{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook will try to predict BTC price through:\n",
"- RNN: Recurrent Neural Network\n",
"- LSTM: Long Short Term Memory\n",
"- ES: Early Stopping\n",
"- Single-Step and Multi-Step time series forecasting\n",
"- Univariate (only input Price) and Multivariate (inputs Price and Volume)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://nbviewer.jupyter.org/github/sjuanandres0/crypto/blob/main/Main.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parameter settings"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# number of total past observations from the original dataset to be considered\n",
"n_past_total = 1200\n",
"\n",
"# number of past observations to be considered for the LSTM training and prediction\n",
"n_past = 30\n",
"\n",
"# number of future datapoints to predict (if higher than 1, the model switch to Multi-Step)\n",
"n_future = 10\n",
"\n",
"# activation function used for the RNN (softsign, relu, sigmoid)\n",
"activation = 'softsign'\n",
"\n",
"# dropout for the hidden layers\n",
"dropout = 0.2\n",
"\n",
"# number of hidden layers\n",
"n_layers = 8\n",
"\n",
"# number of neurons of the hidden layers\n",
"n_neurons = 20\n",
"\n",
"# features to be considered for training (if only one is Close, then its Univariate, if more, then it's Multivariate)\n",
"features = ['Close', 'Volume']\n",
"#features = ['Close']\n",
"\n",
"# number of inputs features (if higher than 1, )\n",
"n_features = len(features)\n",
"\n",
"# patience for the early stopping (number of epochs)\n",
"patience = 25\n",
"\n",
"# optimizer (adam, RMSprop)\n",
"optimizer='adam'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing libraries"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"np.set_printoptions(suppress=True)\n",
"import pandas as pd\n",
"pd.set_option('display.float_format', lambda x: '%.3f' % x) #avoid scientific notation\n",
"import datetime\n",
"import math\n",
"from matplotlib import pyplot as plt\n",
"from keras.models import Sequential, load_model\n",
"from keras.layers import Dense, LSTM, Dropout\n",
"from keras.callbacks import EarlyStopping\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"from plotly.subplots import make_subplots\n",
"from IPython.display import Image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing the files"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" Date \n",
" Open \n",
" High \n",
" Low \n",
" Close \n",
" Adj Close \n",
" Volume \n",
" \n",
" \n",
" \n",
" \n",
" 0 \n",
" 2014-09-17 \n",
" 465.864 \n",
" 468.174 \n",
" 452.422 \n",
" 457.334 \n",
" 457.334 \n",
" 21056800.000 \n",
" \n",
" \n",
" 1 \n",
" 2014-09-18 \n",
" 456.860 \n",
" 456.860 \n",
" 413.104 \n",
" 424.440 \n",
" 424.440 \n",
" 34483200.000 \n",
" \n",
" \n",
" 2 \n",
" 2014-09-19 \n",
" 424.103 \n",
" 427.835 \n",
" 384.532 \n",
" 394.796 \n",
" 394.796 \n",
" 37919700.000 \n",
" \n",
" \n",
" 3 \n",
" 2014-09-20 \n",
" 394.673 \n",
" 423.296 \n",
" 389.883 \n",
" 408.904 \n",
" 408.904 \n",
" 36863600.000 \n",
" \n",
" \n",
" 4 \n",
" 2014-09-21 \n",
" 408.085 \n",
" 412.426 \n",
" 393.181 \n",
" 398.821 \n",
" 398.821 \n",
" 26580100.000 \n",
" \n",
" \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" ... \n",
" \n",
" \n",
" 2454 \n",
" 2021-06-06 \n",
" 35538.609 \n",
" 36436.422 \n",
" 35304.578 \n",
" 35862.379 \n",
" 35862.379 \n",
" 28913440585.000 \n",
" \n",
" \n",
" 2455 \n",
" 2021-06-07 \n",
" 35835.266 \n",
" 36790.570 \n",
" 33480.641 \n",
" 33560.707 \n",
" 33560.707 \n",
" 33683936663.000 \n",
" \n",
" \n",
" 2456 \n",
" 2021-06-08 \n",
" 33589.520 \n",
" 34017.387 \n",
" 31114.443 \n",
" 33472.633 \n",
" 33472.633 \n",
" 49902050442.000 \n",
" \n",
" \n",
" 2457 \n",
" 2021-06-09 \n",
" 33416.977 \n",
" 37537.371 \n",
" 32475.865 \n",
" 37345.121 \n",
" 37345.121 \n",
" 53972919008.000 \n",
" \n",
" \n",
" 2458 \n",
" 2021-06-10 \n",
" 37513.863 \n",
" 37513.863 \n",
" 36615.184 \n",
" 36625.629 \n",
" 36625.629 \n",
" 52061741056.000 \n",
" \n",
" \n",
"
\n",
"
2459 rows × 7 columns
\n",
"
"
],
"text/plain": [
" Date Open High Low Close Adj Close \\\n",
"0 2014-09-17 465.864 468.174 452.422 457.334 457.334 \n",
"1 2014-09-18 456.860 456.860 413.104 424.440 424.440 \n",
"2 2014-09-19 424.103 427.835 384.532 394.796 394.796 \n",
