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Exploratory Data Analysis of Time Series Data and Forecasting using Naïve Approach, Moving Average Method, Simple Exponential Smoothening, Holt’s Linear Trend Model, Holt’s Winter Model, ARIMA and SARIMAX models.
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README.md

Time-Series-Analysis-on-Transportation-Data

• Developed Hypothesis about factors that might affect the passenger count of transportation system and validated them with Exploratory Data Analysis.

• Created a function to check stationarity of Time Series Data and Prepared the data by making the non- stationary data to stationary data by first differencing.

• Divided the data into Train and Validation to and applied multiple Time Series Forecasting techniques like Naïve Approach, Moving Average Method, Simple Exponential Smoothening, Holt’s Linear Trend Model, Holt’s Winter Model, ARIMA and SARIMAX models.

• Predicted the traffic for the next 7 months using the SARIMAX model since it had the least RMSE.

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