Questions tagged [time-series]

Time series are data observed over time (either in continuous time or at discrete time periods).

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9 views

From daily prediction to hourly

I have daily number of sales prediction. I would like to go to an hourly level, taking this prediction. I have past data at hourly level. At this stage, I do not want to predict at hourly level since ...
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model has not yet been built

I'm making CNN-LSTM model for forecasting but I'm receiving this error : This model has not yet been built. Build the model first by calling build() or calling fit() with some data. Or specify ...
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Finding correlation between categorical feature and continuous feature

Our input features are of type: time series. we wanted to find correlation between the features and the target class. For discrete and categorical feasters we used DataFrame.Corr, but this is not the ...
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1answer
20 views

Predicting equipment failure with time series alarm data

I am trying to predict machine failures based on alarm data. The situation: There is approximately 4000 machine failures per year. These are labelled poorly (it is entered manually and can have ...
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How to find lagged cross correlation between time series?

I have 2 time series, $X$ and $Y$, and I'm trying to find the best lag range that correlates $X$ to $Y$ (find the amount(s) of lag of $X$ that best correlate to the target variable $Y$). For instance, ...
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How to use id's in binary classification problem

I would like to predict for a given user (on a website) if he/she logs out from the website within ten minutes. In terms of data, I have a user ID and timestamp of the latest post on the website. ...
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Error when checking target: dimensions error in CNN-LSTM model for multivariate time series forecasting

I'm making a CNN-LSTM model to forecast multivariate time series: ...
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1answer
22 views

Time series binary classification [closed]

Which deep learning architecture and algorithms do you most recommend for time series classification problem? Of course LSTM, I am looking for state of the art papers.
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How to input LSTM output to MLP with concatenate?

I am having a training data set for a time-series dataset like below where my target variable is var1(t) which is the value of var 1 at time=t. ...
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Generating new sequences from given set

I got two classes namely positive and negative with 1500 samples on each a total of 3k. A sample sequence is like: ...
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How to determine sample rate of a time series dataset?

I have a dataset of magnetometer sensor readings which looks like: ...
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1answer
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What is the meaning of each element in input_shape of Conv1D in Keras?

I have a time-series data for 3 classes (each class is 35 second) as I extract each 1 second for 95 feature extracting so my final data has shape (105,95) (rows for time and column for feature). I am ...
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Why is my time series model predicting strange results?

I am trying to predict some time-series data. The output data predicts two numbers (one that's usually greater than 1 and another that is usually less than 1). I've plotted about 800 samples where the ...
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1answer
23 views

Using Low Frequency Labels with High Frequency Features

I am trying to build a model (most likely a regression or random forest regression) for quarterly financial data. My training data has a daily cadence, but I am not sure how to work with these to ...
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How to include the other variables at t=t to predict the target variable with time lags also in LSTM?

I am having a training data set for a time-series dataset like below: ...
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1answer
18 views

Time series data and ML - separating training/test data

I am using XGBoost to try to predict the direction of the stock market based on social media sentiment. Having read through some studies, I was planning to separate the training/test data by time ...
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Using Vector Auto Regression for multiple time series at once

Say I have a dataframe like so: ...
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Using scikit-learn for classification problem where classifier based on high dimensionality data

I am using scikit-learn for time series price data for a spot market to categorize the time series points after as the same, higher, or lower price as the current time series point recorded. I am not ...
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Web scraping using Beatiful Soup

I have this code and I wanna extract holidays, petrol and temperature but I don't know where is the problem. I need your help as soon as possible, please. I want to add this extraction to my dataset ...
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1answer
26 views

Time series forecast for everyday for till a distant future

I have time series data for every single day from last 5 years with seasonal variation and a general increase in trend. This is what my data looks like: And I am trying to predict for every single ...
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1answer
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LSTM's for timeseries with additional regressors

I have a dataset consisting of the weekly sales of 3,000 stores over the past 5 years, and have constructed a LSTM to forecast the next year of sales, given the previous year of sales. At each ...
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I am trying to figure out the stationarity of time series data

Here, this plot shows the number of customers served per day from 1 jan 2018 to 31 dec 2019. I grouped the entire data by each month and calculated the average and variance per month. This is the ...
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Why are Neural Network predictions “correct”, but offset from true value? Not using any past lagged values

I recently asked a similar question, but didn't get a response that really addressed/fixed the issue. Additionally, I've done some more work since then. I'm sorry for the long question below, I just ...
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Why are predictions from my LSTM Neural Network lagging behind true values?

I am running an LSTM neural network in R using the keras package, in an attempt to do time series prediction of Bitcoin. The issue I'm running into is that while my predicted values seem to be ...
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Correcting high AR(1) coefficients in dynamic Gordon model

I have just finished my thesis on a heterogeneous dividend expectations model applied to the COVID-19 crisis! However after receiving some feedback there is one last issue I want to resolve. I'm using ...
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1answer
35 views

What are some good methods to forecast future revenue on categorical and value based data?