"3 2014-09-20 394.673 423.296 389.883 408.904 408.904 \n",
"4 2014-09-21 408.085 412.426 393.181 398.821 398.821 \n",
"... ... ... ... ... ... ... \n",
"2454 2021-06-06 35538.609 36436.422 35304.578 35862.379 35862.379 \n",
"2455 2021-06-07 35835.266 36790.570 33480.641 33560.707 33560.707 \n",
"2456 2021-06-08 33589.520 34017.387 31114.443 33472.633 33472.633 \n",
"2457 2021-06-09 33416.977 37537.371 32475.865 37345.121 37345.121 \n",
"2458 2021-06-10 37513.863 37513.863 36615.184 36625.629 36625.629 \n",
"\n",
" Volume \n",
"0 21056800.000 \n",
"1 34483200.000 \n",
"2 37919700.000 \n",
"3 36863600.000 \n",
"4 26580100.000 \n",
"... ... \n",
"2454 28913440585.000 \n",
"2455 33683936663.000 \n",
"2456 49902050442.000 \n",
"2457 53972919008.000 \n",
"2458 52061741056.000 \n",
"\n",
"[2459 rows x 7 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# reading csv\n",
"dataset = pd.read_csv('data/yahoo_BTC-USD.csv')\n",
"dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EDA (brief)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 2459 entries, 0 to 2458\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Date 2459 non-null object \n",
" 1 Open 2455 non-null float64\n",
" 2 High 2455 non-null float64\n",
" 3 Low 2455 non-null float64\n",
" 4 Close 2455 non-null float64\n",
" 5 Adj Close 2455 non-null float64\n",
" 6 Volume 2455 non-null float64\n",
"dtypes: float64(6), object(1)\n",
"memory usage: 134.6+ KB\n"
]
}
],
"source": [
"# checking for nulls\n",
"dataset.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" Date \n",
" Open \n",
" High \n",
" Low \n",
" Close \n",
" Adj Close \n",
" Volume \n",
" \n",
" \n",
" \n",
" \n",
" 2039 \n",
" 2020-04-17 \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" \n",
" \n",
" 2214 \n",
" 2020-10-09 \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" \n",
" \n",
" 2217 \n",
" 2020-10-12 \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" \n",
" \n",
" 2218 \n",
" 2020-10-13 \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" nan \n",
" \n",
" \n",
"
\n",
"
"
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"text/plain": [
" Date Open High Low Close Adj Close Volume\n",
"2039 2020-04-17 nan nan nan nan nan nan\n",
"2214 2020-10-09 nan nan nan nan nan nan\n",
"2217 2020-10-12 nan nan nan nan nan nan\n",
"2218 2020-10-13 nan nan nan nan nan nan"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# checking if close is not equal to adj close\n",
"dataset[dataset['Close']!=dataset['Adj Close']]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" Open \n",
" High \n",
" Low \n",
" Close \n",
" Adj Close \n",
" Volume \n",
" \n",
" \n",
" \n",
" \n",
" count \n",
" 2455.000 \n",
" 2455.000 \n",
" 2455.000 \n",
" 2455.000 \n",
" 2455.000 \n",
" 2455.000 \n",
" \n",
" \n",
" mean \n",
" 7682.998 \n",
" 7903.197 \n",
" 7440.796 \n",
" 7696.541 \n",
" 7696.541 \n",
" 12680146147.817 \n",
" \n",
" \n",
" std \n",
" 11719.942 \n",
" 12089.894 \n",
" 11283.752 \n",
" 11731.193 \n",
" 11731.193 \n",
" 19763751064.923 \n",
" \n",
" \n",
" min \n",
" 176.897 \n",
" 211.731 \n",
" 171.510 \n",
" 178.103 \n",
" 178.103 \n",
" 5914570.000 \n",
" \n",
" \n",
" 25% \n",
" 461.941 \n",
" 467.522 \n",
" 455.721 \n",
" 461.874 \n",
" 461.874 \n",
" 68840948.000 \n",
" \n",
" \n",
" 50% \n",
" 4332.820 \n",
" 4413.090 \n",
" 4160.860 \n",
" 4338.710 \n",
" 4338.710 \n",
" 4047850000.000 \n",
" \n",
" \n",
" 75% \n",
" 9219.190 \n",
" 9371.618 \n",
" 9041.198 \n",
" 9231.573 \n",
" 9231.573 \n",
" 18823342711.500 \n",
" \n",
" \n",
" max \n",
" 63523.754 \n",
" 64863.098 \n",
" 62208.965 \n",
" 63503.457 \n",
" 63503.457 \n",
" 350967941479.000 \n",
" \n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Open High Low Close Adj Close Volume\n",
"count 2455.000 2455.000 2455.000 2455.000 2455.000 2455.000\n",
"mean 7682.998 7903.197 7440.796 7696.541 7696.541 12680146147.817\n",
"std 11719.942 12089.894 11283.752 11731.193 11731.193 19763751064.923\n",
"min 176.897 211.731 171.510 178.103 178.103 5914570.000\n",
"25% 461.941 467.522 455.721 461.874 461.874 68840948.000\n",
"50% 4332.820 4413.090 4160.860 4338.710 4338.710 4047850000.000\n",
"75% 9219.190 9371.618 9041.198 9231.573 9231.573 18823342711.500\n",
"max 63523.754 64863.098 62208.965 63503.457 63503.457 350967941479.000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# checking the main parameters\n",
