Trained model on NFLX stock data from 2014-2021. Predictions are scaled wrongly. about time-series-autoencoder HOT 10 CLOSED

parser = argparse.ArgumentParser()
parser.add_argument("--batch-size", default=config["batch_size"], type=int, help="batch size")
parser.add_argument("--output-size", default=config["output_size"], type=int,
                    help="size of the ouput: default value to 1 for forecasting")
parser.add_argument("--label-col", default=config["label_col"], type=str, help="name of the target column")
parser.add_argument("--input-att", default=config["input_att"], type=lambda x: (str(x).lower() == "true"),
                    help="whether or not activate the input attention mechanism")
parser.add_argument("--temporal-att", default=config["temporal_att"], type=lambda x: (str(x).lower() == "true"),
                    help="whether or not activate the temporal attention mechanism")
parser.add_argument("--seq-len", default=config["seq_len"], type=int, help="window length to use for forecasting")
parser.add_argument("--hidden-size-encoder", default=config["hidden_size_encoder"], type=int,
                    help="size of the encoder's hidden states")
parser.add_argument("--hidden-size-decoder", default=config["hidden_size_decoder"], type=int,
                    help="size of the decoder's hidden states")
parser.add_argument("--reg-factor1", default=config["reg_factor1"], type=float,
                    help="contribution factor of the L1 regularization if using a sparse autoencoder")
parser.add_argument("--reg-factor2", default=config["reg_factor2"], type=float,
                    help="contribution factor of the L2 regularization if using a sparse autoencoder")
parser.add_argument("--reg1", default=config["reg1"], type=lambda x: (str(x).lower() == "true"),
                    help="activate/deactivate L1 regularization")
parser.add_argument("--reg2", default=config["reg2"], type=lambda x: (str(x).lower() == "true"),
                    help="activate/deactivate L2 regularization")
parser.add_argument("--denoising", default=config["denoising"], type=lambda x: (str(x).lower() == "true"),
                    help="whether or not to use a denoising autoencoder")
parser.add_argument("--do-train", default=True, type=lambda x: (str(x).lower() == "true"),
                    help="whether or not to train the model")
parser.add_argument("--do-eval", default=True, type=lambda x: (str(x).lower() == "true"),
                    help="whether or not evaluating the mode")
parser.add_argument("--data-path", default='nflx.csv', help="path to data file")
parser.add_argument("--output-dir", default=config["output_dir"], help="name of folder to output files")
parser.add_argument("--ckpt", default=None, help="checkpoint path for evaluation")


df = pd.read_csv(config["data_path"])
df = df.set_index('Date_Time')

if not os.path.exists(config["output_dir"]):
    os.makedirs(config["output_dir"])

ts = TimeSeriesDataset(
    data=df,
    categorical_cols=config["categorical_cols"],
    target_col=config["label_col"],
    seq_length=config["seq_len"],
    prediction_window=config["prediction_window"]
)

device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),

categorical_cols=["day"],  # name of columns containing categorical variables
label_col=["Close"],  # name of target column
index_col="Date",

output_size=1,  # for forecasting

num_epochs=100,
batch_size=16,
lr=1e-5,
reg1=True,
reg2=False,
reg_factor1=1e-4,
reg_factor2=1e-4,
seq_len=10,  # previous timestamps to use
prediction_window=1,  # number of timestamps to forecast
hidden_size_encoder=128,
hidden_size_decoder=128,
input_att=True,
temporal_att=True,
denoising=False,
directions=1,

max_grad_norm=0.1,
gradient_accumulation_steps=1,
logging_steps=100,
lrs_step_size=5000,

output_dir="output",
save_steps=5000,
eval_during_training=True

self.preprocessor.named_transformers_["scaler"].inverse_transform(data)