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What is Understanding Non-Stationary Time Series about?

Financial time series data like asset prices, exchange rates, and GDP are often non-stationary and need to be transformed before being used for analysis. There are different types of non-stationary processes including random walks, trends, and combinations. To make the data stationary, random walks can be differenced by subtracting past values, while trends can be removed through detrending without losing observations. Sometimes a series has both stochastic and deterministic trends, requiring both differenc

Author

Akriti Singh

Language

EN