"dataset.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# use close only and fill NaN with ffil\n",
"df = dataset.set_index('Date')[features]#.tail(n_past_total)\n",
"df = df.set_index(pd.to_datetime(df.index))\n",
"df.fillna(method='ffill',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" Close \n",
" Volume \n",
" \n",
" \n",
" \n",
" \n",
" Close \n",
" 1.000 \n",
" 0.799 \n",
" \n",
" \n",
" Volume \n",
" 0.799 \n",
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" \n",
" \n",
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\n",
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"
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"text/plain": [
" Close Volume\n",
"Close 1.000 0.799\n",
"Volume 0.799 1.000"
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"execution_count": 8,
"metadata": {},
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],
"source": [
"# looking at the correlation of the main possible variables\n",
"dataset[['Close','Volume']].corr()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
" \n",
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"metadata": {},
"output_type": "display_data"
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"source": [
"# plotting Closing Price and Volume\n",
"fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
"\n",
"# Add traces\n",
"fig.add_trace(go.Scatter(x=dataset['Close'].index, y=dataset['Close'].values, name='Close'), secondary_y=False)\n",
"\n",
"fig.add_trace(go.Scatter(x=dataset['Volume'].index, y=dataset['Volume'].values, name='Volume'), secondary_y=True)\n",
"\n",
"# Add figure title\n",
"fig.update_layout(title_text=\"BTC: {}, {}\".format('Close', 'Volume'))\n",
"\n",
"# Set x-axis title\n",
"fig.update_xaxes(title_text='Date ')\n",
"\n",
"# Set y-axes titles\n",
"fig.update_yaxes(title_text='Close ', secondary_y=False)\n",
"fig.update_yaxes(title_text='Volume ', secondary_y=True)\n",
"\n",
"# Adding slider\n",
"fig.update_xaxes(\n",
" rangeslider_visible=True,\n",
" rangeselector=dict(\n",
" buttons=list([\n",
" dict(count=1, label=\"1m\", step=\"month\", stepmode=\"backward\"),\n",
" dict(count=6, label=\"6m\", step=\"month\", stepmode=\"backward\"),\n",
" dict(count=1, label=\"YTD\", step=\"year\", stepmode=\"todate\"),\n",
" dict(count=1, label=\"1y\", step=\"year\", stepmode=\"backward\"),\n",
" dict(step=\"all\")\n",
" ])\n",
" )\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can observe a spike in volume around end of Feb2021. Let's take a look."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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" 2340 \n",
" 2021-02-12 \n",
" 47877.035156 \n",
" 48745.734375 \n",
" 46424.976563 \n",
" 47504.851563 \n",
" 47504.851563 \n",
" 76555041196.000000 \n",
" \n",
" \n",
" 2341 \n",
" 2021-02-13 \n",
" 47491.203125 \n",
" 48047.746094 \n",
" 46392.281250 \n",
" 47105.515625 \n",
" 47105.515625 \n",
" 70250456155.000000 \n",
" \n",
" \n",
" 2342 \n",
" 2021-02-14 \n",
" 47114.507813 \n",
" 49487.640625 \n",
" 47114.507813 \n",
" 48717.289063 \n",
" 48717.289063 \n",
" 71248675228.000000 \n",
" \n",
" \n",
" 2343 \n",
" 2021-02-15 \n",
" 48696.535156 \n",
" 48875.570313 \n",
" 46347.476563 \n",
" 47945.058594 \n",
" 47945.058594 \n",
" 77069903166.000000 \n",
" \n",
" \n",
" 2344 \n",
" 2021-02-16 \n",
" 47944.457031 \n",
" 50341.101563 \n",
" 47201.304688 \n",
" 49199.871094 \n",
" 49199.871094 \n",
" 77049582886.000000 \n",
" \n",
" \n",
" 2345 \n",
" 2021-02-17 \n",
" 49207.277344 \n",
" 52533.914063 \n",
" 49072.378906 \n",
" 52149.007813 \n",
" 52149.007813 \n",
" 80820545404.000000 \n",
" \n",
" \n",
" 2346 \n",
" 2021-02-18 \n",
" 52140.972656 \n",
" 52474.105469 \n",
" 51015.765625 \n",
" 51679.796875 \n",
" 51679.796875 \n",
" 52054723579.000000 \n",
" \n",
" \n",
" 2347 \n",
" 2021-02-19 \n",
" 51675.980469 \n",
" 56113.652344 \n",
" 50937.277344 \n",
" 55888.132813 \n",
" 55888.132813 \n",
" 63495496918.000000 \n",
" \n",
" \n",
" 2348 \n",
" 2021-02-20 \n",
" 55887.335938 \n",
" 57505.226563 \n",
" 54626.558594 \n",
" 56099.519531 \n",
" 56099.519531 \n",
" 68145460026.000000 \n",
" \n",
" \n",
" 2349 \n",
" 2021-02-21 \n",
" 56068.566406 \n",
" 58330.570313 \n",
" 55672.609375 \n",
" 57539.945313 \n",
" 57539.945313 \n",
