Time Series Forecasting with Machine Learning:
Time series forecasting with machine learning involves using historical time-stamped data to make predictions about future values or trends. This approach is widely used in various domains, including finance, sales forecasting, energy consumption prediction, and weather forecasting. Here's an overview of the process:
1. Data Preparation:
Collect Data: Gather historical time series data, which typically consists of sequential observations recorded at regular intervals (e.g., hourly, daily, monthly).
Preprocess Data: Clean the data by handling missing values, outliers, and inconsistencies. Ensure that the data is in a suitable format for analysis, such as a pandas DataFrame in Python.