通过读取CSV 数据 ，使用LSTM 模型训练已知数据，预测
csv 数据格式
Date,Open,High,Low,Close,Volume,Bitcoin,BTC,Blockchain,Cryptocurrency,Iota 1/3/2016 0:58,436.75,437.5,435.2,437.4,218.731,90,65,57,62,70 1/3/2016 1:56,437.07,437.07,434.2,436.34,78.762,92,74,48,48,71 1/3/2016 2:59,436.46,436.98,434.25,436.05,104.564,90,76,55,55,65 1/3/2016 3:58,435.53,436.44,429.0,434.22,409.744,92,84,62,68,81 1/3/2016 4:58,434.22,435.71,431.3,434.41,89.213,90,80,63,58,71 1/3/2016 5:59,434.11,434.11,431.37,433.6,54.86,100,86,71,77,57 1/3/2016 6:59,432.06,435.79,432.06,434.18,123.17399999999999,83,88,70,61,43
python 代码：
import pandas as pd
from pandas import DataFrame
from pandas import concat
from math import sqrt
from numpy import concatenate
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as pyplot
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras import Sequential
from keras.layers import LSTM, Dense, Dropout, Activation
from pandas import read_csv
# Load dataset by using Pandas library
dataset = read_csv('Bitcoin_HourlyData.csv', header=0, index_col=0)
print(dataset.head())
values = dataset.values
# Here was prepared column for visualizing
groups = [0, 1, 2, 3, 5, 6, 7, 8, 9]
i = 1
# plot each column
pyplot.figure()
for group in groups:
pyplot.subplot(len(groups), 1, i)
pyplot.plot(values[:, group])
pyplot.title(dataset.columns[group], y=0.5, loc='right')
i += 1
pyplot.show()
# convert series to supervised learning
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
n_vars = 1 if type(data) is list else data.shape[1]
df = DataFrame(data)
cols, names = list(), list()
# Here is created input columns which are (t-n, ... t-1)
for i in range(n_in, 0, -1):
cols.append(df.shift(i))
names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
# Here is created output/forecast column which are (t, t+1, ... t+n)
for i in range(0, n_out):
cols.append(df.shift(-i))
if i == 0:
names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
else:
names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
# put it all together
agg = concat(cols, axis=1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg
# here checked values numeric format
values = values.astype('float32')
print(values)
# Dataset values are normalized by using MinMax method
scaler = MinMaxScaler(feature_range=(0,1))
scaled = scaler.fit_transform(values)
#print(scaled)
# Normalized values are converted for supervised learning
reframed = series_to_supervised(scaled,1,1)
#reframed.drop(reframed.columns[[9,10,11,12,13,14,15]], axis=1, inplace=True)
print(reframed.head())
# Dataset is splitted into two groups which are train and test sets
values = reframed.values
train_size = int(len(values)*0.70)
train = values[:train_size,:]
test = values[train_size:,:]
# Splitted datasets are splitted to trainX, trainY, testX and testY
trainX, trainY = train[:,:-1], train[:,13]
testX, testY = test[:,:-1], test[:,13]
print(trainY, trainY.shape)
# Train and Test datasets are reshaped in 3D size to be used in LSTM
trainX = trainX.reshape((trainX.shape[0],1,trainX.shape[1]))
testX = testX.reshape((testX.shape[0],1,testX.shape[1]))
print(trainX.shape, trainY.shape,testX.shape,testY.shape)
# LSTM model is created and adjusted neuron structure
model = Sequential()
model.add(LSTM(128, input_shape=(trainX.shape[1], trainX.shape[2])))
model.add(Dropout(0.05))
model.add(Dense(1))
model.add(Activation('linear'))
model.compile(loss='mae', optimizer='adam')
# Dataset is trained by using trainX and trainY
history = model.fit(trainX, trainY, epochs=10, batch_size=25, validation_data=(testX, testY), verbose=2, shuffle=False)
# Loss values are calculated for every training epoch and are visualized
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.title("Test and Train set Loss Value Rate")
pyplot.xlabel('Epochs Number', fontsize=12)
pyplot.ylabel('Loss Value', fontsize=12)
pyplot.legend()
pyplot.show()
# Prediction process is performed for train dataset
trainPredict = model.predict(trainX)
trainX = trainX.reshape((trainX.shape[0], trainX.shape[2]))
print(trainX.shape)
# Prediction process is performed for test dataset
testPredict = model.predict(testX)
testX = testX.reshape((testX.shape[0], testX.shape[2]))
print(testX.shape)
# Trains dataset inverts scaling for training
trainPredict = concatenate((trainPredict, trainX[:, -9:]), axis=1)
trainPredict = scaler.inverse_transform(trainPredict)
trainPredict = trainPredict[:,0]
print(trainPredict)
print(len(trainPredict))
# Test dataset inverts scaling for forecasting
testPredict = concatenate((testPredict, testX[:, -9:]), axis=1)
testPredict = scaler.inverse_transform(testPredict)
testPredict = testPredict[:,0]
# invert scaling for actual
testY = testY.reshape((len(testY), 1))
inv_y = concatenate((testY, testX[:, -9:]), axis=1)
inv_y = scaler.inverse_transform(inv_y)
inv_y = inv_y[:,0]
#print('actual: ', len(inv_y))
# Performance measure calculated by using mean_squared_error for train and test prediction
rmse2 = sqrt(mean_squared_error(trainY, trainPredict))
print('Train RMSE: %.3f' % rmse2)
rmse = sqrt(mean_squared_error(inv_y, testPredict))
print('Test RMSE: %.3f' % rmse)
#print(testPredict)
#print(type(trainPredict))
# train and test prediction are concatenated
final = np.append(trainPredict, testPredict)
#print(len(son))
final = pd.DataFrame(data=final, columns=['Close'])
actual = dataset.Close
actual = actual.values
actual = pd.DataFrame(data=actual, columns=['Close'])
# Finally training and prediction result are visualized
pyplot.plot(actual.Close, 'b', label='Original Set')
pyplot.plot(final.Close[0:16781], 'r' , label='Training set')
pyplot.plot(final.Close[16781:len(final)], 'g' , label='Predicted/Test set')
pyplot.title("Hourly Bitcoin Predicted Prices")
pyplot.xlabel('Hourly Time', fontsize=12)
pyplot.ylabel('Close Price', fontsize=12)
pyplot.legend(loc='best')
pyplot.show()
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