Does it classify as Machine Learning?Ask Question Asked5 years, 6 months agoModified2 days agoViewed198 times Asked5 years, 6 months ago 1$\begingroup$I have a gaussian distributed time series ($X_t$) with some parameters in my experiment. Suppose I want to know the mean$\mu$. If I define another time series$Y_t$such that$Y_t=X_t-a$for all$t$. Now say I vary this parameter$a$and generate altogether different time series for each$a$, say$Y_t(a)$. I look at the mean of$Y_t$for each$a$. The value of a, where I get the mean of$Y_t$closest to$0$, will be my estimate of$\mu$. Say I will eventually use this learnt value of$\mu$to generate$Y_t$as my final goal. Can this be called ML? I am using some training data of$X_t$to learn about its parameter and then using test data of$X_t$to generate$Y_t$.Now why am I working so hard on this simple problem? Well, actually I am not. I am doing something else, which will have lots of parameters in the time series and will be used to generate other time series after similar parameter extraction. That will be too complicated to discuss here. I just wanted to clear my basics using an over-simplified example.machine-learningclassificationterminologytime-seriesShareImprove this questionFolloweditedAug 22, 2022 at 18:24desertnaut1,0211111 silver badges1919 bronze badgesaskedApr 25, 2020 at 13:18Raunak Dey1122 bronze badges$\endgroup$2$\begingroup$I don't understand what you mean by "gaussian distributed time series". Can you precisely define it? Are you saying that each element of the time series is sampled from 1. the same or 2. possibly different Gaussian? In general, I don't fully understand your problem. Do you have the original time series $X_t$? If not, do you want to estimate it from "noisy" similar time series? I don't understand how subtracting $a$ from $X_t$ gives you any insight. Also, I don't understand how you are going to generate $Y_t$ if you don't have $X_t$. Are you assuming that $Y_t$ is $X_t -a$, for some $a$?$\endgroup$nbro–nbro2020-04-25 23:44:40 +00:00CommentedApr 25, 2020 at 23:44$\begingroup$@nbro Thanks for your response. The time series $X_t$ is given, and we know it has a Gaussian distribution, (histogram: Gaussian). So each point in $X_t$ belongs to a gaussian distribution with the same mean and variance. Now if you subtract a from each point in $X_t$ to construct a different series $Y_t$ the mean of the gaussian shifts. For some $a$, the constructed $Y_t$ has 0 mean. What I am asking if you vary $a$ and try to see for what $a$ this is achieved, then use that correctly found a, to generate more data points of $Y_t$, from $X_t$ can I call it as ML?$\endgroup$Raunak Dey–Raunak Dey2020-04-27 17:40:18 +00:00CommentedApr 27, 2020 at 17:40Add a comment|1 Answer1Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first)0$\begingroup$Does it classify as Machine Learning?If I understand the problem correctly, my opinion isyes, this is machine learning. In fact, I think it is just linear regression for finding the equation of ahorizontal liney(t) = athat fits the the datasety(t) = Xt.ShareImprove this answerFollowansweredOct 24, 2022 at 2:13Snehal Patel1,05711 gold badge55 silver badges2727 bronze badges$\endgroup$Add a comment|You mustlog into answer this question.Start asking to get answersFind the answer to your question by asking.Ask questionExplore related questionsmachine-learningclassificationterminologytime-seriesSee similar questions with these tags. 1$\begingroup$I have a gaussian distributed time series ($X_t$) with some parameters in my experiment. Suppose I want to know the mean$\mu$. If I define another time series$Y_t$such that$Y_t=X_t-a$for all$t$. Now say I vary this parameter$a$and generate altogether different time series for each$a$, say$Y_t(a)$. I look at the mean of$Y_t$for each$a$. The value of a, where I get the mean of$Y_t$closest to$0$, will be my estimate of$\mu$. Say I will eventually use this learnt value of$\mu$to generate$Y_t$as my final goal. Can this be called ML? I am using some training data of$X_t$to learn about its parameter and then using test data of$X_t$to generate$Y_t$.Now why am I working so hard on this simple problem? Well, actually I am not. I am doing something else, which will have lots of parameters in the time series and will be used to generate other time series after similar parameter extraction. That will be too complicated to discuss here. I just wanted to clear my basics using an over-simplified example.machine-learningclassificationterminologytime-seriesShareImprove this questionFolloweditedAug 22, 2022 at 