" 51897585191.000000 \n",
" \n",
" \n",
" 2350 \n",
" 2021-02-22 \n",
" 57532.738281 \n",
" 57533.390625 \n",
" 48967.566406 \n",
" 54207.320313 \n",
" 54207.320313 \n",
" 92052420332.000000 \n",
" \n",
" \n",
" 2351 \n",
" 2021-02-23 \n",
" 54204.929688 \n",
" 54204.929688 \n",
" 45290.589844 \n",
" 48824.425781 \n",
" 48824.425781 \n",
" 106102492824.000000 \n",
" \n",
" \n",
" 2352 \n",
" 2021-02-24 \n",
" 48835.085938 \n",
" 51290.136719 \n",
" 47213.500000 \n",
" 49705.332031 \n",
" 49705.332031 \n",
" 63695521388.000000 \n",
" \n",
" \n",
" 2353 \n",
" 2021-02-25 \n",
" 49709.082031 \n",
" 51948.968750 \n",
" 47093.851563 \n",
" 47093.851563 \n",
" 47093.851563 \n",
" 54506565949.000000 \n",
" \n",
" \n",
" 2354 \n",
" 2021-02-26 \n",
" 47180.464844 \n",
" 48370.785156 \n",
" 44454.843750 \n",
" 46339.761719 \n",
" 46339.761719 \n",
" 350967941479.000000 \n",
" \n",
" \n",
" 2355 \n",
" 2021-02-27 \n",
" 46344.773438 \n",
" 48253.269531 \n",
" 45269.027344 \n",
" 46188.453125 \n",
" 46188.453125 \n",
" 45910946382.000000 \n",
" \n",
" \n",
" 2356 \n",
" 2021-02-28 \n",
" 46194.015625 \n",
" 46716.429688 \n",
" 43241.617188 \n",
" 45137.769531 \n",
" 45137.769531 \n",
" 53443887451.000000 \n",
" \n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# looking at Feb21 datapoints\n",
"dataset[(dataset['Date']>'2021-02-01') & (dataset['Date']<'2021-03-01')].style.bar(subset=['Volume'], color='#d65f5f')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After checking from a different source, the Volume for 2021-02-26 seems to be correct (USD 350.967.941.479). Looks like that day was crazy.\n",
"https://coinmarketcap.com/historical/20210226/"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image('img/coinmarketcap-20210226.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preprocessing the data"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# droppping firsts observations becuase they may not be representative of BTC behaviour now due to beginnings of crypto market\n",
"df = df.tail(n_past_total)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"training_set.shape:\t (1200, 2)\n"
]
}
],
"source": [
"# train test split\n",
"training_set = df.values\n",
"print('training_set.shape:\\t', training_set.shape)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"training_set_scaled.shape: (1200, 2)\n"
]
},
{
"data": {
"text/plain": [
"array([[0.12426163, 0.01161493],\n",
" [0.11882415, 0.01152876],\n",
" [0.12800168, 0.01262371],\n",
" ...,\n",
" [0.50170116, 0.13497817],\n",
" [0.56595702, 0.14667458],\n",
" [0.55401855, 0.14118339]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# scale\n",
"sc = MinMaxScaler(feature_range=(0,1))\n",
"training_set_scaled = sc.fit_transform(training_set)\n",
"print('training_set_scaled.shape: ', training_set_scaled.shape)\n",
"training_set_scaled"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1161, 30, 2), (1161, 10))"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# creating a data structure with 60 timesteps and 1 output\n",
"X_train = []\n",
"y_train = []\n",
"\n",
"for i in range(n_past, len(training_set_scaled) - n_future + 1):\n",
" X_train.append(training_set_scaled[i-n_past:i, :])\n",
" y_train.append(training_set_scaled[i:i+n_future, 0])\n",
"\n",
"X_train, y_train = np.array(X_train), np.array(y_train)\n",
"X_train.shape, y_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1161, 30, 2)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# reshaping (needed to fit RNN)\n",
"X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], n_features))\n",
"X_train.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RNN - LSTM with early stopping and dropout regularization \n",
"Build and fit"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"lstm (LSTM) (None, 30, 30) 3960 \n",
"_________________________________________________________________\n",
"dropout (Dropout) (None, 30, 30) 0 \n",
"_________________________________________________________________\n",
"lstm_1 (LSTM) (None, 30, 20) 4080 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 30, 20) 0 \n",
"_________________________________________________________________\n",
"lstm_2 (LSTM) (None, 30, 20) 3280 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 30, 20) 0 \n",
"_________________________________________________________________\n",
