Questions tagged [time-series]

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

Filter by
Sorted by
Tagged with
0
votes
0answers
5 views

Partitioning and Indexing Time Series Databases using Graph Algorithms

I am working on a large scale streaming system which requires a partitioned and indexed time series database. I was looking at the possibility of doing it using Graph Algorithms or Graph Query systems....
0
votes
1answer
40 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 ...
0
votes
1answer
21 views

time series anomaly detection

I want to ask in time series anomaly detection we can apply transformer architecture on multiple features or not? I used a transformer for sentiment analysis where I have to provide a sentence and it ...
0
votes
0answers
9 views

Model on non-iid data performing badly

I am on this lecture about non-iid data where we generated a timeseries data using the function below: ...
1
vote
1answer
116 views

Generating a set of different scenarios based on some initial observations

I have a in my hands 3 different time series which model 3 different scenarios (base, downside, upside). Every of this time-series depends on a set of 11 different attributes, which take values for ...
2
votes
1answer
58 views

What Models should i try for this problem?

I need some advice for a problem i'm working on with automobile data. The vehicles provide a series of codes at every second which are bieng stored, though it can vary how many. For example , at time ...
-1
votes
0answers
7 views

For time series prediction is the transformers more appropriate than Combination of LSTM-CNN

Hello I have multiple features in a time series and want to predict the values of the same features for the next time step. For time series prediction is the transformers more appropriate than ...
0
votes
1answer
125 views

Predicting out-of-sample time points with LSTM

I'm working on a time series forecasting problem using LSTM. The data is univariate and non-stationary. I followed this tutorial. The data is processed as the following: First, the difference between ...
-1
votes
1answer
30 views

Anomaly Detection and Removal/Interpolate

I am performing a machine learning regression task on time series data. I have a data frame filled with the close prices of various assets and economic data. I am looking to perform outlier detection ...
2
votes
1answer
189 views

Feature selection for time series prediction

I'm working on an LSTM-based stock market forecasting problem and trying to figure out a way to select input variables. When calculating correlation between variables (e.g. Close price of Tesla vs ...
0
votes
1answer
18 views

determining size of batch, time of sending and memory in to send from scala to ML section

I have a time series (sampling time: 66.66 micro second, number of samples/sampling time=151), I would like to determine some anomalies in them, the inputs are made by scala customer message bus. ...
1
vote
2answers
31 views

unsupervised anomaly detection for univariate fast frequency time series data?

I have a univariate time series (there is a value for each time sampling) (sampling time: 66.66 micro second, number of samples/sampling time=151) coming from a scala customer This time series ...
0
votes
0answers
20 views

Precision, Recall and/or F1? Which should I use? or something different?

I am trying to use tensorflow to predict a decision based on a timeseries dataset. I have three classes: "Wait", ...
2
votes
1answer
665 views

Whole time column in csv file convert into UTC (epoch) using python

I have a dataset with time and columns. I want to plot a graph with time and value. I tried many methods but didn't come proper graph. Because I have a time series. Then I thought I will convert time ...
0
votes
1answer
25 views

ML algorithm for high dimensional time series forecasting

I'm trying to make a forecasting model for goods prices in an economy (trying to forecast inflation). Dataset: has 300 goods prices % monthly variations for last 6 years. And also added $n$ ...
0
votes
0answers
21 views

Building a Model for Time Series Data in R (no forecasting)

Problem: I had planned to use a linear regression model to model time series data in retrospect (i.e., no forecasting). However, I am wondering if this is the best option having come across a few ...
1
vote
1answer
51 views

Predicting when component will fail having its parameters data

I have a component and I need to predict when it will wear out and will need replacement. I monitor, let's say 5 parameters of this component, each one is monitored for every run cycle. So, the ...
0
votes
2answers
31 views

XgBoost given targets its only feature but fails when test targets are outside the range of training targets?