18:24desertnaut1,0211111 silver badges1919 bronze badgesaskedApr 25, 2020 at 13:18Raunak Dey1122 bronze badges$\endgroup$2$\begingroup$I don't understand what you mean by "gaussian distributed time series". Can you precisely define it? Are you saying that each element of the time series is sampled from 1. the same or 2. possibly different Gaussian? In general, I don't fully understand your problem. Do you have the original time series $X_t$? If not, do you want to estimate it from "noisy" similar time series? I don't understand how subtracting $a$ from $X_t$ gives you any insight. Also, I don't understand how you are going to generate $Y_t$ if you don't have $X_t$. Are you assuming that $Y_t$ is $X_t -a$, for some $a$?$\endgroup$nbro–nbro2020-04-25 23:44:40 +00:00CommentedApr 25, 2020 at 23:44$\begingroup$@nbro Thanks for your response. The time series $X_t$ is given, and we know it has a Gaussian distribution, (histogram: Gaussian). So each point in $X_t$ belongs to a gaussian distribution with the same mean and variance. Now if you subtract a from each point in $X_t$ to construct a different series $Y_t$ the mean of the gaussian shifts. For some $a$, the constructed $Y_t$ has 0 mean. What I am asking if you vary $a$ and try to see for what $a$ this is achieved, then use that correctly found a, to generate more data points of $Y_t$, from $X_t$ can I call it as ML?$\endgroup$Raunak Dey–Raunak Dey2020-04-27 17:40:18 +00:00CommentedApr 27, 2020 at 17:40Add a comment| 1$\begingroup$I have a gaussian distributed time series ($X_t$) with some parameters in my experiment. Suppose I want to know the mean$\mu$. If I define another time series$Y_t$such that$Y_t=X_t-a$for all$t$. Now say I vary this parameter$a$and generate altogether different time series for each$a$, say$Y_t(a)$. I look at the mean of$Y_t$for each$a$. The value of a, where I get the mean of$Y_t$closest to$0$, will be my estimate of$\mu$. Say I will eventually use this learnt value of$\mu$to generate$Y_t$as my final goal. Can this be called ML? I am using some training data of$X_t$to learn about its parameter and then using test data of$X_t$to generate$Y_t$.Now why am I working so hard on this simple problem? Well, actually I am not. I am doing something else, which will have lots of parameters in the time series and will be used to generate other time series after similar parameter extraction. That will be too complicated to discuss here. I just wanted to clear my basics using an over-simplified example.machine-learningclassificationterminologytime-seriesShareImprove this questionFolloweditedAug 22, 2022 at 18:24desertnaut1,0211111 silver badges1919 bronze badgesaskedApr 25, 2020 at 13:18Raunak Dey1122 bronze badges$\endgroup$2$\begingroup$I don't understand what you mean by "gaussian distributed time series". Can you precisely define it? Are you saying that each element of the time series is sampled from 1. the same or 2. possibly different Gaussian? In general, I don't fully understand your problem. Do you have the original time series $X_t$? If not, do you want to estimate it from "noisy" similar time series? I don't understand how subtracting $a$ from $X_t$ gives you any insight. Also, I don't understand how you are going to generate $Y_t$ if you don't have $X_t$. Are you assuming that $Y_t$ is $X_t -a$, for some $a$?$\endgroup$nbro–nbro2020-04-25 23:44:40 +00:00CommentedApr 25, 2020 at 23:44$\begingroup$@nbro Thanks for your response. The time series $X_t$ is given, and we know it has a Gaussian distribution, (histogram: Gaussian). So each point in $X_t$ belongs to a gaussian distribution with the same mean and variance. Now if you subtract a from each point in $X_t$ to construct a different series $Y_t$ the mean of the gaussian shifts. For some $a$, the constructed $Y_t$ has 0 mean. What I am asking if you vary $a$ and try to see for what $a$ this is achieved, then use that correctly found a, to generate more data points of $Y_t$, from $X_t$ can I call it as ML?