"lstm_3 (LSTM) (None, 30, 20) 3280 \n",
"_________________________________________________________________\n",
"dropout_3 (Dropout) (None, 30, 20) 0 \n",
"_________________________________________________________________\n",
"lstm_4 (LSTM) (None, 30, 20) 3280 \n",
"_________________________________________________________________\n",
"dropout_4 (Dropout) (None, 30, 20) 0 \n",
"_________________________________________________________________\n",
"lstm_5 (LSTM) (None, 30, 20) 3280 \n",
"_________________________________________________________________\n",
"dropout_5 (Dropout) (None, 30, 20) 0 \n",
"_________________________________________________________________\n",
"lstm_6 (LSTM) (None, 30, 20) 3280 \n",
"_________________________________________________________________\n",
"dropout_6 (Dropout) (None, 30, 20) 0 \n",
"_________________________________________________________________\n",
"lstm_7 (LSTM) (None, 30, 20) 3280 \n",
"_________________________________________________________________\n",
"dropout_7 (Dropout) (None, 30, 20) 0 \n",
"_________________________________________________________________\n",
"lstm_8 (LSTM) (None, 30, 20) 3280 \n",
"_________________________________________________________________\n",
"dropout_8 (Dropout) (None, 30, 20) 0 \n",
"_________________________________________________________________\n",
"lstm_9 (LSTM) (None, 20) 3280 \n",
"_________________________________________________________________\n",
"dense (Dense) (None, 10) 210 \n",
"=================================================================\n",
"Total params: 34,490\n",
"Trainable params: 34,490\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"# Building the RNN\n",
"\n",
"# Initialising the RNN\n",
"regressor = Sequential()\n",
"\n",
"# Input layer\n",
"regressor.add(LSTM(units=n_past, return_sequences=True, activation=activation, input_shape=(X_train.shape[1], n_features))) \n",
"#regressor.add(LSTM(units=neurons, return_sequences=True, activation=activation, input_shape=(X_train.shape[1], 1))) \n",
"\n",
"# Hidden layers\n",
"for _ in range(n_layers):\n",
" regressor.add(Dropout(dropout))\n",
" regressor.add(LSTM(units=n_neurons, return_sequences=True, activation=activation))\n",
"\n",
"# Last hidden layer (changing the return_sequences)\n",
"regressor.add(Dropout(dropout))\n",
"regressor.add(LSTM(units=n_neurons, return_sequences=False, activation=activation))\n",
"\n",
"# Adding the output layer\n",
"regressor.add(Dense(units=n_future))\n",
"\n",
"# Compiling the RNN\n",
"regressor.compile(optimizer=optimizer, loss='mse')\n",
"\n",
"# Model summary\n",
"regressor.summary()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# Adding early stopping\n",
"early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=patience)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/750\n",
"33/33 [==============================] - 27s 136ms/step - loss: 0.0203 - val_loss: 0.5045\n",
"Epoch 2/750\n",
"33/33 [==============================] - 2s 49ms/step - loss: 0.0105 - val_loss: 0.4923\n",
"Epoch 3/750\n",
"33/33 [==============================] - 2s 48ms/step - loss: 0.0102 - val_loss: 0.3013\n",
"Epoch 4/750\n",
"33/33 [==============================] - 2s 47ms/step - loss: 0.0039 - val_loss: 0.0947\n",
"Epoch 5/750\n",
"33/33 [==============================] - 2s 47ms/step - loss: 0.0020 - val_loss: 0.1120\n",
"Epoch 6/750\n",
"33/33 [==============================] - 2s 47ms/step - loss: 0.0018 - val_loss: 0.0773\n",
"Epoch 7/750\n",
"33/33 [==============================] - 2s 47ms/step - loss: 0.0016 - val_loss: 0.0814\n",
"Epoch 8/750\n",
"33/33 [==============================] - 2s 48ms/step - loss: 0.0014 - val_loss: 0.0671\n",
"Epoch 9/750\n",
"33/33 [==============================] - 2s 48ms/step - loss: 0.0015 - val_loss: 0.0722\n",
"Epoch 10/750\n",
"33/33 [==============================] - 2s 48ms/step - loss: 0.0014 - val_loss: 0.0704\n",
"Epoch 11/750\n",
"33/33 [==============================] - 2s 47ms/step - loss: 0.0018 - val_loss: 0.0765\n",
"Epoch 12/750\n",
"33/33 [==============================] - 2s 47ms/step - loss: 0.0014 - val_loss: 0.0589\n",
"Epoch 13/750\n",
"33/33 [==============================] - 2s 47ms/step - loss: 0.0013 - val_loss: 0.0635\n",
"Epoch 14/750\n",
