1+ {
2+ "nbformat" :4 ,
3+ "nbformat_minor" :0 ,
4+ "metadata" : {
5+ "colab" : {
6+ "name" :" ARIMA & SARIMA.ipynb"
7+ "provenance" : [],
8+ "mount_file_id" :" 1h0-Hypu0AntwTzJgUU1aW4y0SWIsinUC"
9+ "authorship_tag" :" ABX9TyMTf7bGAILd6nKrxNRhZPIC"
10+ "include_colab_link" :true
11+ },
12+ "kernelspec" : {
13+ "name" :" python3"
14+ "display_name" :" Python 3"
15+ },
16+ "language_info" : {
17+ "name" :" python"
18+ }
19+ },
20+ "cells" : [
21+ {
22+ "cell_type" :" markdown"
23+ "metadata" : {
24+ "id" :" view-in-github"
25+ "colab_type" :" text"
26+ },
27+ "source" : [
28+ " <a href=\" https://colab.research.google.com/github/noobcoder2/demo-repo/blob/main/ARIMA_%26_SARIMA.ipynb\" target=\" _parent\" ><img src=\" https://colab.research.google.com/assets/colab-badge.svg\" alt=\" Open In Colab\" /></a>"
29+ ]
30+ },
31+ {
32+ "cell_type" :" markdown"
33+ "source" : [
34+ " \n "
35+ " ARIMA and Seasonal ARIMA\n "
36+ " Autoregressive Integrated Moving Averages\n "
37+ " The general process for ARIMA models is the following:\n "
38+ " \n "
39+ " Visualize the Time Series Data\n "
40+ " Make the time series data stationary\n "
41+ " Plot the Correlation and AutoCorrelation Charts\n "
42+ " Construct the ARIMA Model or Seasonal ARIMA based on the data\n "
43+ " Use the model to make predictions\n "
44+ " \n "
45+ " \n "
46+ ],
47+ "metadata" : {
48+ "id" :" CjJ4wkcghkSx"
49+ }
50+ },
51+ {
52+ "cell_type" :" code"
53+ "source" : [
54+ " import pandas as pd\n "
55+ " import numpy as np\n "
56+ " import seaborn as sns\n "
57+ " import matplotlib.pyplot as plt\n "
58+ " %matplotlib inline"
59+ ],
60+ "metadata" : {
61+ "id" :" 82o_BC4-XPOz"
62+ },
63+ "execution_count" :null ,
64+ "outputs" : []
65+ },
66+ {
67+ "cell_type" :" code"
68+ "execution_count" :12 ,
69+ "metadata" : {
70+ "id" :" xkPuoTAhVnNM"
71+ },
72+ "outputs" : [],
73+ "source" : [
74+ " CS_hs = pd.read_csv('CS_hs.csv');\n "
75+ " CS_m_all = pd.read_csv('CS_m_all.csv')\n "
76+ " CS_mor = pd.read_csv('CS_mor.csv')\n "
77+ " CS_pr = pd.read_csv('CS_pr.csv')\n "
78+ " CS_q_all = pd.read_csv('CS_q_all.csv')\n "
79+ " CS_wa = pd.read_csv('CS_wa.csv')\n "
80+ " FH_hs = pd.read_csv('FH_hs.csv')\n "
81+ " FH_m_all = pd.read_csv('FH_m_all.csv')\n "
82+ " FH_mor = pd.read_csv('FH_mor.csv')\n "
83+ " FH_pr = pd.read_csv('FH_pr.csv')\n "
84+ " FH_q_all = pd.read_csv('FH_q_all.csv')\n "
85+ " FH_wa = pd.read_csv('FH_wa.csv')\n "
86+ " ZI_m_all = pd.read_csv('Zi_m_all.csv')\n "
87+ " ZI_mor = pd.read_csv('Zi_mor.csv')\n "
88+ " ZI_pr = pd.read_csv('Zi_prfi.csv')\n "
89+ " ZI_q_all = pd.read_csv('Zi_q_all.csv')\n "
