Ever wanted to delete all your likes/favorites from Twitter but only found broken/expensive tools? You are in the right place.
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| # Tweepy is an easy-to-use Python library for accessing the Twitter API | |
| # https://www.tweepy.org | |
| from datetime import datetime, timedelta | |
| import tweepy | |
| import sys, os, time | |
| import json | |
| # The best way to manage your Twitter data is through the archive of all your account data | |
| # Request archive at https://twitter.com/settings/download_your_data |
| def AIC_iteration(): | |
| warnings.filterwarnings("ignore") | |
| df1 = df['price_sterling'] | |
| df2 = df1.loc[:300] | |
| X = df2.values | |
| size = int(len(X) * 0.66) | |
| train, test = X[0:size] , X[size:len(X)] | |
| history = [x for x in train] | |
| p = d = q = range(0,6) |
| def stat_test(): | |
| df1 = df.loc[:200, 'price_sterling'] | |
| X = df1.values | |
| print(X) | |
| result = adfuller(X) | |
| print('ADF Statistic: %f' % result[0]) | |
| print('p-value: %f' % result[1]) | |
| print('Critical Values:') | |
| for key, value in result[4].items(): | |
| print('\t%s: %.3f' % (key, value)) |
| def ARIM_pre(): | |
| warnings.filterwarnings("ignore") | |
| df1 = df['price_sterling'] | |
| df2 = df1.loc[:300] | |
| X = df2.values | |
| size = int(len(X) * 0.66) | |
| train, test = X[0:size] , X[size:len(X)] | |
| history = [x for x in train] | |
| predictions = [] | |
| predictionslower = [] |