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Datasets Summary

1. airline_passengers.csv

  • Description: This dataset contains monthly totals of international airline passengers from 1949 to 1960.
  • Shape: 144 rows, 2 columns.
  • Columns:
    • Month: The month of the record (YYYY-MM format).
    • Thousands of Passengers: The number of passengers (in thousands).

2. Alcohol_Sales.csv

  • Description: This dataset represents monthly alcohol sales.
  • Shape: 325 rows, 2 columns.
  • Columns:
    • DATE: The date of the record (YYYY-MM-DD format).
    • S4248SM144NCEN: The sales figure (numeric).

3. DailyTotalFemaleBirths.csv

  • Description: Daily total female births in California in 1959.
  • Shape: 365 rows, 2 columns.
  • Columns:
    • Date: The date of the record (MM/DD/YYYY format).
    • Births: The number of births (numeric).

4. RestaurantVisitors.csv

  • Description: This dataset tracks daily visitors to four different restaurants, along with whether the day was a holiday.
  • Shape: 517 rows, 9 columns.
  • Columns:
    • date: The date of the record (MM/DD/YYYY format).
    • weekday: The day of the week.
    • holiday: Indicator if the day is a holiday (1 for holiday, 0 otherwise).
    • holiday_name: Name of the holiday, if applicable.
    • rest1 to rest4: Visitor count for each restaurant (numeric).
    • total: Total visitors across all restaurants (numeric).

5. household_power_consumption.txt

  • Description:The Household Power Consumption dataset is a multivariate time series dataset that describes the electricity consumption for a single household over four years. The data was collected between December 2006 and November 2010 and observations of power consumption within the household were collected every minute.
  • Shape: 2075259 rows and 7 columns.
  • Columns:
    • global active power: The total active power consumed by the household (kilowatts).
    • global reactive power: The total reactive power consumed by the household (kilowatts). � voltage: Average voltage (volts).
    • global intensity: Average current intensity (amps).
    • sub metering 1: Active energy for kitchen (watt-hours of active energy).
    • sub metering 2: Active energy for laundry (watt-hours of active energy).
    • sub metering 3: Active energy for climate control systems (watt-hours of active energy).

Each dataset can be used to illustrate different techniques and models in time series analysis and forecasting, ranging from basic methods to more advanced deep learning approaches. The variety in these datasets (from sales data to daily counts) provides a comprehensive resource for demonstrating different time series scenarios and methods.