Time-series forecasting Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsRecurrent neural network producing same predictionsTime Resolution Changes in Time Series ForecastingTrying to use TensorFlow to predict financial time series datatime series forecasting - sliding window methodTime series forecasting using multiple time series as training dataML Algorithm for anomaly detection in paired time-seriesCan Reinforcement learning be applied for time series forecasting?Is there an R tutorial of using LSTM for multivariate time series forecasting?Need help understanding time series approach used for predicting earthquake arrival?
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Time-series forecasting
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsRecurrent neural network producing same predictionsTime Resolution Changes in Time Series ForecastingTrying to use TensorFlow to predict financial time series datatime series forecasting - sliding window methodTime series forecasting using multiple time series as training dataML Algorithm for anomaly detection in paired time-seriesCan Reinforcement learning be applied for time series forecasting?Is there an R tutorial of using LSTM for multivariate time series forecasting?Need help understanding time series approach used for predicting earthquake arrival?
$begingroup$
Here is the data:
l <- data.frame(date = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24))
k <- data.frame(cost = c(25,20,18,15,5,0,0,0,10,15,30,40,45,34,26,20,10,7,4,4,15,34,57,62))
m <- cbind(l,k)
ggplot(m, aes(m$date,m$cost)) + geom_line()
This is the graph I get:
What is a good prediction model? I think I can use polynomial regression if I subset the max and min values. see image below for better understanding. (Red for max, blue for min, lines were created using paint to explain a point)
Another way, I don't know what it is called, but I think they use it to predict weather, Not sure what the formula to obtain the blue line below would look like. (Look at graph below for better understanding)
What would be an appropriate formula to get the blue fitted line and predict the points highlighted in red??
r time-series forecasting
edited Apr 4 at 19:28
oW_♦
3,411933
asked Apr 4 at 19:17
FirstSlackFirstSlack
1016
$endgroup$
add a comment |
$begingroup$
Here is the data:
l <- data.frame(date = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24))
k <- data.frame(cost = c(25,20,18,15,5,0,0,0,10,15,30,40,45,34,26,20,10,7,4,4,15,34,57,62))
m <- cbind(l,k)
ggplot(m, aes(m$date,m$cost)) + geom_line()
This is the graph I get:
What is a good prediction model? I think I can use polynomial regression if I subset the max and min values. see image below for better understanding. (Red for max, blue for min, lines were created using paint to explain a point)
Another way, I don't know what it is called, but I think they use it to predict weather, Not sure what the formula to obtain the blue line below would look like. (Look at graph below for better understanding)
What would be an appropriate formula to get the blue fitted line and predict the points highlighted in red??
r time-series forecasting
edited Apr 4 at 19:28
oW_♦
3,411933
asked Apr 4 at 19:17
FirstSlackFirstSlack
1016
$endgroup$
add a comment |
$begingroup$
Here is the data:
l <- data.frame(date = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24))
k <- data.frame(cost = c(25,20,18,15,5,0,0,0,10,15,30,40,45,34,26,20,10,7,4,4,15,34,57,62))
m <- cbind(l,k)
ggplot(m, aes(m$date,m$cost)) + geom_line()
This is the graph I get:
What is a good prediction model? I think I can use polynomial regression if I subset the max and min values. see image below for better understanding. (Red for max, blue for min, lines were created using paint to explain a point)
Another way, I don't know what it is called, but I think they use it to predict weather, Not sure what the formula to obtain the blue line below would look like. (Look at graph below for better understanding)
What would be an appropriate formula to get the blue fitted line and predict the points highlighted in red??
r time-series forecasting
edited Apr 4 at 19:28
oW_♦
3,411933
asked Apr 4 at 19:17
FirstSlackFirstSlack
1016
$endgroup$
Here is the data:
l <- data.frame(date = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24))
k <- data.frame(cost = c(25,20,18,15,5,0,0,0,10,15,30,40,45,34,26,20,10,7,4,4,15,34,57,62))
m <- cbind(l,k)
ggplot(m, aes(m$date,m$cost)) + geom_line()
This is the graph I get:
What is a good prediction model? I think I can use polynomial regression if I subset the max and min values. see image below for better understanding. (Red for max, blue for min, lines were created using paint to explain a point)
Another way, I don't know what it is called, but I think they use it to predict weather, Not sure what the formula to obtain the blue line below would look like. (Look at graph below for better understanding)
What would be an appropriate formula to get the blue fitted line and predict the points highlighted in red??
r time-series forecasting
r time-series forecasting
edited Apr 4 at 19:28
oW_♦
3,411933
asked Apr 4 at 19:17
FirstSlackFirstSlack
1016
edited Apr 4 at 19:28
oW_♦
3,411933
asked Apr 4 at 19:17
FirstSlackFirstSlack
1016
edited Apr 4 at 19:28
oW_♦
3,411933
edited Apr 4 at 19:28
oW_♦
3,411933
edited Apr 4 at 19:28
oW_♦
3,411933
3,411933
asked Apr 4 at 19:17
FirstSlackFirstSlack
1016
asked Apr 4 at 19:17
FirstSlackFirstSlack
1016
asked Apr 4 at 19:17
FirstSlackFirstSlack
1016
1016
add a comment |
add a comment |
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