A time-series data is a series of data points or observations recorded at different or regular time intervals. In general, a time series is a sequence of data points taken at equally spaced time intervals. The frequency of recorded data points may be hourly, daily, weekly, monthly, quarterly or annually.
Time-Series Forecasting is the process of using a statistical model to predict future values of a time-series based on past results.
A time series analysis encompasses statistical methods for analyzing time series data. These methods enable us to extract meaningful statistics, patterns and other characteristics of the data. Time series are visualized with the help of line charts. So, time series analysis involves understanding inherent aspects of the time series data so that we can create meaningful and accurate forecasts.
Applications of time series are used in statistics, finance or business applications. A very common example of time series data is the daily closing value of the stock index like NASDAQ or Dow Jones. Other common applications of time series are sales and demand forecasting, weather forecasting, econometrics, signal processing, pattern recognition and earthquake prediction.
Notebook 1 contains the Theory behind Time Series forcasting and the general know how.
Notebook 2 contains an ARIMA and AUTO ARIMA model for forecasting Flight Passenger Time Series Data
Notebook 2 also contains a SARIMA and SARIMAX model for forecasting Drug Company Data