What are the Benefits of Using Deep Learning to Analyze Time Series?

The use of deep learning for time series analysis involves the following benefits:

• Trouble-free extract features: Deep neural networks reduce the necessity of the data scaling process and stationary data and feature engineering procedures which are necessary for time-series forecasting. These neural networks can learn by themselves, if proper training is provided, they can extract features from the raw input data without any difficulty.
• Not difficult to predict from training data: Data can be represented at several points in time using many deep learning models including gradient booster regressor, random forest, and time-delay neural networks.

Advantages of Time Series Analysis: 

• The time-series data will lead to arduous, and complicated calculations because of its nature which in turn makes forecasting difficult. With the help of Python and R languages, analysts can create and tune perfect time series forecasts with minimal effort.
• Time series models have fewer assumptions and are stable. It means in case a large and unpredicted event occurs; the model can provide valuable insight to solutions throughout the event.

Disadvantages:

• The major disadvantage is that time series analysis is expensive because forecasts are derived from historical data patterns that are necessary to predict the upcoming market behaviour.

Time series analysis is mainly used to explain, describe, and predict changes via the time of chosen variables. Many companies use time series forecasting, and analysis to develop business strategies. These techniques help businesses in measuring, tracking, defining, and predicting business trends.  Time series analysis provides you with an opportunity to know the impact of the past over the future.