Author(s): Shrashti Singhal
Originally published on Towards AI.
The Comprehensive Guide- Part 1
Photo by Jon Tyson on Unsplash
This article is divided into three parts. Part 1 below:
Time series problems involve using historical data to make predictions about future events. These problems are commonly found in finance, economics, and engineering. Common techniques for solving time series problems include ARIMA, Exponential smoothing, and Time-series forecasting using Deep Learning techniques like LSTM or Prophet.
is a statistical model that analyzes and forecasts time series data. It combines autoregression (a model that uses the dependent relationship between an observation and some number of lagged observations) and a moving average model (a model that uses the dependency between… Read the full blog for free on Medium.
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Published via Towards AI