Time series prediction problems are a difficult type of predictive modeling problem.
Unlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables.
A powerful type of neural network designed to handle sequence dependence is called recurrent neural networks. The Long Short-Term Memory network or LSTM network is a type of recurrent neural network used in deep learning because very large architectures can be successfully trained.
Why Time Series Forecasting ?
Whenever we have to make a decision under uncertainty, we all make our own forecasts. Even if we are not thinking that we are forecasting, our choices will be decided by our anticipation of results of our actions. Delays and indecision are the main reasons of failure. As a result of realizing 'Time is Money', 'Time Series Forecasting' is being applied in dynamic decision making problems. Time Series Analysis & Forecasting has been used by a wide range of businesses from managerial decisions, stock market analysis, sales, policy research, weather forecasting to astronomy. Deep learning may not be the best solution for all time series forecasting problems, but for those problems where classical methods fail and machine learning methods require elaborate feature engineering, deep learning methods can be used with great success.
- Trend
- Seasonality
- Irregularity
- Cyclic
Time series is said to be stationary if its statistical properties such as mean, variance remain constant over time. A model that shows stationarity is one that shows there is constancy to the data. Most economic and market data show trends, so the purpose of differencing is to remove any trends or seasonal structures. If not Stationary, It has to perform transformations on the data to make it Stationary. Whenever we have to make a decision under uncertainty, we all make our own forecasts. Even if we are not thinking that we are forecasting, our choices will be decided by our anticipation of results of our actions. Delays and indecision are the main reasons of failure. As a result of realizing 'Time is Money', 'Time Series Forecasting' is being applied in dynamic decision making problems. Time Series Analysis & Forecasting has been used by a wide range of businesses from managerial decisions, stock market analysis, sales, policy research, weather forecasting to astronomy. Deep learning may not be the best solution for all time series forecasting problems, but for those problems where classical methods fail and machine learning methods require elaborate feature engineering, deep learning methods can be used with great success
Predictions are made for new data when the actual outcome may not be known until some future date. The future is being predicted, but all prior observations are almost always treated equally. Perhaps with some very minor temporal dynamics to overcome the idea of “concept drift” such as only using the last year of observations rather than all data available.