Overview
A time series is a series of measured values with a timestamp attached to each measurement. For example, a series of
equity asset prices may be taken at the close of the market every day. The collection of prices would then form a time series.
Random Walk
The random walk represents a typical time series.
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Modeling
- Definitions and Theorems - gives a mathematical description of time series and some important theorems
- Models - is the process of building a mathematical model to describe a time series, often for the purpose of forecasting.
- Signals and Systems
Data Preparation
Outlines some of the issues surrounding preparing a dataset for a time series analysis.
- Building a Single Dataset : Time series data often comes from multiple datasets. Prior to analyzing the data, it is often necessary to combine the data into a single set.
- Data Cleansing
- Time Series Features/Transformations :
Most time series datasets require a set of data transformations applied in order
to extract features of the data that can then be modeled. The process of
feature extraction is a core process of statistical/analysis
and machine learning. However, due to the unique nature of
time series data, the process of extracting features from a time series is unique to itself.
Sample Features include:
- Differences including returns
- Volatility
- Moving Averages and Filters