time-series

问题 I have a time-series data as below: print(df) ric datel timel val 0 xyz 2017-01-01 09:00:00 2 1 xyz 2017-01-01 09:04:00 5 2 xyz 2017-01-01 09:37:00 6 Now I have to fill missing timestamps upto 09:45:00 . Expected Output: ric datel timel val 0 xyz 2017-01-01 09:00:00 2 1 xyz 2017-01-01 09:01:00 nan 2 xyz 2017-01-01 09:02:00 nan 3 xyz 2017-01-01 09:03:00 nan 4 xyz 2017-01-01 09:04:00 5 ... ... 37 xyz 2017-01-01 09:37:00 6 ... ... 45 xyz 2017-01-01 09:45:00 nan What I tried : df1=df.resample("1

问题 I create some fake (time series) data in R at daily intervals. I want to "aggregate" the data by "week", and then plot the data. I have posted my code below: #set seed set.seed(123) #load libraries library(xts) library(ggplot2) #create data date_decision_made = seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day") date_decision_made <- format(as.Date(date_decision_made), "%Y/%m/%d") property_damages_in_dollars <- rnorm(731,100,10) final_data <- data.frame(date_decision_made, property_damages

问题 Time series data is data over time. I am trying to animate a line plot of time series data in python. In my code below this translates to plotting xtraj as they and trange as the x. The plot does not seem to be working though. I have found similar questions on Stack overflow but none of the solutions provided here seem to work. Some similar questions are matplotlib animated line plot stays empty, Matplotlib FuncAnimation not animating line plot and a tutorial referencing the help file

问题 The bfast() function in package bfast is supposed to be able to detect both breakpoints in long-term trends and changes in the seasonal component. One example is this graph (source): In this graph, subplot no. 2 shows a detected change in seasonality, while no. 3 shows a breakpoint in the trend. However, I don't understand how to tell bfast() to look for changes/breakpoints in seasonality. All I get is breakpoints in the long-term trend. Here is a reproducible example, simulating a 50-year

问题 The bfast() function in package bfast is supposed to be able to detect both breakpoints in long-term trends and changes in the seasonal component. One example is this graph (source): In this graph, subplot no. 2 shows a detected change in seasonality, while no. 3 shows a breakpoint in the trend. However, I don't understand how to tell bfast() to look for changes/breakpoints in seasonality. All I get is breakpoints in the long-term trend. Here is a reproducible example, simulating a 50-year

问题 I have two pandas dataframes with following format: df_ts = pd.DataFrame([ [10, 20, 1, 'id1'], [11, 22, 5, 'id1'], [20, 54, 5, 'id2'], [22, 53, 7, 'id2'], [15, 24, 8, 'id1'], [16, 25, 10, 'id1'] ], columns = ['x', 'y', 'ts', 'id']) df_statechange = pd.DataFrame([ ['id1', 2, 'ok'], ['id2', 4, 'not ok'], ['id1', 9, 'not ok'] ], columns = ['id', 'ts', 'state']) I am trying to get it to the format, such as: df_out = pd.DataFrame([ [10, 20, 1, 'id1', None ], [11, 22, 5, 'id1', 'ok' ], [20, 54, 5,

问题 I have two pandas dataframes with following format: df_ts = pd.DataFrame([ [10, 20, 1, 'id1'], [11, 22, 5, 'id1'], [20, 54, 5, 'id2'], [22, 53, 7, 'id2'], [15, 24, 8, 'id1'], [16, 25, 10, 'id1'] ], columns = ['x', 'y', 'ts', 'id']) df_statechange = pd.DataFrame([ ['id1', 2, 'ok'], ['id2', 4, 'not ok'], ['id1', 9, 'not ok'] ], columns = ['id', 'ts', 'state']) I am trying to get it to the format, such as: df_out = pd.DataFrame([ [10, 20, 1, 'id1', None ], [11, 22, 5, 'id1', 'ok' ], [20, 54, 5,

问题 I am trying to implement a time series model and getting some strange exceptions that tells nothing to me. I wonder if I am making a mistake or if it is totally expected. Here comes details... When training my model, I try to make a grid search to find the best (p, d, q) settings. Here is the complete code (and I will explain down below what is happening here): The reproducible code below is essentially a copy from https://machinelearningmastery.com/grid-search-arima-hyperparameters-with

问题 I am trying to implement a time series model and getting some strange exceptions that tells nothing to me. I wonder if I am making a mistake or if it is totally expected. Here comes details... When training my model, I try to make a grid search to find the best (p, d, q) settings. Here is the complete code (and I will explain down below what is happening here): The reproducible code below is essentially a copy from https://machinelearningmastery.com/grid-search-arima-hyperparameters-with

问题 I've tried searching but couldn't find a specific answer to this question. So far I'm able to realize that Time Series Forecasting is possible using SVM. I've gone through a few papers/articles who've performed the same but didn't mention any code, instead explained the algorithm (which I didn't quite understand). And some have done it using python. My problem here is that: I have a company data(say univariate) of sales from 2010 to 2017. And I need to forecast the sales value for 2018 using