Time Series Forecasting with TensorFlow: Components, Models & Steps

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.

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.

Time Series Components

Before knowing about time series TensorFlow forecasting, one should be familiar with the component of a time series. A time series is decomposed into four components during time series analysis. These components help in understanding the dataset properly. The four components of a time series are as follows:

  • Trends – The behaviour of a series/dataset over time is explained by trends. The increasing and decreasing behaviour of a time series is explained by trends.
  • Level – Level is the base value of the time series, considering that the representation of the series is a straight line. Many experts also define level as the average value of the series.
  • Seasonality – The behaviours of the series that are repeated over time are called seasonality. Some experts also term seasonality as periodic fluctuations.
  • Noise – Each dataset contains some data points/outliers that the time series model cannot explain. These datasets possess unpredictable properties and cannot be mapped via time series analysis/forecasting.

One should note that a time series must have a level and some noise. However, trends and seasonality in a time series are optional.

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