scikit学习svr的时间序列预测

Question

我有日期温度数据集，按日期排序，我需要在scikit-learn中使用[SVR] [1]预测未来温度。

我坚持选择X和Y的培训和X测试 设置。例如，如果我想在时间t预测Y然后我需要 训练集包含了X & Y在t-1, t-2, ..., t-N其中N是用于在t预测Y以前的天数。

我该怎么做？

这是它。

df=daily_temp1 
# define function for create N lags 
def create_lags(df, N): 
    for i in range(N): 
     df['datetime' + str(i+1)] = df.datetime.shift(i+1) 
     df['dewpoint' + str(i+1)] = df.dewpoint.shift(i+1) 
     df['humidity' + str(i+1)] = df.humidity.shift(i+1) 
     df['pressure' + str(i+1)] = df.pressure.shift(i+1) 
     df['temperature' + str(i+1)] = df.temperature.shift(i+1) 
    df['vism' + str(i+1)] = df.vism.shift(i+1) 
    df['wind_direcd' + str(i+1)] = df.wind_direcd.shift(i+1) 
    df['wind_speed' + str(i+1)] = df.wind_speed.shift(i+1) 
    df['wind_direct' + str(i+1)] = df.wind_direct.shift(i+1) 

    return df 

# create 10 lags 
df = create_lags(df,10) 


# the first 10 days will have missing values. can't use them. 
df = df.dropna() 

# create X and y 
y = df['temperature'] 
X = df.iloc[:, 9:] 

# Train on 70% of the data 
train_idx = int(len(df) * .7) 

# create train and test data 
X_train, y_train, X_test, y_test = X[:train_idx], y[:train_idx], X[train_idx:], y[train_idx:] 


# fit and predict 
clf = SVR() 
clf.fit(X_train, y_train) 

clf.predict(X_test) 

Answer 1

下面是构建特征矩阵X作为LAG1简单的解决方案 - lagN其中LAG1是前几天的温度和lagN是温度N天前。

# create fake temperature 
df = pd.DataFrame({'temp':np.random.rand(500)}) 

# define function for create N lags 
def create_lags(df, N): 
    for i in range(N): 
     df['Lag' + str(i+1)] = df.temp.shift(i+1) 
    return df 

# create 10 lags 
df = create_lags(df,10) 

# the first 10 days will have missing values. can't use them. 
df = df.dropna() 

# create X and y 
y = df.temp.values 
X = df.iloc[:, 1:].values 

# Train on 70% of the data 
train_idx = int(len(df) * .7) 

# create train and test data 
X_train, y_train, X_test, y_test = X[:train_idx], y[:train_idx], X[train_idx:], y[:train_idx] 

# fit and predict 
clf = SVR() 
clf.fit(X_train, y_train) 

clf.predict(X_test)