HR Trainee

Time series forecasting is a method used to predict future values based on historical data points ordered chronologically. It analyzes patterns, trends, and seasonality within past data to estimate future outcomes, such as sales, weather, or stock prices. This technique is valuable for informed decision-making and planning.

Key Concepts:

• Time Series Data:
  

Data points collected and recorded at regular intervals over a period of time.

• Stationarity:
  

A key characteristic of time series data where statistical properties like mean and variance remain constant over time. Many forecasting methods assume or require stationary data.

• Trend:
  

The general direction (upward or downward) of the time series data over the long term.

• Seasonality:
  

Repeating patterns within the data at fixed intervals (e.g., daily, monthly, or yearly).

• Cyclical Patterns:
  

Non-seasonal fluctuations influenced by broader economic conditions.

• Forecasting Models:
  

Various techniqu

Requirements

Time series forecasting is a method used to predict future values based on historical data points ordered chronologically. It analyzes patterns, trends, and seasonality within past data to estimate future outcomes, such as sales, weather, or stock prices. This technique is valuable for informed decision-making and planning.

Benefits

Time series forecasting is a method used to predict future values based on historical data points ordered chronologically. It analyzes patterns, trends, and seasonality within past data to estimate future outcomes, such as sales, weather, or stock prices. This technique is valuable for informed decision-making and planning.

Key Concepts:

• Time Series Data:
  

Data points collected and recorded at regular intervals over a period of time.

• Stationarity:
  

A key characteristic of time series data where statistical properties like mean and variance remain constant over time. Many forecasting methods assume or require stationary data.

• Trend:
  

The general direction (upward or downward) of the time series data over the long term.

• Seasonality:
  

Repeating patterns within the data at fixed intervals (e.g., daily, monthly, or yearly).

• Cyclical Patterns:
  

Non-seasonal fluctuations influenced by broader economic conditions.

• Forecasting Models:
  

Various techniqu
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