I'm learning to use XgBoost, and I'm doing an exercise involving predicting prices. However I'm noticing some weird behavior where XgBoost's predictions deviate from the target value even if I'm ...
0
votes
0answers
7 views

How to clean/analyze relative time series data

I'm relatively new to data science, and I decided to stretch a bit and try a project with time series data. I downloaded the Human Activity Recognition from Continuous Ambient Sensor Data Data Set, ...
1
vote
1answer
39 views

which Model to apply on panel data where unique id has 6-8 records and total records are 2,000,000?

I am new to such panel data where I have multiple observation for same ID in different Quarter and I am not sure what kind of machine learning algorithm I can apply. I have data from Q1-18 till Q4-...
1
vote
1answer
73 views

Multi-step forecasts of factory production data using a Seq2Seq Encoder-Decoder Model with Attention

I am attempting to use a Seq2Seq model to make forecasts of factory production data using an Encoder-Decoder model augmented with Attention. I have become a little stuck as the output of the model ...
1
vote
1answer
49 views

Timeseries LSTM: does test data need to come after training data?

I have one single, very long time series. I want to train an LSTM to distinguish between two behaviours (A or B) at every timestep (sequence-to-sequence). Because the time series is very long, I plan ...
1
vote
1answer
21 views

What methods are there for predicting a signal?

I have a large dataset of signals (composed of time series). All time series describe the same process, but each series has a different duration (number of points). Based on these time series, I want ...
2
votes
1answer
135 views

Reinforcement Learning on real time data over a web server

Question: is it possible to implement a reinforcement learning model over a NodeJS server? This server would be receiving binary forms of data (open /close; yes/no) in real time. The objective for ...
0
votes
1answer
18 views

finding winning strategy

For a given asset, I have simulations of the price and implied volatility for T periods in N scenarios. Furthermore, assuming that I know the value of the risk-free asset (and the dividend yield), I ...
0
votes
0answers
13 views

Quantifying treatment effect in Interrupted Time Series

I have a multivariate time series dataset, from which I am building an ITS (Interrupted Time Series) model by using facebook's Prophet to construct the counterfactual. Let's say I have a y variable ...
0
votes
0answers
7 views

regressor column might have different length

I'm attempting to use a neural network to do some time series forecasting. The goal is to forecast price and I have a fewer regressors to help along like fuel prices and number of sick people among ...
2
votes
1answer
1k views

Using SMAPE as a loss function for an LSTM

I am currently working on a time series forecasting problem and am looking into using an LSTM. My final accuracy metric that I use to determine whether or not the forecast is good or not is defined ...
0
votes
0answers
27 views

Sensitivity analysis in time series forecasting

Given a data set consisting of features time signals $X=[x_1,\dots, x_n]$ and one target time series $y$, I would like to study the sensitivity of $y$ with each of the $x$'s. What I think: Compute ...
1
vote
0answers
17 views

Question about a reading

So I'm trying to do multivariate time series prediction and a google search led me to this article: https://bookdown.org/singh_pratap_tejendra/intro_time_series_r/neural-networks-in-time-series-...
0
votes
0answers
13 views

times series prediction with several regressors( using R)

Absolute beginner here. I'm trying to use a neural network to predict price of a product that's being shipped while using temperature, deaths during a pandemic, rain volume, and a column of 0 and 1's (...
0
votes
1answer
197 views

TensorFlow Time Series Tutorial Enhancement Gone Wrong

I’ve been following this time series tutorial for Tensorflow… https://www.tensorflow.org/tutorials/structured_data/time_series And it was going well and seemed to work ok. I substituted with my own ...
1
vote
1answer
18 views

Loss drops to NaN after a short time for a time series classification

here is my model code for a binary classification of a time series: ...
0
votes
1answer
18 views

Should I use an LSTM model when the outcome is a different variable from the training data?

I am trying to model a health outcome as a function of climate variables. I have many observations of health outcomes at different times and locations (but NOT a sequence at one location). For each ...
0
votes
0answers
59 views

What is the right way to make prediction with time series forecasting models?

