bhushan45/end-to-end-data-science-project-time-series-analysis-for-temperature-forecasting-using-arima-model

Successfully collected, cleaned and analysed the time series data and used ARIMA model to forecast average temperature using and visualized the Temperature pattern using Python Numpy, Pandas, Matplotlib and Statsmodels

End to End Data Science Project Time Series Analysis for Temperature Forecasting using ARIMA Model

Requirements

• Python 3.5 or higher
• Numpy
• Pandas
• Matplotlib
• Statsmodels

Dataset

In this project, the Dataset used is the famous Kaggle Dataset.

Exploratory Data Analysis

First we use pandas to read the CSV file. After that we perform Data Cleaning such as removing unwanted columns, identifying and filling the missing values.

Checking Time Series Data Stationarity:

This is achieved using Augmented Dickey-Fuller test which assists finding out the stationary properties in the Time series data.

The stationarity check is carried out using the plot and p-value is tested.

Augmented Dickey-Fuller test:

type of df_test: <class 'tuple'>
df_test: (-4.271198430066125, 0.00049808780753740904, 16, 508, {'1%': -3.4432882895877501, '10%': -2.5698092313534628, '5%': -2.8672462791357867}, 2060.5638228165117)
df_output:
Test Statistic -4.271198

p-value 0.000498

#Lags Used 16.000000
Number of Observations Used 508.000000
dtype: float64
Test Statistic -4.271198
p-value 0.000498
#Lags Used 16.000000
Number of Observations Used 508.000000
Critical Value (1%) -3.443288
Critical Value (10%) -2.569809
Critical Value (5%) -2.867246
dtype: float64\

As the p-value is below the threshold value, thus we can reject null-hypothesis which states that the data is not stationary.

Modelling and Forecasting Temperature

I used ARIMA model which stands for "Auto-Regressive Integrated Moving Averages".

In this case as the data is stationary therefore only AR and MA have been taken into account.

Then AIC is calculated and then I found values of p and q having lowest AIC where the p represents the number of Auto-Regressive (AR) terms and q represents the number of Moving Averages (MA) terms.

After that the results are predicted. Here is the example of results.

Year	Temperature Forecasting (Deg C)
2023-01-01	-0.244643
2023-02-01	0.817309
2023-03-01	4.039086
2023-04-01	8.557097
2023-05-01	13.160331
2023-06-01	16.614962
2023-07-01	17.995051
2023-08-01	16.930731
2023-09-01	13.707325
2023-10-01	9.188862
2023-11-01	4.586472
2023-12-01	1.133756