The LTSM does not predict Apple Stock Close column well

Quoted from Forex.com.bd-Editorial External question — Data Science Stack Exchange Author: Nisa Source score (net votes, not local likes): 1 Original post: https://datascience.stackexchange.com/questions/108151 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I am using LSTM (Long Short Term Memory) to predict the Apple Stock Closing prices using the 3 previous days. My problem is that the model underestimate closing prices. The graph of the final result is given at the end of this passage. First of all, I have a very highly left skewed data set of Apple stocks as can be seen from the photo below. I used a LSTM model to predict the closing prices using 3 previous days and a batch size of 10. rec_obj <- recipe(Close ~ .,aapl) %>% step_sqrt(Close) %>% step_center(Close) %>% step_scale(Close) %>% prep() aapl_normalized <- bake(rec_obj, aapl) #10372 observations. #keep centers for denormalization later center_history <- rec_obj$steps[[2]]$means["Close"] center_history scale_history <- rec_obj$steps[[3]]$sds["Close"] scale_history c("center" = center_history, "scale" = scale_history) Train validation and test datasets aapl_trn <- aapl_normalized[1:8500,] aapl_val <- aapl_normalized[8501:9401,] #900 aapl_test <- aapl_normalized[9402:10372 ,] #970 Reshaping the Data n_inputs <- 3 #number of inputs in the RNN e.g. 1st it. use first 10 days to predict the 11th n_predictions <- 1 batch_size <- 10 #number of batches that you give. large the model is faster -- parmeter Functions build_windowed_matrix <- function(data, timesteps) { #tranforms data into the windows of 4+1) if you have 14K rows this produce a matric of 14K x 5 t(sapply(1:(length(data) - timesteps + 1), function(x) data[x:(x + timesteps - 1)])) } reshape_3D <- function(df){ #to do it 14kx5x1 since this is required by keras. If it was multivariate (n) it should be 14kx5xn!!! dim(df) <- c(dim(df)[1], dim(df)[2], 1) df } get_x <- function(mtx, n_inputs, batch_size){#for each row gets the the x's (4 in number) mtx <- mtx[, 1:n_inputs] mtx <- mtx[1:(nrow(mtx) %/% batch_size * batch_size), ] reshape_3D(mtx) } get_y <- function(mtx, n_inputs, n_predictions, batch_size) {#for each row gets the the y (5th element) + put them in 3D mtx <- mtx[, (n_inputs+1):(n_inputs+n_predictions), drop=FALSE] mtx <- mtx[1:(nrow(mtx) %/% batch_size * batch_size), , drop=FALSE] if(n_predictions==1){ dim(mtx) <- c(length(mtx)[1], 1) } reshape_3D(mtx) } Extract 'Close' Values Extract close values and disregard dates trn <- aapl_trn %>% select(Close) %>% pull() #into vector val <- aapl_val %>% select(Close) %>% pull() test <- aapl_test %>% select(Close) %>% pull() Build matrices actually using the functions that I defined aboved trn_mtx <- build_windowed_matrix(trn, n_inputs + n_predictions) val_mtx <- build_windowed_matrix(val, n_inputs + n_predictions) test_mtx <- build_windowed_matrix(test, n_inputs + n_predictions) X_train <- get_x(trn_mtx, n_inputs, batch_size) #X_train_close Y_train <- get_y(trn_mtx, n_inputs, n_predictions, batch_size) X_val <- get_x(val_mtx, n_inputs, batch_size) Y_val <- get_y(val_mtx, n_inputs, n_predictions, batch_size) X_test <- get_x(test_mtx, n_inputs, batch_size) Y_test <- get_y(test_mtx, n_inputs, n_predictions, batch_size) #Y_test is the actual closing value in the test set. head(X_test) head(Y_test) I am using the LSTM model below: 1.) Build first model (use only close) model <- keras_model_sequential() model %>% layer_lstm( #lstm with 32 units in each cell units = 32, batch_input_shape = c(batch_size, n_inputs, 1) #1 feature is included ) layer_lstm( #lstm with 32 units in each cell units = 16, batch_input_shape = c(batch_size, n_inputs, 1) ) model %>% layer_dense(units = 1) model %>% compile( loss = 'mean_squared_error', optimizer = 'sgd', metrics = list("mean_squared_error") ) ''' ```{r} callbacks <- list(#stop criterion depends on if the network is not learning any more...stop the model from training after 5 epochs if there is no learning callback_early_stopping(patience = 5) ) history <- model %>% fit( x = X_train, y = Y_train, validation_data = list(X_val, Y_val), batch_size = batch_size, epochs = 100, callbacks = callbacks ) Predictions using one feature pred_test <- model %>% predict(X_test, batch_size = batch_size) # de-normalize to original scale pred_test <- (pred_test * scale_history + center_history) ^2 #denormalization mse_test <- (pred_test - Y_test[,,1]) ^2 #Y_test is the actual closing value in the test set. Plot predictions vs actual ggplot(aapl[(9402 + n_inputs):(9401 + n_inputs + dim(pred_test)[1]),], aes(x = Date, y = Close, group = 1)) + geom_line() + scale_x_discrete(breaks = levels(aapl$Date)[floor(seq(1, nlevels(aapl$Date),length.out = 5))]) + geom_line(aes(y = pred_test), color = "blue") + labs(x = "Date", y = "Close Value", title = "Apple Stock") As you see from the graph, my model does seem to underestimate the values. How can I fix this? I tried hyperparameter tuning but it didn't work. Is it because my data was super left skewed? How do I go about this? Best regards

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