$\endgroup$Raunak Dey–Raunak Dey2020-04-27 17:40:18 +00:00CommentedApr 27, 2020 at 17:40Add a comment| $\begingroup$I have a gaussian distributed time series ($X_t$) with some parameters in my experiment. Suppose I want to know the mean$\mu$. If I define another time series$Y_t$such that$Y_t=X_t-a$for all$t$. Now say I vary this parameter$a$and generate altogether different time series for each$a$, say$Y_t(a)$. I look at the mean of$Y_t$for each$a$. The value of a, where I get the mean of$Y_t$closest to$0$, will be my estimate of$\mu$. Say I will eventually use this learnt value of$\mu$to generate$Y_t$as my final goal. Can this be called ML? I am using some training data of$X_t$to learn about its parameter and then using test data of$X_t$to generate$Y_t$.Now why am I working so hard on this simple problem? Well, actually I am not. I am doing something else, which will have lots of parameters in the time series and will be used to generate other time series after similar parameter extraction. That will be too complicated to discuss here. I just wanted to clear my basics using an over-simplified example.machine-learningclassificationterminologytime-seriesShareImprove this questionFolloweditedAug 22, 2022 at 18:24desertnaut1,0211111 silver badges1919 bronze badgesaskedApr 25, 2020 at 13:18Raunak Dey1122 bronze badges$\endgroup$ I have a gaussian distributed time series ($X_t$) with some parameters in my experiment. Suppose I want to know the mean$\mu$. If I define another time series$Y_t$such that$Y_t=X_t-a$for all$t$. Now say I vary this parameter$a$and generate altogether different time series for each$a$, say$Y_t(a)$. I look at the mean of$Y_t$for each$a$. The value of a, where I get the mean of$Y_t$closest to$0$, will be my estimate of$\mu$. Say I will eventually use this learnt value of$\mu$to generate$Y_t$as my final goal. Can this be called ML? I am using some training data of$X_t$to learn about its parameter and then using test data of$X_t$to generate$Y_t$.Now why am I working so hard on this simple problem? Well, actually I am not. I am doing something else, which will have lots of parameters in the time series and will be used to generate other time series after similar parameter extraction. That will be too complicated to discuss here. I just wanted to clear my basics using an over-simplified example. I have a gaussian distributed time series ($X_t$) with some parameters in my experiment. Suppose I want to know the mean$\mu$. If I define another time series$Y_t$such that$Y_t=X_t-a$for all$t$. Now say I vary this parameter$a$and generate altogether different time series for each$a$, say$Y_t(a)$. I look at the mean of$Y_t$for each$a$. The value of a, where I get the mean of$Y_t$closest to$0$, will be my estimate of$\mu$. Say I will eventually use this learnt value of$\mu$to generate$Y_t$as my final goal. Can this be called ML? I am using some training data of$X_t$to learn about its parameter and then using test data of$X_t$to generate$Y_t$. Now why am I working so hard on this simple problem? Well, actually I am not. I am doing something else, which will have lots of parameters in the time series and will be used to generate other time series after similar parameter extraction. That will be too complicated to discuss here. I just wanted to clear my basics using an over-simplified example. machine-learningclassificationterminologytime-series machine-learningclassificationterminologytime-series machine-learningclassificationterminologytime-series ShareImprove this questionFolloweditedAug 22, 2022 at 18:24desertnaut1,0211111 silver badges1919 bronze badgesaskedApr 25, 2020 at 13:18Raunak Dey1122 bronze badges ShareImprove this questionFolloweditedAug 22, 2022 at 18:24desertnaut1,0211111 silver badges1919 bronze badgesaskedApr 25, 2020 at 13:18Raunak Dey1122 bronze badges ShareImprove this questionFollow ShareImprove this questionFollow ShareImprove this questionFollow Improve this question editedAug 22, 2022 at 18:24desertnaut1,0211111 silver badges1919 bronze badges editedAug 22, 2022 at 18:24desertnaut1,0211111 silver badges1919 bronze badges editedAug 22, 2022 at 18:24 editedAug 22, 2022 at 18:24 desertnaut1,0211111 silver badges1919 bronze badges 1,0211111 silver badges1919 bronze badges askedApr 25, 2020 at 13:18Raunak Dey1122 bronze badges askedApr 25, 2020 at 13:18Raunak Dey1122 bronze badges askedApr 25, 2020 at 13:18 askedApr 25, 2020 at 13:18 Raunak Dey1122 bronze badges $\begingroup$I don't understand what you mean by "gaussian distributed time series". Can you precisely define it? Are you saying that each element of the time series is sampled from 1. the same or 2. possibly different Gaussian? In general, I don't fully understand your problem. Do you have the original time series $X_t$? If not, do you want to estimate it from "noisy" similar time series? I don't understand how subtracting $a$ from $X_t$ gives you any insight. Also, I don't understand how you are going to generate $Y_t$ if you don't have $X_t$. Are you assuming that $Y_t$ is $X_t -a$, for some $a$?