In order, to provide cutting-edge solution to optimize operations, enhance planning, and deliver exceptional customer experiences, I had implemented Airline Passengers Traffic Forecasting using a Time Series Model i.e. ARIMA Model to predict the traffic for next 5 years for helping the Travel Agency helps organizations understand the underlying causes of trends or systemic patterns over time.
An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends. A statistical model is autoregressive if it predicts future values based on past values.
It's used in statistics and econometrics to measure events that happen over a period of time.
ARIMA models use differencing to convert a non-stationary time series into a stationary one, and then predict future values from historical data. These models use “auto” correlations and moving averages over residual errors in the data to forecast future values.
I have meticulously performed dataset cleaning and preprocessing using Python, then successfully developed a robust pipeline to test the stationarity of data. Two methods were employed to confirm non-stationarity of data. Firstly, the Rolling Statistics technique was utilized to observe variations in moving averages or standard deviations over time. Secondly, the Augmented Dickey-Fuller test (ADCF) provided crucial values for identifying stationarity. To achieve stationarity, data transformations such as log scale, exponential decay, and time shift were applied. Finally, models were built using the ARIMA model to provide accurate predictions. By leveraging this statistical approach, which relies on the autoregressive nature of the data, accurate predictions of future values based on historical data have been achieved.
Python - Model Building
Hence, Airline Passengers Traffic Forecasting using ARIMA Model successfully implemented for next 5 years.