Hi,

I try to make predictions with keras models but face an issue when I use fit. My goal is to get 30 next minutes prediction on BNB/USDT stock

The error I get is

```
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Incompatible shapes: [32,30] vs. [32,30,1]
[[{{node loss/dense_loss/SquaredDifference}}]]
[[training/Adam/gradients/gradients/lstm_1/while/ReadVariableOp/Enter_grad/b_acc_3/_125]]
(1) Invalid argument: Incompatible shapes: [32,30] vs. [32,30,1]
[[{{node loss/dense_loss/SquaredDifference}}]]
```

Here’s the code

```
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
from binance.client import Client
import csv
import tensorflow as tf
pd.options.mode.chained_assignment = None
tf.random.set_random_seed(0)
api = {'key':'...','secret':'...'}
# client = Client(api['key'], api['secret'])
# length_data = "2 day"
# klines = client.get_historical_klines("BNBUSDT", Client.KLINE_INTERVAL_1MINUTE, length_data + " UTC")
# with open('./bnbusdt_price_train_test.csv', 'w') as f:
# writer = csv.writer(f)
# writer.writerow(['timestamp','open','max','min','close'])
# for sub in klines:
# writer.writerow([sub[0], sub[1], sub[2], sub[3], sub[4]])
df = pd.read_csv('./bnbusdt_price_train_test.csv')
df['Date'] = pd.to_datetime(df.timestamp, unit='ms')
df.sort_values('Date')
y = df['close'].fillna(method='ffill')
y = y.values.reshape(-1, 1)
scaler = MinMaxScaler(feature_range=(0, 1))
scaler = scaler.fit(y)
y = scaler.transform(y)
n_lookback = 60
n_forecast = 30
X = []
Y = []
for i in range(n_lookback, len(y) - n_forecast + 1):
X.append(y[i - n_lookback: i])
Y.append(y[i: i + n_forecast])
X = np.array(X)
Y = np.array(Y)
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(n_lookback, 1)))
model.add(LSTM(units=50))
model.add(Dense(n_forecast))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X, Y, epochs=1, batch_size=32, verbose=2)
```

The CSV I load up contains :

- timestamp (ms)
- open price
- max price
- min price
- close price

I tried to changed my 3d inputs to 2d but got another error on model.add

Do you have any idea ?