Forecasting is a common task for most organizations. To handle the complexity of forecasting many forecasting techniques have been developed in recent years. Traditional statistical methods are good for stable markets, but they fail when patterns and trends are not consistent.
Here are 3 reasons why you should use deep learning for forecasting:
- Easy to extract features: Deep learning reduces the need for extensive feature engineering, data normalization and stationarity in time series. Deep neural networks can extract features on their own during the learning process.
- Extract patterns easily: Time series forecasting is basically looking for patterns and eventually spanning them over long sequences. Recurrent Neural Networks (RNN) do this well. 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. They can capture the temporal dependence from the data and can easily figure out what previous observations are important and how they are relevant to the current forecasting.
- Support for multiple inputs and outputs: They support multivariate inputs and thereby supporting multivariate forecasting. Complex Time Series evaluation requires multivariate and multi-step forecasting.
Here’s a quick demo of how we put this concept into practice in the context of forecasting sales.
Have a look at our Sales Forecasting report below.
To download our slide deck on Demand Forecasting, click
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