"33/33 [==============================] - 2s 47ms/step - loss: 0.0014 - val_loss: 0.0863\n",
"Epoch 15/750\n",
"33/33 [==============================] - 2s 47ms/step - loss: 0.0015 - val_loss: 0.0706\n",
"Epoch 16/750\n",
"33/33 [==============================] - 2s 50ms/step - loss: 0.0012 - val_loss: 0.0548\n",
"Epoch 17/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 0.0012 - val_loss: 0.0611\n",
"Epoch 18/750\n",
"33/33 [==============================] - 2s 50ms/step - loss: 0.0014 - val_loss: 0.0853\n",
"Epoch 19/750\n",
"33/33 [==============================] - 2s 48ms/step - loss: 0.0012 - val_loss: 0.0754\n",
"Epoch 20/750\n",
"33/33 [==============================] - 2s 54ms/step - loss: 0.0012 - val_loss: 0.0626\n",
"Epoch 21/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 0.0013 - val_loss: 0.0632\n",
"Epoch 22/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 0.0012 - val_loss: 0.0693\n",
"Epoch 23/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 0.0012 - val_loss: 0.0714\n",
"Epoch 24/750\n",
"33/33 [==============================] - 2s 54ms/step - loss: 0.0012 - val_loss: 0.0697\n",
"Epoch 25/750\n",
"33/33 [==============================] - 2s 55ms/step - loss: 0.0014 - val_loss: 0.0749\n",
"Epoch 26/750\n",
"33/33 [==============================] - 2s 54ms/step - loss: 0.0011 - val_loss: 0.0548\n",
"Epoch 27/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 0.0012 - val_loss: 0.0798\n",
"Epoch 28/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 0.0011 - val_loss: 0.0559\n",
"Epoch 29/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 0.0015 - val_loss: 0.0588\n",
"Epoch 30/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 0.0013 - val_loss: 0.0637\n",
"Epoch 31/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 0.0011 - val_loss: 0.0669\n",
"Epoch 32/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 0.0010 - val_loss: 0.0534\n",
"Epoch 33/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 9.4748e-04 - val_loss: 0.0678\n",
"Epoch 34/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 0.0010 - val_loss: 0.0644\n",
"Epoch 35/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 9.5336e-04 - val_loss: 0.0671\n",
"Epoch 36/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 0.0011 - val_loss: 0.0841\n",
"Epoch 37/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 8.4692e-04 - val_loss: 0.0615\n",
"Epoch 38/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 0.0011 - val_loss: 0.0603\n",
"Epoch 39/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 8.4627e-04 - val_loss: 0.0672\n",
"Epoch 40/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 8.6851e-04 - val_loss: 0.0506\n",
"Epoch 41/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 8.3417e-04 - val_loss: 0.0739\n",
"Epoch 42/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 8.0763e-04 - val_loss: 0.0537\n",
"Epoch 43/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 9.2846e-04 - val_loss: 0.0691\n",
"Epoch 44/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 8.5094e-04 - val_loss: 0.0640\n",
"Epoch 45/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 8.7225e-04 - val_loss: 0.0672\n",
"Epoch 46/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 9.2768e-04 - val_loss: 0.0432\n",
"Epoch 47/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 8.1308e-04 - val_loss: 0.0423 ETA: 0s - loss: 8.1836e\n",
"Epoch 48/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 7.4661e-04 - val_loss: 0.0610\n",
"Epoch 49/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 7.8371e-04 - val_loss: 0.0697\n",
"Epoch 50/750\n",
"33/33 [==============================] - 2s 51ms/step - loss: 8.3649e-04 - val_loss: 0.0728\n",
"Epoch 51/750\n",
"33/33 [==============================] - 2s 56ms/step - loss: 7.5589e-04 - val_loss: 0.0828\n",
"Epoch 52/750\n",
"33/33 [==============================] - 2s 54ms/step - loss: 0.0010 - val_loss: 0.0556\n",
"Epoch 53/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 8.5908e-04 - val_loss: 0.0762\n",
"Epoch 54/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 6.7105e-04 - val_loss: 0.0785\n",
"Epoch 55/750\n",
"33/33 [==============================] - 2s 51ms/step - loss: 7.6480e-04 - val_loss: 0.0552\n",
"Epoch 56/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 6.6989e-04 - val_loss: 0.0720\n",
