Chapter 15: Time Series Forecasting with PyTorch
Abstract : Time series forecasting is a statistical and machine learning method used to predict future values based on historical, time-stamped data . It involves analyzing patterns like trends, seasonality, and cyclical movements in past data to make informed estimations about future outcomes, and is used in fields like sales, weather, and finance. Modern techniques include deep learning models like neural networks, and even generative AI like time series transformers , which can handle complex and nonlinear relationships. Key concepts Trend: The overall long-term direction of the data, either upward or downward. Seasonality: Regular, repeating patterns that occur within a fixed period, such as daily, weekly, or yearly cycles. Cyclical: Variations that occur over longer periods, greater than a year, and are often influenced by economic conditions. Irregular (or Noise): Random fluctuations in the data that are ...