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I have a trained and tested LSTM model which is meant to predict Ethereum close prices using all time csv data (24h steps). How do I now go about inputting an empty dataframe with future dates to recieve a plot of the future price trend.

Price data csv format example:

Currency,Date,Closing Price (USD),24h Open (USD),24h High (USD),24h Low (USD)

ETH,2015-08-09,0.909046,1.749289,1.91654,0.794497

ETH,2015-08-10,0.692321,0.909046,0.909046,0.692321

ETH,2015-08-11,0.668067,0.692321,0.692321,0.654331
...

code:

from keras.models import Sequential
from keras.layers import Activation, Dense, Dropout, LSTM
import matplotlib.pyplot as plt
import numpy as np
from numpy.core.numeric import NaN
import pandas as pd
import seaborn as sns
from sklearn.metrics import mean_absolute_error

def train_test_split(df, test_size=0.2):
    split_row = len(df) - int(test_size * len(df))
    train_data = df.iloc[:split_row]
    test_data = df.iloc[split_row:]
    return train_data, test_data

def line_plot(line1, line2, label1=None, label2=None, title='', lw=2):
    fig, ax = plt.subplots(1, figsize=(13, 7))
    ax.plot(line1, label=label1, linewidth=lw)
    ax.plot(line2, label=label2, linewidth=lw)
    ax.set_ylabel('Price [USD]', fontsize=14)
    ax.set_title(title, fontsize=16)
    ax.legend(loc='best', fontsize=16)

def normalise_zero_base(df):
    return df / df.iloc[0] - 1

def normalise_min_max(df):
    return (df - df.min()) / (data.max() - df.min())

def extract_window_data(df, window_len=5, zero_base=True):
    window_data = []
    for idx in range(len(df) - window_len):
        tmp = df[idx: (idx + window_len)].copy()
        if zero_base:
            tmp = normalise_zero_base(tmp)
        window_data.append(tmp.values)
    return np.array(window_data)

def prepare_data(df, target_col, window_len=10, zero_base=True, test_size=0.2):
    train_data, test_data = train_test_split(df, test_size=test_size)
    X_train = extract_window_data(train_data, window_len, zero_base)
    X_test = extract_window_data(test_data, window_len, zero_base)
    y_train = train_data[target_col][window_len:].values
    y_test = test_data[target_col][window_len:].values
    if zero_base:
        y_train = y_train / train_data[target_col][:-window_len].values - 1
        y_test = y_test / test_data[target_col][:-window_len].values - 1

    return train_data, test_data, X_train, X_test, y_train, y_test

def build_lstm_model(input_data, output_size, neurons=100, activ_func='linear',dropout=0.2, loss='mse', optimizer='adam'):
    model = Sequential()
    model.add(LSTM(neurons, input_shape=(input_data.shape[1], input_data.shape[2])))
    model.add(Dropout(dropout))
    model.add(Dense(units=output_size))
    model.add(Activation(activ_func))

    model.compile(loss=loss, optimizer=optimizer)
    return model

np.random.seed(42)
window_len = 5
test_size = 0.2
zero_base = True
lstm_neurons = 100
epochs = 20
batch_size = 32
loss = 'mse'
dropout = 0.2
optimizer = 'adam'

hist = pd.read_csv(r"C:\Users\tristan\Documents\ETH-USD\ETH_USD_2015-08-09_2021-01-18-CoinDesk.csv")
del hist['Currency']
hist = hist.set_index('Date')
hist.index = pd.to_datetime(hist.index)
hist = hist.reindex(columns=['Closing Price (USD)','24h High (USD)','24h Low (USD)','24h Open (USD)'])
target_col = 'Closing Price (USD)'
train, test, X_train, X_test, y_train, y_test = prepare_data(hist, target_col, window_len=window_len, zero_base=zero_base, test_size=test_size)
model = build_lstm_model(X_train, output_size=1, neurons=lstm_neurons, dropout=dropout, loss=loss,optimizer=optimizer)
history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=1, shuffle=True)
targets = test[target_col][window_len:]
preds = model.predict(X_test).squeeze()
print(mean_absolute_error(preds, y_test))
preds = test[target_col].values[:-window_len] * (preds + 1)
preds = pd.Series(index=targets.index, data=preds)
line_plot(targets, preds, 'actual', 'prediction', lw=3)
plt.show()

Thank you!

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