90+ " ZI_wa = pd.read_csv('Zi_Wa.csv')"
91+ ]
92+ },
93+ {
94+ "cell_type" :" code"
95+ "source" : [
96+ " Zi_hs = pd.read_csv('Zi_hs.csv')"
97+ ],
98+ "metadata" : {
99+ "id" :" SzAUMvQXmTM_"
100+ },
101+ "execution_count" :18 ,
102+ "outputs" : []
103+ },
104+ {
105+ "cell_type" :" code"
106+ "source" : [
107+ " # Convert Month into Datetime\n "
108+ " CS_hs['Date']=pd.to_datetime(CS_hs['Date'])\n "
109+ " CS_mor['Date']=pd.to_datetime(CS_mor['Date'])\n "
110+ " CS_pr['Date']=pd.to_datetime(CS_pr['Date'])\n "
111+ " CS_wa['Date']=pd.to_datetime(CS_wa['Date'])\n "
112+ " CS_q_all['Date']=pd.to_datetime(CS_q_all['Date'])\n "
113+ " CS_m_all['Date']=pd.to_datetime(CS_m_all['Date'])\n "
114+ ],
115+ "metadata" : {
116+ "id" :" NMrrkyvnidMA"
117+ },
118+ "execution_count" :13 ,
119+ "outputs" : []
120+ },
121+ {
122+ "cell_type" :" code"
123+ "source" : [
124+ " FH_hs['Date']=pd.to_datetime(FH_hs['Date'])\n "
125+ " FH_mor['Date']=pd.to_datetime(FH_mor['Date'])\n "
126+ " FH_pr['Date']=pd.to_datetime(FH_pr['Date'])\n "
127+ " FH_wa['Date']=pd.to_datetime(FH_wa['Date'])\n "
128+ " FH_q_all['Date']=pd.to_datetime(FH_q_all['Date'])\n "
129+ " FH_m_all['Date']=pd.to_datetime(FH_m_all['Date'])"
130+ ],
131+ "metadata" : {
132+ "id" :" _eO1HxE0jPne"
133+ },
134+ "execution_count" :14 ,
135+ "outputs" : []
136+ },
137+ {
138+ "cell_type" :" code"
139+ "source" : [
140+ " ZI_hs['Date']=pd.to_datetime(ZI_hs['Date'])\n "
141+ " ZI_mor['Date']=pd.to_datetime(ZI_mor['Date'])\n "
142+ " ZI_pr['Date']=pd.to_datetime(ZI_pr['Date'])\n "
143+ " ZI_wa['Date']=pd.to_datetime(ZI_wa['Date'])\n "
144+ " ZI_q_all['Date']=pd.to_datetime(ZI_q_all['Date'])\n "
145+ " ZI_m_all['Date']=pd.to_datetime(ZI_m_all['Date'])"
146+ ],
147+ "metadata" : {
148+ "id" :" Kl6Lb_RUje0O"
149+ },
150+ "execution_count" :20 ,
151+ "outputs" : []
152+ },
153+ {
154+ "cell_type" :" code"
155+ "source" : [
156+ " df.set_index('Month',inplace=True)"
157+ ],
158+ "metadata" : {
159+ "id" :" 9fnag4Osipmc"
160+ },
161+ "execution_count" :null ,
162+ "outputs" : []
163+ },
164+ {
165+ "cell_type" :" code"
166+ "source" : [
167+ " df.describe()"
168+ ],
169+ "metadata" : {
170+ "id" :" 5RMvQw9vipu0"
171+ },
172+ "execution_count" :null ,
173+ "outputs" : []
174+ },
175+ {
176+ "cell_type" :" markdown"
177+ "source" : [
178+ " ARIMA TESTING"
179+ ],
180+ "metadata" : {
181+ "id" :" y30Ph_rBmpxn"
182+ }
183+ },
184+ {
185+ "cell_type" :" code"
186+ "source" : [
187+ " "
188+ ],
189+ "metadata" : {
190+ "id" :" S9FanlKDmtXi"
191+ },
192+ "execution_count" :null ,
193+ "outputs" : []
194+ }
195+ ]
196+ }