# What is a time series?

A time series is a set of observations ordered in time usually collected at regular intervals. Contrasting with usual regressions, the principle behind causal forecasting is that the value that has to be predicted is dependant on the input features (causal factors). In time series forecasting, the to be predicted value is expected to follow a certain pattern over time.

## Models for solving time series problems

- Simple Exponential Smoothing: approximate the time series with an exponentional function
- Trend-Corrected Exponential Smoothing (Holt‘s Method): exponential smoothing that also models the trend
- Trend- and Seasonality-Corrected Exponential Smoothing (Holt-Winter‘s Method): exponential smoothing that also models trend and seasonality
- Time Series Decomposition: decomposed a time series into the four components trend, seasonal variation, cycling varation and irregular component
- Autoregressive models: similar to multiple linear regression, except that the dependent variable y_t depends on its own previous values rather than other independent variables.
- Deep learning approaches (RNN, LSTM, etc.)

## Problems with using trees for solving time series problems

Random Forest models are not able to extrapolate time series data and understand increasing/decreasing trends. It will provide us with average data points if the validation data has values greater than the training data points.