# simple way is to do a log transform
fig, axes = plt.subplots(figsize=(10,8), nrows=3)
ts_data.plot(ax=axes[0])
ts_log_data = np.log(ts_data)
ts_log_data.plot(ax=axes[1], style='b-', label='actual')
# in-sample fit
# ===================================
model = sm.tsa.ARMA(ts_log_data, order=(1,1)).fit()
print(model.params)
y_pred = model.predict(ts_log_data.index[0].isoformat(), ts_log_data.index[-1].isoformat())
y_pred.plot(ax=axes[1], style='r--', label='in-sample fit')
y_resid = model.resid
y_resid.plot(ax=axes[2])
# out-sample predict
# ===================================
start_date = ts_log_data.index[-1] + Day(1)
end_date = ts_log_data.index[-1] + Day(7)
y_forecast = model.predict(start_date.isoformat(), end_date.isoformat())
print(y_forecast)
2015-07-11 7.5526
2015-07-12 7.4584
2015-07-13 7.3830
2015-07-14 7.3224
2015-07-15 7.2739
2015-07-16 7.2349
2015-07-17 7.2037
Freq: D, dtype: float64
# NOTE: this step introduces bias, it is used here just for simplicity
# E[exp(x)] != exp[E[x]]
print(np.exp(y_forecast))
2015-07-11 1905.6328
2015-07-12 1734.4442
2015-07-13 1608.3362
2015-07-14 1513.8595
2015-07-15 1442.1183
2015-07-16 1387.0486
2015-07-17 1344.4080
Freq: D, dtype: float64
为每个子组运行 ARMA 模型(非常耗时):
import pandas as pd
from pandas.tseries.offsets import *
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
csv_file = '/home/Jian/Downloads/analyse vtr.csv'
df = pd.read_csv(csv_file, index_col=[0], sep='\t')
grouped = df.groupby('adserver_id')
def forecast_func(group):
ts_log_data = np.log(pd.TimeSeries(group.c_start.values, index=pd.to_datetime(group.day)))
# for some group, it raise convergence issue
try:
model = sm.tsa.ARMA(ts_log_data, order=(1,1)).fit()
start_date = ts_log_data.index[-1] + Day(1)
end_date = ts_log_data.index[-1] + Day(7)
y_forecast = model.predict(start_date.isoformat(), end_date.isoformat())
return pd.Series(np.exp(y_forecast).values, np.arange(1, 8))
except Exception:
pass
result = df.groupby('adserver_id').apply(forecast_func)
替代模型:为了快速计算,考虑指数平滑;另外,我发现数据看起来像一个具有时变 Possion 分布的正值过程,可以考虑使用
pymc
模块的状态空间模型。