Exploratory data analysis ”

1. Exploration and Analysis:
   1. Thoroughly explored and analyzed time series data to identify inherent patterns in real-world data.
   2. Documented findings and prepared a comprehensive technical report with informative graphs.
2. Identified Patterns:
   1. Revealed an additive trend and multiplicative seasonality in the dataset.
   2. Discovered additional weekly seasonality through rigorous analysis.
   3. Employed statistical tests to supplement visual analysis and addressed any discrepancies.
   4. Generated and examined Autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs.
3. Conclusion:
   1. Provided a summary of patterns observed across all individual time series within the group.
   2. Analyzed commonalities and differences among time series patterns, discussing implications for automated forecasting.

Machine Learning Models for Accurate Forecasting

1. Model Construction and Evaluation:
   1. Constructed and evaluated seven forecasting models, including exponential smoothing, ARIMA, linear regression, and neural networks.
2. Performance Assessment:
   1. Assessed model performance using metrics such as RMSE, MAE, MAPE, AIC, and BIC, alongside time series cross-validation techniques.
3. Model Comparison:
   1. Compared model performance against Naïve and Seasonal Naïve models to determine the best-fit model for forecasting ATM withdrawal data.

Exponential smoothing model

ARIMA model

Regression model

Neural network model