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Time Series Forecasting

Steps

  1. Overview
  2. Pre-requisites
  3. Key Choices and Guidelines
  4. Implementation

1 Overview​

Description​

Time series forecasting is the process of analyzing time-ordered data points to predict future values. Time series data is a sequence of observations collected at successive points in time, typically at uniform intervals (e.g., daily stock prices, hourly temperature readings, monthly sales). Each data point is dependent on past values, making the temporal order crucial.

Expected Outcome​

The expected outcome is a model that captures the underlying patterns in historical data (like trends, seasonality, and noise) and uses these patterns to forecast future values.

Benefits​

  • Improved Decision-Making: Forecasting future trends enables data-driven decisions in areas like budgeting, staffing, and supply chain management.
  • Resource Optimization: Helps allocate resources more efficiently by predicting demand, reducing waste, and avoiding stockouts or overproduction.
  • Competitive Advantage: Businesses that anticipate market trends and customer behavior gain a strategic edge over competitors.