"Epoch 57/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 6.2902e-04 - val_loss: 0.0566\n",
"Epoch 58/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 7.1327e-04 - val_loss: 0.0776\n",
"Epoch 59/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 6.8479e-04 - val_loss: 0.0653\n",
"Epoch 60/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 7.2756e-04 - val_loss: 0.0629\n",
"Epoch 61/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 7.0480e-04 - val_loss: 0.0673\n",
"Epoch 62/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 6.8987e-04 - val_loss: 0.0710\n",
"Epoch 63/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 6.9047e-04 - val_loss: 0.0691\n",
"Epoch 64/750\n",
"33/33 [==============================] - 2s 51ms/step - loss: 6.4233e-04 - val_loss: 0.0674\n",
"Epoch 65/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 6.9760e-04 - val_loss: 0.0812\n",
"Epoch 66/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 5.8079e-04 - val_loss: 0.0574\n",
"Epoch 67/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 6.2155e-04 - val_loss: 0.0606\n",
"Epoch 68/750\n",
"33/33 [==============================] - 2s 51ms/step - loss: 6.2063e-04 - val_loss: 0.0437\n",
"Epoch 69/750\n",
"33/33 [==============================] - 2s 51ms/step - loss: 9.1900e-04 - val_loss: 0.0795\n",
"Epoch 70/750\n",
"33/33 [==============================] - 2s 52ms/step - loss: 6.5857e-04 - val_loss: 0.0666\n",
"Epoch 71/750\n",
"33/33 [==============================] - 2s 53ms/step - loss: 6.1912e-04 - val_loss: 0.0721\n",
"Epoch 72/750\n",
"33/33 [==============================] - 2s 54ms/step - loss: 6.0794e-04 - val_loss: 0.0628\n",
"Epoch 00072: early stopping\n"
]
}
],
"source": [
"# Fitting the RNN to the Training set\n",
"res = regressor.fit(X_train, y_train\n",
" , batch_size=32\n",
" , epochs=750\n",
" , validation_split=0.1\n",
" , callbacks=[early_stop]\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# Exporting the regressor\n",
"last_date = dataset.Date.values[-1]\n",
"params = ['reg', last_date, n_past_total, n_past, n_future, activation, n_layers, n_neurons, n_features, patience, optimizer]\n",
"modelname = 'output/'\n",
"for i in params:\n",
" modelname += str(i)\n",
" if i!= params[-1]:\n",
" modelname += '_'\n",
"if not os.path.exists(modelname):\n",
" os.makedirs(modelname)\n",
"regressor.save('{}/regressor.h5'.format(modelname))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['loss', 'val_loss']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(res.history)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Validation"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plotting Loss/MSE\n",
"\n",
"results = res\n",
"\n",
"history = results.history\n",
"plt.figure(figsize=(12,4))\n",
"plt.plot(history['val_loss'])\n",
"plt.plot(history['loss'])\n",
"plt.legend(['val_loss', 'loss'])\n",
"plt.title('Loss')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Loss')\n",
"plt.savefig('{}/Loss.png'.format(modelname))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def dummy_invscaler(y, n_features):\n",
" '''\n",
" Since the scaler was trained into 2 features, it needs two features to perform the inverse scaleer.\n",
" For that purpose, this function will create a dummy array and concatenate it to the y_pred/y_true.\n",
" That dummy of ones will be drop after performing the inverse_transform.\n",
" INPUTS: array 'y', shape (X,)\n",
" '''\n",
" y = np.array(y).reshape(-1,1)\n",
" if n_features>1:\n",
" dummy = np.ones((len(y), n_features-1))\n",
" y = np.concatenate((y, dummy), axis=1)\n",
" y = sc.inverse_transform(y)\n",
" y = y[:,0]\n",
" else:\n",
" y = sc.inverse_transform(y)\n",
" return y"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y_pred:\n",
" [37148.42007106434, 37375.90173512726, 37340.001538518474, 37676.947550316, 38732.70573365663, 39165.05632037239, 39478.00301122301, 39727.33587560325, 40236.67395618728, 40719.41198114914]\n",
"y_true:\n",
" [[36684.925781], [37575.179688], [39208.765625], [36894.40625], [35551.957031], [35862.378906], [33560.707031], [33472.632813], [37345.121094], [36625.628906]]\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Validation\n",
"\n",
"# getting the predictions\n",
"y_pred = regressor.predict(X_train[-1].reshape(1, n_past, n_features)).tolist()[0]\n",
