Business Objective: Oil price may fluctuate time to time based on more factors technical economical and natural as well as political so the forecasting may not be influenced by these some unexpected scenarios like Geopolitical issues (e.g.: War and Oil price Cap)
Data Set Details: Oil is a product that goes completely in a different direction for a single market event as the oil prices are rarely based on real-time data, instead, it is driven by externalities making our attempt to forecast it even more challenging .As the economy will be highly affected by oil prices our model will help to understand the pattern in prices to help the customers and businesses to make smart decisions!
In this Project for Time Series Analysis and Forecasting
Each step taken like EDA, Feature Engineering, Model Building, Model Evaluation and Prediction table, and Deployment. Explaining which model to select on basis of metrics like RMSE, MAPE and MAE value for each model. Finally explaining which model we will use for Forecasting.
Better accuracy in short-term forecasting is required for intermediate planning for the target to reduce CO2 emissions. High stake climate change conventions need accurate predictions of the future emission growth path of the participating organization to make informed decisions. Exponential techniques, Linear statistical modeling,Autoregressive models and LSTM model are used to forecast the emissions and the best model will be selected on these basis
- 1.) Minimum error
- 2.) Low bias and low variance trade off
This project is part of Data Science internship at AiVariant
| Name | 💌Email Adress |
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| Vaibhav Dongre | [email protected] |
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