I'm into ML and data science for a while but now I started exploring time-series forecasting, and I have (lets say) a simple question: What are the features/inputs for the time-series forecasting ...
4
votes
1answer
496 views

k-Nearest Neighbours with time series data - how to obtain whole-time-period estimators

I have a large dataset for the activities performed by multiple staff in a factory over a long period of time - 01/01/2017 - present. The activities performed by the different staff are recorded at ...
1
vote
1answer
54 views

What kind of algorithm should I use to build ML model that can predict just next reoccurence of an event in the future (at irregular time interval)?

I'm quite new to machine learning and statistics. I've a dataset from some ecommerce sale's history. It's almost 2k instances, and features include personId (string), productCategory (string/...
1
vote
0answers
15 views

Prediction method when the time series is not sequential?

I have multivariate time series data consisting of monthly sales of contraceptives at various delivery sites in a certain country, between January 2016 and June 2019. The data looks as follows: The ...
2
votes
1answer
410 views

ML methods for prediction, using categorical variables and time

Most of the time series analysis tutorials/textbooks I found time series data, usually deal with continuous numerical variables. I am currently trying to solve a problem that deals with multivariate ...
0
votes
0answers
11 views

Selecting the best model parameters from grid search SARIMA [Time series]

I ran a manual gridsearch of SARIMA across several parameters and now I have 7875 rows of scores (RMSE, MAE, MAPE each) from it. These were the parameters (30k+ permutations) I ran a grid search over- ...
1
vote
1answer
67 views

How to measure/rate the effect of a exogenous covariate in a ARIMAX Model?

I have an ARIMA model, I'm trying to figure out how much an external variable (exogenous covariate) could improve the forecast, so I need to "synthesize" a rate that tell me the usefulness (or impact) ...
2
votes
1answer
33 views

Sensorfusion: Generate virtual sensor based on analysis of sensorsdata

I have a steam engine which is equipped with the following sensors: temperature sensor in the boiler room temperature sensor in the heating room pressure sensor in the boiler room rotations-per-...
0
votes
0answers
17 views

How can I use a prediction model (e.g., ARMA model or LSTM) for multi-variate data?

I have had a dataset below: sensor1 sensor2 sensor3 ... 2021-01-01 1.32 2.2 1.0 2021-01-02 4.3 2.0 0.8 ... ... I know ...
0
votes
0answers
21 views

Recurrent models for asynchronous / mixed frequency time series

What are some of the RNN/LSTM models for handling mixed frequency/asynchronous time series data, such as macroeconomics, financial, precipitation, etc.? So far I have found phased lstm from a similar ...
0
votes
0answers
14 views

LSTM decoder with 2d's input

I am developing a CNN-LSTM autoencoder in pytorch to predict time sequences. The CNN input is a RGB image: RGB image => tensor[Batch size= 4, channel = 3,width= 256, height=256] and the output is ...
2
votes
1answer
63 views

How to treat patients without events in time-to-event analysis?

I'm working with longitudinal data for a series of patients. Duration of followup on a patient-level is non-uniform. Patients can either experience a discrete event (e.g., a heart attack) or never ...
1
vote
1answer
121 views

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 ...
1
vote
1answer
237 views

Voting classifier using grid search for Time Series

I have three models: Arima Auto ARIMA Double Exponential Smoothing I would like to apply an ensemble method - a voting method and allow the classifier to learn weights for these three models. I ...
3
votes
1answer
39 views

preprocessing time sequence

I have a long list of event (400 unique events, sequence ~10M long). I want to train an RNN to predict next event. The preprocessing steps i took are: (1) turning to OneHotEncoding using pandas: <...
0
votes
0answers
10 views

Prediction Intervals on (Multi-Step) Judgement Forecasts

Are there any R packages available or general methodologies for calculating prediction intervals on judgment-based forecasts? I've looked at Hyndman's text and the R forecast package - which will ...

1
2 3 4 5
29