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Added SMA and improved plotting

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Dennis Thiessen
2017-12-15 15:16:22 +01:00
parent 055ada2766
commit 5c08182aad
1 changed files with 20 additions and 6 deletions
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26
bot/core.py
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@@ -2,12 +2,15 @@
import time
import pandas as pd
import numpy as np
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.dates as md
import tensorflow as tf
import urllib.request, json
from bot.shared_config import *
def main():
start_time = time.time()
@@ -29,7 +32,7 @@ def main():
dump(green("Retrieved API in {0:.3f}ms sec".format((time.time() - start_time)*100)))
dump(yellow("Initialize Tensorflow"))
f_horizon = 1 # forecast horizon, one period into the future
f_horizon = 10 # forecast horizon, one period into the future
num_periods = 20 # number of periods per vector we are using to predict one period ahead
inputs = 2 # number of vectors submitted
hidden = 100 # number of neurons we will recursively work through, can be changed to improve accuracy
@@ -94,13 +97,24 @@ def main():
dump(yellow("Start Plotting and output"))
actual_series = pd.Series(np.concatenate([np.ravel(X_test)[::2],np.ravel(Y_test)]))
actual_prediction = pd.Series(np.concatenate([np.ravel(X_test)[::2],np.ravel(y_pred)]))
actual_series = pd.Series(np.concatenate([np.ravel(X_test)[::2], np.ravel(Y_test)]))
actual_prediction = pd.Series(np.concatenate([np.ravel(X_test)[::2], np.ravel(y_pred)]))
plt.title("Forecast vs Actual", fontsize=14)
plt.plot(actual_series, "b-", markersize=10, label="Actual")
# plt.plot(pd.Series(np.ravel(Y_test)), "w*", markersize=10)
plt.plot(actual_prediction, "r-", markersize=7, label="Forecast")
xfmt = md.DateFormatter('%d.%m %H:%M:%S')
plt.subplots_adjust(bottom=0.2)
plt.xticks(rotation=25)
plt.gca().xaxis.set_major_formatter(xfmt)
dates = [dt.datetime.fromtimestamp(int(ts)) for ts in timestamps]
datenums = md.date2num(dates)
plt.plot(datenums[-40:], actual_series, "b--", linewidth=1.0, label="Actual")
plt.plot(datenums[-40:], actual_prediction, "r--", linewidth=1.0, label="Forecast")
plt.plot(datenums[-40:], actual_series.rolling(window=3, center=False).mean(), "y-", linewidth=2.0, label="Actual MA")
plt.plot(datenums[-40:], actual_prediction.rolling(window=3, center=False).mean(), "g-", linewidth=2.0, label="Predicted MA")
plt.legend(loc="upper left")
plt.xlabel("Time Periods")