#按半小时进行重采样 a = data.index.time by_time = data.groupby(data.index.time).mean() hourly_ticks = 4 * 60 * 60 * np.arange(6) by_time.plot(xticks=hourly_ticks, style=[':', '--', '-']); data.rolling(360).mean().plot(figsize=(20,10), linewidth=5, fontsize=20) plt...
从这一篇开始就进入深度学习领域中的时序模型介绍了,主要集中在RNN架构、Transformer架构以及之前有介绍过的AR-net神经网络,本篇主要介绍LSTM的模型架构及实际python应用,包括在实际建模过程中可以用到的建模技巧。 1、模型原理 长短期记忆(Long short-term memory,简称:LSTM)模型是循环神经网络(RNN)的一个子类型,由 ...
1#预测:2fut_pre = 1234test_inputs = train_data_normalized[-train_window:].tolist()5print(test_inputs)67model.eval()8foriinrange(fut_pre):9seq = torch.FloatTensor(test_inputs[-train_window:])10with torch.no_grad():11model.hidden = (torch.zeros(1,1,model.hidden_layer_size),12to...
cols.append(df.shift(i)) names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)] #预测数据(input对应的输出值) forecast sequence (t, t+1, ... t+n) for i in range(0, n_out): cols.append(df.shift(-i)) if i == 0: names += [('var%d(t)' % (j +...
(-1,time_step,input_size)y_train=torch.from_numpy(y_train).float()# 训练模型num_epochs=100forepochinrange(num_epochs):model.train()optimizer.zero_grad()outputs=model(X_train)loss=criterion(outputs,y_train.view(-1,1))loss.backward()optimizer.step()if(epoch+1)%10==0:print(f'Epoch ...
[]foriinrange(len(data)-time_step):X.append(data[i:(i+time_step),:])y.append(data[i+time_step,0])# 假设预测目标是第一列returnnp.array(X),np.array(y)time_step=10# 设置时间步长X,y=create_dataset(scaled_data,time_step)X=X.reshape(X.shape[0],X.shape[1],X.shape[2])# ...
date_range=pd.date_range(start='1990-01-01',end='2023-09-30',freq='M')# Add empty valuesinfits to match the original time series fits=np.full(train_size,np.nan)foriinrange(train_size-look_back):fits[i+look_back]=lstm_fits[i]# Plot actual,fits,and forecasts ...
classtime_series_decoder_paper(Dataset):"""synthetic time series dataset from section 5.1"""def__init__(self,t0=120,N=4500,dx=None,dy=None,transform=None):"""Args:t0:previous t0 data points to predictfromN:numberofdata pointstransform:any transformations to be applied to time series"""...
为了在一年中的某个时间创建相同的循环逻辑，我们将使用时间戳功能。 python中的时间戳是一个值，用于计算自1970.01.01 0H：0m：0s以来经过了多少秒。 python中的每个date对象都具有timestamp（）函数。# Extracting the timestamp from the datetime object d["timestamp"] = [x.timestamp() for x in d[...
("Results of dickey fuller test") adft = adfuller(timeseries,autolag='AIC') output = pd.Series(adft[0:4],index=['Test Statistics','p-value','No. of lags used','Number of observations used']) for key,values in adft[4].items(): output['critical value (%s)'%key] = values ...