Inspiration
The inspiration for this project came from the desire to understand patterns in time series data. The ability to forecast future data points in a series can be incredibly valuable in many fields.
What it does
This project involves an in-depth analysis of the AirPassengers dataset, which is a classic dataset for time series analysis. It shows the monthly totals of international airline passengers from 1949 to 1960. We applied various techniques such as decomposition, checking for stationarity, and fitting an ARIMA model to the data.
How we built it
We used Python and its powerful libraries like pandas, matplotlib, and statsmodels for the analysis. The entire project was carried out in a Jupyter notebook, which allowed for interactive coding and visualization.
Challenges we ran into
One of the main challenges was ensuring that the time series data was stationary before applying the ARIMA model. This required differencing the data and checking for stationarity multiple times.
Accomplishments that we're proud of
We're proud of successfully building an ARIMA model and using it to forecast future data points in the series. The model showed a good fit to the data, and the forecasts aligned well with the trends and seasonality observed in the original data.
What we learned
Through this project, we learned how to perform time series analysis in Python, from loading and visualizing the data to decomposing it into its components, checking for stationarity, and finally building and evaluating a forecasting model.
What's next for Time Series Analysis
The next step would be to apply this analysis to other time series datasets. There's also room to explore other models for time series forecasting, such as state space models and recurrent neural networks (RNNs). Another interesting direction could be to build a web application that allows users to upload their own time series data and get forecasts using the model we built.
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