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4 Reasons Why You Should Use Deep Learning For Time Series Forecasting

4 Reasons Why You Should Use Deep Learning For Time Series Forecasting

Disha Misal

 Time Series forecasting is an important area of Machine Learning. It is important because there are so many prediction problems that involve a time component. However, while the time component adds additional information, it also makes time series problems more difficult to handle compared to many other prediction tasks. Deep Learning has plenty of applications in the world of statistical analysis. One of the areas with an environment for its applicability is the time series.

Here are 5 reasons to add Deep Learning to your Time Series analysis:

1. Easy-to-extract features

The Deep Neural Networks of deep learning have the ability to reduce the need for feature engineering processes, data scaling procedures and stationary data, which is required in time series forecasting. These networks can learn on their own and on training can by themselves extract features from the raw input data, which is what time series forecasting demands. On a model of neural networks, a sequence of objects can be treated as a one-dimensional image. This model is what the neural network can refine into the most related elements. Neural networks are therefore widely used in the time series forecasting.

2. Good at extracting patterns easily:

Time series forecasting is basically looking for patterns and eventually spanning them over long sequences. Recurrent Neural Networks (RNN) have high applicability in Time Series forecasting. Each neuron in an RNN is capable to maintain information of the previous input using its internal memory. This makes them good with sequential data and hence in time series. RNN has loops that allow information to be carried across neurons while reading in the input. They help with grasping the temporal dependence from the data and they can easily identify what previous observations are important and how they are relevant to the current forecasting. It can learn what information is important from the input is important for the mapping and can dynamically change this context as needed.

3. Easy to predict from training data:

The Long Short Memory Network (LSTM) is a neural network that can make predictions according to the previously encountered data. LTSM is very popular in time series, apart from its applications in other domains. Data can be represented at different points in time using deep learning models like a random forest, gradient boosting regressor and time delay neural networks.

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4. Support for multiple Inputs and Outputs:

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Time Series forecasting often requires dealing with multiple inputs and forecasting multiple time steps. Again, a neural network can be applied which allows for a fixed/multiple a number of inputs for a mapping function. They support multivariate inputs and thereby supporting multivariate forecasting. Complex Time Series evaluation requires multivariate and multi-step forecasting. Neural networks also support an arbitrary number of output values as well to help with multiple outputs in time series forecasting.

In Conclusion

Deep Learning methods offers a lot of promise for Time Series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With their quality of extracting patterns from the input data for long durations, they have the perfect applicability in forecasting. They can, therefore, deal with large amounts of data, multiple, complex variables and multi-step actions, which is what time series forecasting demands.

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