Stationary Process | Strict Stationarity & Weak Stationarity || Time Series

In this video you will learn what is a stationary process and what is strict and weak stationary condition in the context of times series analysis
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[@rakshitsinha8665]

Genuinely good stuff mate

[@KishanIndiaDev]

Thank you... Clear explanation

[@90hotshot]

thank you so much

[@89rmehra]

Thank you. This video was very helpful :)

[@raywang0519]

it is so helpful

[@natty3036]

Thanks for your video, removed the fog

[@AnalyticsUniversity]

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[@gitaghosh2657]

No, all strictly stationary processes are not weakly stationary. For example, when a series comes from a Cauchy population, moments don't exist. Therefore, even if the process is strictly stationary it isn't weakly stationary.

[@saunokchakrabarty8384]

Provided moments exist and are finite, yes, they are. Strict stationarity implies that the joint distribution isn't changing over time. If the joint distribution doesn't change, then the mean and autocovariance (all moments in general) will also be unchanged over time, implying weak stationarity.

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[@sarahyuan8635]

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[@AnwarHussain-fe7us]

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[@GregThatcher]

Thanks!

[@mustafizurrahman5699]

Excellent

[@rachnabhalla4693]

I am sorry, but I don't understand yt and yt-s are two different variables in a time series data? If yes, could you provide an example? The stock price example doesn't seem clear.

[@zaranikdel]

Let's say y is a random process, y_t is a random variable in y at time t which it's value comes from let's say pdf1 (probability density function), y_{t+s} is another random variable in random process y at time "t+s" which it's value come from pdf2. The probability of seeing y_t and y_{t+s} together is a joint probability of both of them. It's like you say I have a coin and a dice and at each time step I roll a coin and next time step I roll a dice, the probability of observing a "head" and a "2" is a joint probability of coin and dice.

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Nice