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‎ARIMA_&_SARIMA.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "ARIMA & SARIMA.ipynb",
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"provenance": [],
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"mount_file_id": "1h0-Hypu0AntwTzJgUU1aW4y0SWIsinUC",
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"authorship_tag": "ABX9TyMTf7bGAILd6nKrxNRhZPIC",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<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>"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"\n",
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"ARIMA and Seasonal ARIMA\n",
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"Autoregressive Integrated Moving Averages\n",
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"The general process for ARIMA models is the following:\n",
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"\n",
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"Visualize the Time Series Data\n",
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"Make the time series data stationary\n",
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"Plot the Correlation and AutoCorrelation Charts\n",
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"Construct the ARIMA Model or Seasonal ARIMA based on the data\n",
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"Use the model to make predictions\n",
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"\n",
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"\n"
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],
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"metadata": {
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"id": "CjJ4wkcghkSx"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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],
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"metadata": {
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"id": "82o_BC4-XPOz"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"id": "xkPuoTAhVnNM"
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},
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"outputs": [],
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"source": [
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"CS_hs = pd.read_csv('CS_hs.csv');\n",
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"CS_m_all = pd.read_csv('CS_m_all.csv')\n",
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"CS_mor = pd.read_csv('CS_mor.csv')\n",
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"CS_pr = pd.read_csv('CS_pr.csv')\n",
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"CS_q_all = pd.read_csv('CS_q_all.csv')\n",
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"CS_wa = pd.read_csv('CS_wa.csv')\n",
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"FH_hs = pd.read_csv('FH_hs.csv')\n",
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"FH_m_all = pd.read_csv('FH_m_all.csv')\n",
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"FH_mor = pd.read_csv('FH_mor.csv')\n",
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"FH_pr = pd.read_csv('FH_pr.csv')\n",
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"FH_q_all = pd.read_csv('FH_q_all.csv')\n",
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"FH_wa = pd.read_csv('FH_wa.csv')\n",
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"ZI_m_all = pd.read_csv('Zi_m_all.csv')\n",
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"ZI_mor = pd.read_csv('Zi_mor.csv')\n",
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"ZI_pr = pd.read_csv('Zi_prfi.csv')\n",
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"ZI_q_all = pd.read_csv('Zi_q_all.csv')\n",
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"ZI_wa = pd.read_csv('Zi_Wa.csv')"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"Zi_hs = pd.read_csv('Zi_hs.csv')"
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],
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"metadata": {
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"id": "SzAUMvQXmTM_"
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},
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"execution_count": 18,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"# Convert Month into Datetime\n",
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"CS_hs['Date']=pd.to_datetime(CS_hs['Date'])\n",
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"CS_mor['Date']=pd.to_datetime(CS_mor['Date'])\n",
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"CS_pr['Date']=pd.to_datetime(CS_pr['Date'])\n",
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"CS_wa['Date']=pd.to_datetime(CS_wa['Date'])\n",
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"CS_q_all['Date']=pd.to_datetime(CS_q_all['Date'])\n",
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"CS_m_all['Date']=pd.to_datetime(CS_m_all['Date'])\n"
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],
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"metadata": {
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"id": "NMrrkyvnidMA"
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},
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"execution_count": 13,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"FH_hs['Date']=pd.to_datetime(FH_hs['Date'])\n",
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"FH_mor['Date']=pd.to_datetime(FH_mor['Date'])\n",
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"FH_pr['Date']=pd.to_datetime(FH_pr['Date'])\n",
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"FH_wa['Date']=pd.to_datetime(FH_wa['Date'])\n",
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"FH_q_all['Date']=pd.to_datetime(FH_q_all['Date'])\n",
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"FH_m_all['Date']=pd.to_datetime(FH_m_all['Date'])"
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],
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"metadata": {
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"id": "_eO1HxE0jPne"
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},
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"execution_count": 14,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"ZI_hs['Date']=pd.to_datetime(ZI_hs['Date'])\n",
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"ZI_mor['Date']=pd.to_datetime(ZI_mor['Date'])\n",
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"ZI_pr['Date']=pd.to_datetime(ZI_pr['Date'])\n",
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"ZI_wa['Date']=pd.to_datetime(ZI_wa['Date'])\n",
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"ZI_q_all['Date']=pd.to_datetime(ZI_q_all['Date'])\n",
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"ZI_m_all['Date']=pd.to_datetime(ZI_m_all['Date'])"
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],
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"metadata": {
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"id": "Kl6Lb_RUje0O"
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},
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"execution_count": 20,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"df.set_index('Month',inplace=True)"
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],
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"metadata": {
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"id": "9fnag4Osipmc"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"df.describe()"
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],
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"metadata": {
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"id": "5RMvQw9vipu0"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"ARIMA TESTING"
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],
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"metadata": {
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"id": "y30Ph_rBmpxn"
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}
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},
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{
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"cell_type": "code",
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"source": [
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""
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],
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"metadata": {
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"id": "S9FanlKDmtXi"
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},
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"execution_count": null,
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"outputs": []
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}
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]
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}

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