Source: thenewstack.io
What Is Time Series Forecasting?
How do weather forecasters predict tomorrow’s weather, or stock market analysts analyze future market trends? Implementing time series forecasting techniques can empower businesses to make informed decisions, anticipate customer demands well in advance, and gain a competitive edge in their respective markets.
Captures non-linear patterns and relationships in the data, making it useful for modeling complex time series data Can be applied to both stationary and non-stationary time series data, including data with trends and seasonal patterns Well-suited for short-term forecasting, especially when the time series data exhibit a high degree of autocorrelation Provides diagnostic statistics that can be used to evaluate how well the model fits the task at hand, and the quality of the forecasts.
https://medium.com/analytics-vidhya/time-series-forecasting-using-tbats-model-ce8c429442a9 (trigonometric seasonality, https://onlinestatbook.com/2/transformations/box-cox.html, ARMA errors, trend, and seasonal components) is a state-of-the-art time series forecasting model that extends the basic exponential smoothing framework.
Seasonal Decomposition https://towardsdatascience.com/different-types-of-time-series-decomposition-396c09f92693 is a method that separates the time series data into its trend, seasonal and residual components.
Captures non-linear patterns and relationships in the data, making it useful for modeling complex time series data Can be applied to both stationary and non-stationary time series data, including data with trends and seasonal patterns Well-suited for short-term forecasting, especially when the time series data exhibit a high degree of autocorrelation Provides diagnostic statistics that can be used to evaluate how well the model fits the task at hand, and the quality of the forecasts.
https://medium.com/analytics-vidhya/time-series-forecasting-using-tbats-model-ce8c429442a9 (trigonometric seasonality, https://onlinestatbook.com/2/transformations/box-cox.html, ARMA errors, trend, and seasonal components) is a state-of-the-art time series forecasting model that extends the basic exponential smoothing framework.
Seasonal Decomposition https://towardsdatascience.com/different-types-of-time-series-decomposition-396c09f92693 is a method that separates the time series data into its trend, seasonal and residual components.
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