Time Series Forecasting with TensorFlow: Components, Models & Steps

Time Series Forecasting with TensorFlow: Components, Models & Steps

What is Time Series Forecasting? Time Series Forecasting with TensorFlow: Components, Models & Steps. All are answered in this article.

What is Time Series Forecasting?

Do you know that Asia Pacific is the largest growing deep learning market globally with a CAGR (Compound Annual Growth Rate) of over 40%? Time series TensorFlow prediction is an important concept in deep learning & ML. All the deep learning/ML models have a respective dataset that is a collection of observations. These observations often include a time component. Time series arranges the observations sequentially in time, thus adding a new dimension to the dataset, i.e., time. 

One may think about what will be achieved by increasing the dimensionality of their dataset? Well, adding a time dimension to your dataset will create a detailed level of dependence among observations. The outcome is then predicted with the help of time series forecasting of prior observations.

The use of time-series data (historical data) for predicting the future is called time series forecasting. One should also ensure that the future should be completely unknown and can only be predicted based on historical data.

The time-series data is analysed to develop models that describe the data effectively. The time series is decomposed into several components for developing apt models. Once the models that best describe the historical data are developed, it is then used for forecasting. One should not confuse time series analysis with time series forecasting as the latter comes into action only when the former is completed.

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Time Series Forecasting with TensorFlow: Components, Models & Steps

Time series TensorFlow forecasting is an important aspect of ML (Machine Learning) and deep learning. Let's explore it now.