机器算法验证 - LSTM NN 产生“偏移”预测（低质量结果） - 吾爱随笔录

我正在尝试了解循环神经计算的力量。我只给 NN 一个特征，一个过去一步的时间序列数据，并预测一个当前数据。然而，时间序列是双季节性的，具有相当长的 ACF 结构（约 64），加上滞后 6 的较短季节性。

输入时间序列：

验证结果：

你可以注意到它被转移了。我检查了我的向量，它们看起来还可以。

MSE 残差也很糟糕（由于添加了 sigma = 0.1 的高斯噪声，我预计两次火车验证的结果都是 0.01）：

> head(x_train)
[1]  0.9172955  0.9285578  0.4046166 -0.4144658 -0.3121450  0.3958689
> head(y_train)
           [,1]
[1,]  0.9285578
[2,]  0.4046166
[3,] -0.4144658
[4,] -0.3121450
[5,]  0.3958689
[6,]  1.5823631

问：我在 LSTM 架构、数据准备或批处理方面做错了吗？

library(keras)
library(data.table)

# constants

features <- 1
timesteps <- 1

x_diff <- sin(seq(0.1, 100, 0.1)) + sin(seq(1, 1000, 1)) + rnorm(1000, 0, 0.1)

#x_diff <- ((x_diff - min(x_diff)) / (max(x_diff) - min(x_diff)) - 0.5) * 2


# generate  training data

train_list <- list()
train_y_list <- list()

for(
     i in 1:(length(x_diff) / 2 - timesteps)
    )
{
     train_list[[i]] <- x_diff[i:(timesteps + i - 1)]
     train_y_list[[i]] <- x_diff[timesteps + i]
}

x_train <- unlist(train_list)
y_train <- unlist(train_y_list)

x_train <- array(x_train, dim = c(length(train_list), timesteps, features))
y_train <- matrix(y_train, ncol = 1)


# generate  validation data

val_list <- list()
val_y_list <- list()

for(
     i in (length(x_diff) / 2):(length(x_diff) - timesteps)
)
{
     val_list[[i - length(x_diff) / 2 + 1]] <- x_diff[i:(timesteps + i - 1)]
     val_y_list[[i - length(x_diff) / 2 + 1]] <- x_diff[timesteps + i]
}

x_val <- unlist(val_list)
y_val <- unlist(val_y_list)

x_val <- array(x_val, dim = c(length(val_list), timesteps, features))
y_val <- matrix(y_val, ncol = 1)


## lstm (stacked) ----------------------------------------------------------

# define and compile model
# expected input data shape: (batch_size, timesteps, features)

rm(fx_model)

fx_model <- 
     keras_model_sequential() %>% 
     layer_lstm(
          units = 32
          #, return_sequences = TRUE
          , input_shape = c(timesteps, features)
          ) %>% 
     #layer_lstm(units = 16, return_sequences = TRUE) %>% 
     #layer_lstm(units = 16) %>% # return a single vector dimension 16
     #layer_dropout(rate = 0.5) %>% 
     layer_dense(units = 4, activation = 'tanh') %>% 
     layer_dense(units = 1, activation = 'linear') %>% 
     compile(
          loss = 'mse',
          optimizer = 'RMSprop',
          metrics = c('mse')
     )


# train

# early_stopping <-
#      callback_early_stopping(
#           monitor = 'val_loss'
#           , patience = 10
#           )

history <- 
     fx_model %>% 
     fit( 
     x_train, y_train, batch_size = 50, epochs = 100, validation_data = list(x_val, y_val)
)

plot(history)

## plot predict

fx_predict <- data.table(
     forecast = as.numeric(predict(
          fx_model
          , x_val
     ))
     , fact = as.numeric(y_val[, 1])
     , timestep = 1:length(x_diff[(length(x_diff) / 2):(length(x_diff) - timesteps)])
)

fx_predict_melt <- melt(fx_predict
                        , id.vars = 'timestep'
                        , measure.vars = c('fact', 'forecast')
                        )

ggplot(
     fx_predict_melt[timestep < 301, ]
       , aes(x = timestep
             , y = value
             , group = variable
             , color = variable)
       ) +
     geom_line(
          alpha = 0.95
          , size = 1
     ) +
     ggplot_theme