Data Preprocessing Questions
The common techniques used for data smoothing are:
1. Moving Average: It involves calculating the average of a fixed number of adjacent data points to smooth out fluctuations and highlight trends.
2. Exponential Smoothing: It assigns exponentially decreasing weights to older data points, giving more importance to recent observations. It is useful for capturing short-term trends and removing noise.
3. Savitzky-Golay Filter: It applies a weighted polynomial regression to a sliding window of data points, effectively smoothing the data while preserving important features.
4. Lowess (Locally Weighted Scatterplot Smoothing): It fits a regression line to a subset of nearby data points, giving more weight to points closer to the target point. It is particularly useful for handling non-linear relationships.
5. Kernel Smoothing: It uses a kernel function to assign weights to nearby data points, with the weights decreasing as the distance from the target point increases. It is effective in smoothing data with irregular patterns.
6. Fourier Transform: It decomposes the time series data into a combination of sine and cosine waves, allowing for the removal of high-frequency noise and extraction of underlying trends.
These techniques help in reducing noise, removing outliers, and revealing underlying patterns in the data, making it more suitable for analysis and modeling.