在R中使用RNN（Keras）的時間序列預測

Question

我正在遵循Chollet的深度學習與R方法（ 擬合RNN到時間序列數據 ）來擬合RNN用於時間序列預測。

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
)

在這里，使用以下方法生成訓練，驗證和測試數據：

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，我閱讀了Keras文檔並找到了預測功能。 要查找測試數據的預測：

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

但是，它僅給出測試數據的損失值。

我的問題是，如何獲得測試數據集中每個點的預測，就像我們可以獲得其他時間序列方法一樣？ 如何繪制這些預測值和實際值？

Answer 1

在我看來，您需要重新定義generator ，您需要只獲取samples作為輸出。 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
)

現在你可以調用predict_generator ：

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

但是現在你需要對這些變量進行去標准化 ，因為你在擬合之前縮放了每個變量。

denorm_pred = preds * std + mean

小心std和mean應僅在train數據上計算T (degC) ，否則你就過度擬合了。