Introduction
A time series model is a set of data points ordered in time, where time is the independent variable. These models are used to analyze and forecast the future.
There are 3 main characteristics of time series data
Autocorrelation which is the similarity between observations as a function of the time lag between them
Seasonality refers to periodic fluctuations Seasonal patterns are recurring, predictable fluctuations over time, often related to calendar or seasonal events
Stationarity A time series is said to be stationary if its statistical properties don’t change over time. In other words, it has a constant mean and variance, and its covariance is independent of time
When developing a time series model
- you prepare your data. This involves handling missing values, outliers, and ensuring that your data is stationary. Stationarity means that the statistical properties (e.g., mean and variance) remain relatively constant over time (Data preprocessing)
- understand your data's characteristics. Visualizing time series data and conducting statistical tests can help identify trends, seasonality, and patterns
Time series models are categorized into several types, depending on their underlying assumptions and complexity. Some of the most popular models include
ARIMA (Auto Regressive Integrated Moving Average): A model that combines autoregressive and moving average components to capture temporal dependencies.
Exponential Smoothing Models: ETS (Error, Trend, Seasonality) models are designed to capture different components of time series data.
Prophet: Developed by Facebook, this model is designed for forecasting with daily observations displaying patterns on different time scales.
State Space Models: These models describe how observations are generated from underlying latent states, including Hidden Markov Models (HMMs).
After selecting your right model from the above, you go on and perform; model evaluation
forecasting
Seasonal Decomposition
Incorporating Exogenous Variables
Machine Learning Approaches
Cross-Validation and Hyperparameter Tuning
Model Deployment and Monitoring
Application of time series model
Time series models offer several applications i.e. determining patterns, forecasting future trends and determining abnormalities that make them suitable for a range of industries including;
- Healthcare; Time series models can be used to monitor the spread of diseases by observing how many people transmit a disease and how many people die after being infected
- Agriculture; Time series models take into account seasonal temperatures, the number of rainy days each month and other variables over the course of years, allowing agricultural workers to assess environmental conditions and ensure a successful harvest Finance; financial analysts can leverage time series models to
- record sales numbers for each month and predict potential stock market behavior
- Cyber security; IT and cybersecurity teams can develop patterns in user behavior with time series models, allowing them to be aware of when behavior doesn’t align with normal trends
- Retail; Retailers may apply time series models to study how other companies’ prices and the number of customer purchases change over time, helping them optimize prices.
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