$\endgroup$nbro–nbro2020-04-25 23:44:40 +00:00CommentedApr 25, 2020 at 23:44$\begingroup$@nbro Thanks for your response. The time series $X_t$ is given, and we know it has a Gaussian distribution, (histogram: Gaussian). So each point in $X_t$ belongs to a gaussian distribution with the same mean and variance. Now if you subtract a from each point in $X_t$ to construct a different series $Y_t$ the mean of the gaussian shifts. For some $a$, the constructed $Y_t$ has 0 mean. What I am asking if you vary $a$ and try to see for what $a$ this is achieved, then use that correctly found a, to generate more data points of $Y_t$, from $X_t$ can I call it as ML?$\endgroup$Raunak Dey–Raunak Dey2020-04-27 17:40:18 +00:00CommentedApr 27, 2020 at 17:40Add a comment| $\begingroup$I don't understand what you mean by "gaussian distributed time series". Can you precisely define it? Are you saying that each element of the time series is sampled from 1. the same or 2. possibly different Gaussian? In general, I don't fully understand your problem. Do you have the original time series $X_t$? If not, do you want to estimate it from "noisy" similar time series? I don't understand how subtracting $a$ from $X_t$ gives you any insight. Also, I don't understand how you are going to generate $Y_t$ if you don't have $X_t$. Are you assuming that $Y_t$ is $X_t -a$, for some $a$?$\endgroup$nbro–nbro2020-04-25 23:44:40 +00:00CommentedApr 25, 2020 at 23:44$\begingroup$@nbro Thanks for your response. The time series $X_t$ is given, and we know it has a Gaussian distribution, (histogram: Gaussian). So each point in $X_t$ belongs to a gaussian distribution with the same mean and variance. Now if you subtract a from each point in $X_t$ to construct a different series $Y_t$ the mean of the gaussian shifts. For some $a$, the constructed $Y_t$ has 0 mean. What I am asking if you vary $a$ and try to see for what $a$ this is achieved, then use that correctly found a, to generate more data points of $Y_t$, from $X_t$ can I call it as ML?$\endgroup$Raunak Dey–Raunak Dey2020-04-27 17:40:18 +00:00CommentedApr 27, 2020 at 17:40 $\begingroup$I don't understand what you mean by "gaussian distributed time series". Can you precisely define it? Are you saying that each element of the time series is sampled from 1. the same or 2. possibly different Gaussian? In general, I don't fully understand your problem. Do you have the original time series $X_t$? If not, do you want to estimate it from "noisy" similar time series? I don't understand how subtracting $a$ from $X_t$ gives you any insight. Also, I don't understand how you are going to generate $Y_t$ if you don't have $X_t$. Are you assuming that $Y_t$ is $X_t -a$, for some $a$?$\endgroup$nbro–nbro2020-04-25 23:44:40 +00:00CommentedApr 25, 2020 at 23:44 $\begingroup$I don't understand what you mean by "gaussian distributed time series". Can you precisely define it? Are you saying that each element of the time series is sampled from 1. the same or 2. possibly different Gaussian? In general, I don't fully understand your problem. Do you have the original time series $X_t$? If not, do you want to estimate it from "noisy" similar time series? I don't understand how subtracting $a$ from $X_t$ gives you any insight. Also, I don't understand how you are going to generate $Y_t$ if you don't have $X_t$. Are you assuming that $Y_t$ is $X_t -a$, for some $a$?$\endgroup$nbro–nbro2020-04-25 23:44:40 +00:00CommentedApr 25, 2020 at 23:44 2020-04-25 23:44:40 +00:00 $\begingroup$@nbro Thanks for your response. The time series $X_t$ is given, and we know it has a Gaussian distribution, (histogram: Gaussian). So each point in $X_t$ belongs to a gaussian distribution with the same mean and variance. Now if you subtract a from each point in $X_t$ to construct a different series $Y_t$ the mean of the gaussian shifts. For some $a$, the constructed $Y_t$ has 0 mean. What I am asking if you vary $a$ and try to see for what $a$ this is achieved, then use that correctly found a, to generate more data points of $Y_t$, from $X_t$ can I call it as ML?$\endgroup$Raunak Dey–Raunak Dey2020-04-27 17:40:18 +00:00CommentedApr 27, 2020 at 17:40 $\begingroup$@nbro Thanks for your response. The time series $X_t$ is given, and we know it has a Gaussian distribution, (histogram: Gaussian). So each point in $X_t$ belongs to a gaussian distribution with the same mean and variance. Now if you subtract a from each point in $X_t$ to construct a different series $Y_t$ the mean of the gaussian shifts. For some $a$, the constructed $Y_t$ has 0 mean. What I am asking if you vary $a$ and try to see for what $a$ this is achieved, then use that correctly found a, to generate more data points of $Y_t$, from $X_t$ can I call it as ML?