"y_pred = dummy_invscaler(y_pred, n_features)\n",
"\n",
"# creating a DF of the predicted prices\n",
"y_pred_df = pd.DataFrame(y_pred, \n",
" index=df[['Close']].tail(n_future).index, \n",
" columns=df[['Close']].columns)\n",
"\n",
"# getting the true values\n",
"y_true_df = df[['Close']].tail(n_past)\n",
"y_true = y_true_df.tail(n_future).values\n",
"\n",
"print('y_pred:\\n', y_pred.tolist())\n",
"print('y_true:\\n', y_true.tolist())\n",
"\n",
"# plotting the results\n",
"plt.figure(figsize=(16,5))\n",
"plt.plot(y_pred_df, label='Predicted')\n",
"plt.plot(y_true_df, label='True')\n",
"\n",
"plt.title('BTC price Predicted vs True')\n",
"plt.legend()\n",
"plt.savefig('{}/Validation.png'.format(modelname))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3530.4372124427737"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Root Mean Square Error (RMSE)\n",
"rmse = math.sqrt(mean_squared_error(y_true, y_pred))\n",
"rmse"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2895.4838726926305"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Mean Square Error (MSE)\n",
"mse = mean_absolute_error(y_true, y_pred)\n",
"mse"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-1.2490513116772766"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Explained variance regression score function.\n",
"explained_variance_score(y_true, y_pred)\n",
"# Best possible score is 1.0, lower values are worse."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predictions"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y_pred:\n",
" [37012.387997282254, 37245.56685084102, 37207.198830030684, 37543.77484780673, 38584.722493763125, 39018.28723561734, 39340.4047491558, 39586.88901878811, 40092.390656508614, 40572.0106736001]\n",
"y_true:\n",
" [[36684.925781], [37575.179688], [39208.765625], [36894.40625], [35551.957031], [35862.378906], [33560.707031], [33472.632813], [37345.121094], [36625.628906]]\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Predicting/Forecasting\n",
"\n",
"# getting the predictions\n",
"x = df[features][-n_past:].values\n",
"x = sc.transform(x)\n",
"y_pred = regressor.predict(x.reshape(1, n_past, n_features)).tolist()[0]\n",
"y_pred = dummy_invscaler(y_pred, n_features)\n",
"\n",
"# creating a DF of the predicted prices\n",
"y_pred_df = pd.DataFrame(y_pred, \n",
" index=pd.date_range(start=df[['Close']].index[-1]+datetime.timedelta(days=1),\n",
" periods=len(y_pred), \n",
" freq=\"D\"), \n",
" columns=df[['Close']].columns)\n",
"\n",
"# getting the true values\n",
"y_true_df = df[['Close']].tail(n_past)\n",
"\n",
"# linking them\n",
"#y_true_df = y_true_df.append(y_pred_df.head(1))\n",
"y_pred_df = y_pred_df.append(y_true_df.tail(1)).sort_index()\n",
"\n",
"print('y_pred:\\n', y_pred.tolist())\n",
"print('y_true:\\n', y_true.tolist())\n",
"\n",
"# plotting the results\n",
"plt.figure(figsize=(16,5))\n",
"plt.plot(y_pred_df, label='Predicted')\n",
"plt.plot(y_true_df, label='True')\n",
"\n",
"plt.title('BTC price Predicted vs True')\n",
"plt.legend()\n",
"plt.savefig('{}/Predictions.png'.format(modelname))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" \n",
" Close \n",
" \n",
" \n",
" \n",
" \n",
" 2021-06-10 \n",
" 36625.629 \n",
" \n",
" \n",
" 2021-06-11 \n",
" 37012.388 \n",
" \n",
" \n",
" 2021-06-12 \n",
" 37245.567 \n",
" \n",
" \n",
" 2021-06-13 \n",
" 37207.199 \n",
" \n",
" \n",
" 2021-06-14 \n",
" 37543.775 \n",
" \n",
" \n",
" 2021-06-15 \n",
" 38584.722 \n",
" \n",
" \n",
" 2021-06-16 \n",
" 39018.287 \n",
" \n",
" \n",
" 2021-06-17 \n",
" 39340.405 \n",
" \n",
" \n",
" 2021-06-18 \n",
" 39586.889 \n",
" \n",
" \n",
" 2021-06-19 \n",
" 40092.391 \n",
" \n",
" \n",
" 2021-06-20 \n",
" 40572.011 \n",
" \n",
" \n",
"
\n",
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"
],
"text/plain": [
" Close\n",
"2021-06-10 36625.629\n",
"2021-06-11 37012.388\n",
"2021-06-12 37245.567\n",
"2021-06-13 37207.199\n",
"2021-06-14 37543.775\n",
"2021-06-15 38584.722\n",
"2021-06-16 39018.287\n",
"2021-06-17 39340.405\n",
"2021-06-18 39586.889\n",
"2021-06-19 40092.391\n",
"2021-06-20 40572.011"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# export to csv\n",
"y_pred_df.to_csv('{}/preds_{}.csv'.format(modelname,n_future))\n",
"# show\n",
"y_pred_df"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}