Airlines-Passengers-Time-Series-analysis

The airline industry relies heavily on forecasting passenger demand to optimize operational efficiency and profitability. However, accurately predicting passenger demand is a complex task due to various external and internal factors, such as seasonality, economic conditions, and marketing campaigns.

• In this project, we aim to develop a time series forecasting model using historical airline passenger data to accurately predict future passenger demand.
• The model should take into account different patterns and trends in the data, such as seasonality and trend, and incorporate external factors that affect passenger demand.
• The goal is to provide actionable insights to airline companies to make informed decisions on capacity planning, pricing, and marketing strategies.

Walk Through The Project

• Exploratory data analysis will be conducted to analyze the time series data using visual plots and comment on the findings.

• The following forecasting approaches will be applied and compared:

  • Simple Moving Average
  • Naïve Forecasting
  • Weight Moving Average
  • Simple Linear Regression Model
  • Classical Decomposition Approach
  • STL Decomposition Approach
  • ARIMA/S-ARIMA Model
  • Exponential Smoothing
  • Facebook Prophet Algorithm
  • Supervised Machine Learning algorithm (XGBOOST Regressor)

• Each approach will be commented on to explain why the results are produced in that manner and their pros and cons.

• Before applying any approach, stationarity of the time series data will be checked and necessary transformations will be applied.

• The classical and STL decomposition approaches will be compared based on the decomposed components.

• Different ARIMA models will be tested based on ACF, PACF plots, summary statistics, and residuals analysis, and the AIC or BIC will also be checked.

• For exponential smoothing, single, double, and triple exponential smoothing approaches will be applied, and the need for dampening will be determined both theoretically and practically.

• The Facebook Prophet algorithm will be applied, and its effectiveness will be assessed.

• Finally, a supervised machine learning algorithm (XGBOOST Regressor) will be applied using lag features (Yt−1, Yt−2, ... , avg(yt−n)).

Evaluation Metric

The dataset will be evaluated using the RMSE metric

Through three different spliting techniques:

• roll-forward

• simple train-test split

• Time series cross-validation from sklearn

Results

Technique	Best RMSE
Simple Moving Average	112.16
Naïve forecast	105.75
Weighted Moving Average	122.87
Linear Regression Model	85.7
Classical Decomposition	60
STL Decomposition	68.7
ARIMA	39.72
S-ARIMA	13.76
Exponential Smoothing Single	47.36
Exponential Smoothing Double Additive	47.34
Exponential Smoothing Double Multiplicative	43.7
Exponential Smoothing Triple Additive	12.3
Exponential Smoothing Triple Multiplicative	11.51
FB Prophet	22.48
XGBOOST Regressor	49.6

Conclusion

we have analyzed the airline passengers dataset using various time series techniques and found that the Exponential Smothing Triple Multiplicative model with RMSE =11.51 which is the best results overall when predicting future passenger numbers.