1.1 Definition and characteristics of time series data

Time series data is a sequence of observations recorded at regular intervals, like hourly stock prices or daily temperature readings. It's characterized by temporal dependence, where current values are influenced by past ones, and often includes components like trends, seasonality, and cyclical patterns.

Understanding time series data is crucial for forecasting and uncovering underlying patterns in various fields. From finance to environmental studies, this type of data helps us analyze how variables change over time, making it a powerful tool for decision-making and trend analysis.

Introduction to Time Series Data

Definition of time series data

Top images from around the web for Definition of time series data

• 

  Time series forecasting - different to regular machine learning View original

  Is this image relevant?

• 

  Time Series Analysis View original

  Is this image relevant?

• 

  Time Series Analysis View original

  Is this image relevant?

• 

  Time series forecasting - different to regular machine learning View original

  Is this image relevant?

• 

  Time Series Analysis View original

  Is this image relevant?

1 of 3

Top images from around the web for Definition of time series data

• 

  Time series forecasting - different to regular machine learning View original

  Is this image relevant?

• 

  Time Series Analysis View original

  Is this image relevant?

• 

  Time Series Analysis View original

  Is this image relevant?

• 

  Time series forecasting - different to regular machine learning View original

  Is this image relevant?

• 

  Time Series Analysis View original

  Is this image relevant?

1 of 3

• Sequence of observations recorded at regular time intervals (hourly, daily, monthly)
• Each observation associated with a specific timestamp or date
• Key components include trend, seasonality, cyclical component, and irregularity or noise
  • Trend represents long-term increase or decrease over time
  • Seasonality refers to recurring patterns at fixed intervals (holidays, summer months)
  • Cyclical component captures patterns over longer periods without fixed frequency (business cycles)
  • Irregularity or noise encompasses random fluctuations not explained by other components

Characteristics of time series data

• Temporal dependence whereby current observations influenced by previous values
  • Crucial for forecasting and understanding underlying patterns
• Seasonality exhibits regular, predictable patterns recurring over fixed time intervals
  • Increased retail sales during holiday seasons
  • Higher electricity consumption in summer months
• Autocorrelation measures correlation between a variable's current and past values
  • Positive autocorrelation indicates high values followed by high values, low by low
  • Negative autocorrelation suggests high values likely followed by low values, and vice versa
• Stationarity assumes statistical properties remain constant over time
  • Non-stationary series may require transformations (differencing) to achieve stationarity
• Trend-cycle component represents overall long-term pattern
  • Trend refers to general direction (increasing or decreasing)
  • Cycle captures longer-term fluctuations around the trend
• Sampling frequency determines rate at which observations are recorded
  • High-frequency data collected at short intervals (hourly, daily)
  • Low-frequency data recorded at longer intervals (monthly, quarterly, annually)

Time series vs cross-sectional data

• Time series data consists of observations recorded over time for a single entity
  • Focuses on evolution of variables over time
  • Daily stock prices of a particular company over a year
• Cross-sectional data comprises observations collected at a single point in time across multiple entities
  • Focuses on relationships between variables at a specific moment
  • Income levels of individuals in a city surveyed on a specific date

Examples of time series data

• Finance and economics
  • Stock prices, exchange rates, GDP, inflation rates
• Environmental studies
  • Temperature measurements, air quality indices, sea level records
• Healthcare
  • Disease incidence rates, hospital admissions, patient vital signs
• Energy
  • Electricity consumption, oil prices, renewable energy production
• Social media and web analytics
  • User engagement metrics, website traffic, social media post interactions
• Meteorology
  • Weather variables (temperature, humidity, wind speed, precipitation)
• Epidemiology
  • Disease case counts, mortality rates, vaccination rates
• Transportation
  • Traffic volume, public transit ridership, flight passenger counts
• Retail and e-commerce
  • Sales figures, customer transactions, inventory levels
• Sensor data
  • Readings from IoT devices (smart meters, wearables, industrial sensors)