Oil & Gas Forecasting & Predictions using Time Series Analysis Last Updated: 2 months ago Mentor: Edvantage Learning
Oil & Gas Forecasting & Predictions using Time Series Analysis
₹14000/ $200 ₹15500/ $225
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Course Objectives:
Facing the dilemma between resource shortages and environmental destruction, numerous researches have been initiated within the field of oil & gas studies.

The analysis of Oil & Gas scenarios for future energy systems requires appropriate historical data. However, while more or less detailed data on energy production is often available, appropriate data on energy consumption is often scarce.

Time series analysis in the oil and gas sector involves systematically recording data points at regular intervals over a specified timeframe. This structured approach enables analysts to uncover trends, patterns, and variations in critical parameters, such as production volumes and exploration results. By studying the temporal evolution of key variables, professionals can make informed decisions, predict future trends, and optimize operational strategies for greater oil & gas efficiency and profitability.

Learning Objectives:

✓ Creates the opportunity to clean your data
✓ Time Series Forecasting Can Predict the future
✓ Time series analysis helps you identify platforms
✓ You can make the future performance of Oil estimations
✓ Helps you to detect anomaly detection of your problems

Prerequisites:
✓ Will teach from scratch, so no perquisites as such required
✓ Having knowledge of data visualization, graphs and charts will be added on advantage

Topics to Be covered:
Introduction to time series data, and how it is different from normal data. We are already doing forecasting in our real life.

Mathematics and Statistics relevant to forecasting

  • Lag features
  • Algebra, Calculus
  • Outlier Removal

Terminology of Time Series

  • Ts objects, Time plots & Seasonality
  • Periodicity & Trend
  • White Noise
  • Unit root & Smoothening

Introductory signal analysis

  • Fourier Transforms: Taking time series data into frequency domain
  • Recursion Plots
  • Which spike is an anomaly?

Getting Deeper into time series

  • Differencing 
  • ACF and PACF
  • Hypothetical tests
  • Time series Data Analysis

Thinking of time series problem as a regression problem

  • Statistical Models
  • Auto Regression
  • Moving Average
  • ARIMA

Deep Learning for time series

  • RNN
  • LSTM

Advanced (Optional)

  • Anomaly detection using Auto Encoders & Isolation Forest