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在R中使用RNN（Keras）的时间序列预测

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我正在使用R方法（fitting RNNs to time series data）跟踪Chollet的深度学习，以便拟合RNN进行时间序列预测 .

model <- keras_model_sequential() %>% 
  layer_gru(units = 32, 
            dropout = 0.1, 
            recurrent_dropout = 0.5,
            return_sequences = TRUE,
            input_shape = list(NULL, dim(data)[[-1]])) %>% 
  layer_gru(units = 64, activation = "relu",
            dropout = 0.1,
            recurrent_dropout = 0.5) %>% 
  layer_dense(units = 1)

model %>% compile(
  optimizer = optimizer_rmsprop(),
  loss = "mae"
)

history <- model %>% fit_generator(
  train_gen,
  steps_per_epoch = 500,
  epochs = 40,
  validation_data = val_gen,
  validation_steps = val_steps
)

在这里，使用以下方法生成训练，验证和测试数据：

lookback <- 1440
step <- 6
delay <- 144
batch_size <- 128

train_gen <- generator(
  data,
  lookback = lookback,
  delay = delay,
  min_index = 1,
  max_index = 200000,
  shuffle = TRUE,
  step = step, 
  batch_size = batch_size
)

val_gen = generator(
  data,
  lookback = lookback,
  delay = delay,
  min_index = 200001,
  max_index = 300000,
  step = step,
  batch_size = batch_size
)

test_gen <- generator(
  data,
  lookback = lookback,
  delay = delay,
  min_index = 300001,
  max_index = NULL,
  step = step,
  batch_size = batch_size
)

# How many steps to draw from val_gen in order to see the entire validation set
val_steps <- (300000 - 200001 - lookback) / batch_size

# How many steps to draw from test_gen in order to see the entire test set
test_steps <- (nrow(data) - 300001 - lookback) / batch_size

在此之后，我阅读了Keras文档并找到了预测功能 . 要查找测试数据的预测：

m <- model %>% evaluate_generator(test_gen, steps = test_steps)
m

但是，它仅给出测试数据的损失值 .

我的问题是，如何获得测试数据集中每个点的预测，就像我们可以获得其他时间序列方法一样？如何绘制这些预测值和实际值？

r keras lstm rnn

1 回答

  • 1

    在我看来，你需要重新定义 generator ，你需要只获得 samples 作为输出 . 按照你的例子：

    # generator function
generator <- function(data, lookback, delay, min_index, max_index,
                      shuffle = FALSE, batch_size = 128, step = 6) {
  if (is.null(max_index))
    max_index <- nrow(data) - delay - 1
  i <- min_index + lookback
  function() {
    if (shuffle) {
      rows <- sample(c((min_index+lookback):max_index), size = batch_size)
    } else {
      if (i + batch_size >= max_index)
        i <<- min_index + lookback
      rows <- c(i:min(i+batch_size-1, max_index))
      i <<- i + length(rows)
    }

    samples <- array(0, dim = c(length(rows), 
                                lookback / step,
                                dim(data)[[-1]]))
    targets <- array(0, dim = c(length(rows)))

    for (j in 1:length(rows)) {
      indices <- seq(rows[[j]] - lookback, rows[[j]]-1, 
                     length.out = dim(samples)[[2]])
      samples[j,,] <- data[indices,]
      targets[[j]] <- data[rows[[j]] + delay,2]
    }            

    list(samples) # just the samples, (quick and dirty solution, I just removed targets)
  }
}

# test_gen is the same
test_gen <- generator(
  data,
  lookback = lookback,
  delay = delay,
  min_index = 300001,
  max_index = NULL,
  step = step,
  batch_size = batch_size
)

    现在你可以拨打 predict_generator ：

    preds <- model %>% predict_generator(test_gen, steps = test_steps)

    但是现在你需要对这些变量进行去标准化，因为你在拟合之前缩放了每个变量 .

    denorm_pred = preds * std + mean

    请注意， std 和 mean 应仅在 T (degC) 数据上的 T (degC) 上进行计算，否则您将过度拟合 .

    回复于

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