Chapter 1
Visualizing the time series data is an essential part of understanding its characteristics, such as trend, seasonality, and noise.
Decomposing a time series into its components, such as trend, seasonality, and residual, can help in understanding the underlying patterns.
Understanding the concept of stationarity in time series data is important for building accurate forecasting models.
Autocorrelation refers to the correlation of a time series with its own past and future values. It is essential for understanding the underlying patterns in time series data.
Autoregressive integrated moving average (ARIMA) models are commonly used for time series forecasting. Understanding how to build and tune these models is important for accurate predictions.
Exponential smoothing is a popular class of time series forecasting methods that can handle time series data with trend and seasonality. Understanding the various types of exponential smoothing models is important for accurate predictions.
Prophet is an open-source time series forecasting package developed by Facebook. It is designed to handle time series data with multiple seasonality and has gained popularity in the industry.