$\endgroup$Raunak Dey–Raunak Dey2020-04-27 17:40:18 +00:00CommentedApr 27, 2020 at 17:40 Raunak Dey–Raunak Dey 2020-04-27 17:40:18 +00:00 1 Answer1Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first)0$\begingroup$Does it classify as Machine Learning?If I understand the problem correctly, my opinion isyes, this is machine learning. In fact, I think it is just linear regression for finding the equation of ahorizontal liney(t) = athat fits the the datasety(t) = Xt.ShareImprove this answerFollowansweredOct 24, 2022 at 2:13Snehal Patel1,05711 gold badge55 silver badges2727 bronze badges$\endgroup$Add a comment|You mustlog into answer this question.Start asking to get answersFind the answer to your question by asking.Ask questionExplore related questionsmachine-learningclassificationterminologytime-seriesSee similar questions with these tags. 1 Answer1Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first) 1 Answer1Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first) Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first) Sorted by:Reset to defaultHighest score (default)Date modified (newest first)Date created (oldest first) Sorted by:Reset to default Highest score (default)Date modified (newest first)Date created (oldest first) 0$\begingroup$Does it classify as Machine Learning?If I understand the problem correctly, my opinion isyes, this is machine learning. In fact, I think it is just linear regression for finding the equation of ahorizontal liney(t) = athat fits the the datasety(t) = Xt.ShareImprove this answerFollowansweredOct 24, 2022 at 2:13Snehal Patel1,05711 gold badge55 silver badges2727 bronze badges$\endgroup$Add a comment| 0$\begingroup$Does it classify as Machine Learning?If I understand the problem correctly, my opinion isyes, this is machine learning. In fact, I think it is just linear regression for finding the equation of ahorizontal liney(t) = athat fits the the datasety(t) = Xt.ShareImprove this answerFollowansweredOct 24, 2022 at 2:13Snehal Patel1,05711 gold badge55 silver badges2727 bronze badges$\endgroup$Add a comment| $\begingroup$Does it classify as Machine Learning?If I understand the problem correctly, my opinion isyes, this is machine learning. In fact, I think it is just linear regression for finding the equation of ahorizontal liney(t) = athat fits the the datasety(t) = Xt.ShareImprove this answerFollowansweredOct 24, 2022 at 2:13Snehal Patel1,05711 gold badge55 silver badges2727 bronze badges$\endgroup$ Does it classify as Machine Learning?If I understand the problem correctly, my opinion isyes, this is machine learning. In fact, I think it is just linear regression for finding the equation of ahorizontal liney(t) = athat fits the the datasety(t) = Xt. Does it classify as Machine Learning?If I understand the problem correctly, my opinion isyes, this is machine learning. In fact, I think it is just linear regression for finding the equation of ahorizontal liney(t) = athat fits the the datasety(t) = Xt. ShareImprove this answerFollowansweredOct 24, 2022 at 2:13Snehal Patel1,05711 gold badge55 silver badges2727 bronze badges ShareImprove this answerFollowansweredOct 24, 2022 at 2:13Snehal Patel1,05711 gold badge55 silver badges2727 bronze badges ShareImprove this answerFollow ShareImprove this answerFollow ShareImprove this answerFollow answeredOct 24, 2022 at 2:13Snehal Patel1,05711 gold badge55 silver badges2727 bronze badges answeredOct 24, 2022 at 2:13Snehal Patel1,05711 gold badge55 silver badges2727 bronze badges answeredOct 24, 2022 at 2:13 answeredOct 24, 2022 at 2:13 Snehal Patel1,05711 gold badge55 silver badges2727 bronze badges 1,05711 gold badge55 silver badges2727 bronze badges Start asking to get answersFind the answer to your question by asking.Ask questionExplore related questionsmachine-learningclassificationterminologytime-seriesSee similar questions with these tags. Start asking to get answersFind the answer to your question by asking.Ask question Start asking to get answersFind the answer to your question by asking.Ask question Start asking to get answers Find the answer to your question by asking. Explore related questionsmachine-learningclassificationterminologytime-seriesSee similar questions with these tags. Explore related questionsmachine-learningclassificationterminologytime-seriesSee similar questions with these tags. Explore related questions machine-learningclassificationterminologytime-series See similar questions with these tags. 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