I have monthly snapshots (3 years) of all the contract data. It includes following information: Contract status [Categorical]: Proposed, tracked, submitted, won, lost, etc Contract stages [...
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Multidimensional time series regression

I’m new to time series forecasting and I’m trying to implement regression models using both ARIMA and LSTM for a multidimensional and multivariate time series. The samples are indexed by time, ...
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1answer
13 views

Rolling average: when is it possible to consider it?

I would like to know if I can consider rolling average to predict the future trend of sells. I collected data from January 2020 to March 2020, day by day, on sells in a shop and I would like to run ...
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Time Series Predictive Model

I have a dataset similar to the following one: ...
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1answer
27 views

Can 1D-CNN method apply to real-time time series classification?

So I got an EEG dataset with shape (data points, 19), each row's shape (1,19) represent 1 second of EEG. I read much research on EEG classification that used many Deep Learning method and 1D-CNN is ...
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How can I explain this chart showing 5-days moving average?

I have plotted the frequency of items sold through time, trying to determine the trends by moving average. I considered a 5-days window. I would like to know if this approach makes sense and how I ...
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Time Series Analysis / Modeling with auto_arima

I recently dived into Time Series and was attempting at doing some data analysis and modeling. I'm using the following dataframe ...
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1answer
18 views

Can features passed into Conv1d layers be randomized?

If I have input time series data shape as such, X.shape = (batch_size, 50, 5), it means that the data has 5 "features", each having 50 time steps. Does passing data like this into a Conv1d ...
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2answers
43 views

Predict time to reaction (chemical engineering)?

I need a hint on the problem below. This is related to predictive analysis and chemical engineering. I don't background in chemical engineering, and that's why I am looking for some hints. I want to ...
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6 views

Ljung-box test on weekly percentage of total quarter bookings

I have a data on the weekly percentage of the total quarter bookings. The data looks as follows (note: weekly percentages add up to 100 for each quarter) : (not real data) I used the Ljung-box test ...
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1answer
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How to get back stationary data from non stationary data after performing diff operations in var (time series)?

I have applied var model on multiple features ,but var model accepts the stationary data so i have converted non-stationary data to stationary data by doing diff() ,given the data to var model. what ...
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1answer
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Building Timeseries models for stock trading having multiple stocks

I have gone through some of the tutorials on the timeseries and all of them have taken one stock for the timeseries and tried to forecast it. My dataset contains many stocks for the time period(each ...
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12 views

Predict all possible dates in time series data

In my problem statement one part is to predict all possible dates(t-n) where t is my current date. I want to process below dataset to predict all possible order dates for next login date(single ...
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1answer
19 views

Use both differencing and normalization in time series modeling to make it stationary?

I am working on a time series dataset. Should we use both differencing and normalizing or either of the ones to make it stationary?
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Chossing between Prediction vs classification model for dataset having daily record(date value)

I have a use case, where I have 4 classes based on the score, for example class 1 : when the score < 10 class 2 : when the score between 11 to 20 class 3 : when the score between 21 to 30 class 4 ...
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1answer
14 views

How to go about cooling load forecast for a district level dataset?

I am trying to build a high accuracy cooling load forecast for a district-level dataset over various time horizons. My data consists of a time series of cooling load and consecutive weather data. I ...
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2answers
27 views

Input with variable length Classification problem

I have a dataset with patient information with discrete labels (labels are stages of a particular disease) which needs to be predicted (Basically a classification problem). The dataset looks ...
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1answer
14 views

Can we do multivariate time series analysis using holt-winter ( Exponential smoothing) method?

Just like we have a method like ARIMAX and SARIMAX where we can provide exog and endog variable for perfroming multivariate analysis. I was hoping is there a way, we can achieve same using ETS as well....
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1answer
17 views

Best way to handle padding in time series data such as text

I have a bunch of documents containing sequential data that I want to use to train a neural network with. It is as a collection of letters each about a 2-3000 characters long. My task is, given an ...
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questions on multi-step air pollution prediction

I am trying to use RNN to predict the concentration of various air pollutants for the next 24 hours. The input data consists of 72 hours long and every hour owns 14 elements such as temperature, ...
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2answers
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What are some good loss functions used to minimize extreme errors in regression and time series forecasting?

E.g. In detriment of a smaller mean error, I want to have fewer big mistakes I'm working on a time series forecasting task and in some specific cases I don't need perfect accuracy, but the network ...
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Predicting parameters of simple configured trajectories using RNN

What I'd like to do: predict orbital elements given an input observation sequence in 2D, that is $$input = X = [position_{t_{0}}, position_{t_{1}},\ ..., position_{T}]$$ $$output = y = parameters\ ...
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Python- forecast for time series using continuous hidden Markov models

I have a dataset with a time series containing the revenue that my company does every day. I want to forecast the revenue that we will have for the next day. I have tried different models(ARIMA, LSTM,...
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12 views

distinguishing two in phase components in a signal

Do you guys know some algorithems that can surely extract a component out of the following signal: I have an IR-sensor that generates the Signal s(t), which consists out of two components: s(t) = c(t)...
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1answer
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static and dynamic data in clinical trials

Hi everybody and thanks in advance for those who will help me for this problem. I have multiple data regarding patients involved in a clinical trial and my goal is to predict their death/non death. ...

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