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Introduction to Time-Series Forecasting

Time-Series Forecasting with TensorFlow 2.0

Introduction to Time-Series Forecasting

Time-series forecasting refers to the use of a machine learning model to predict future values based on previously observed values. Though this definition might somewhat remind you of regression models, time-series forecasting is applied to forecast data that are ordered by time; for example, stock prices by year.

stock data visualization
Figure: Example of data ordered by date on which time-series forecasting can be applied.

Time-series forecasting is one of the most used applications of Deep Learning in the modern world. Quantitative analysts use it to predict the value of stocks, business professionals use it to forecast their sales, and government agencies use it to forecast resource consumption (energy, water, etc.).

Head on to the next lesson on ‘Getting started with Time-series Data‘ to get started with building our time-series forecasting model using TensorFlow 2.0.

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