Questions tagged [time-series]

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

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in-sample and out of sample forecasting plot look very different

why the in-sample looks very different from the out-of-sample? Is the in-sample overfitting? Horizon is 12, using pycaret time series function.
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Using Bayesian statistics in time series forecasting

I would like to forecast demand count time series of taxi fleets at different locations on the map at different points in time. I.e. multivariate demand Time series forecasting. Given hierarchinal ...
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Eventual Forecast Function [closed]

(1 - 0.43B)(1 - B)Zt = at How do I calculate the eventual forecast function for an ARIMA(1,1,0) model? My textbook does not explain for this specific model...
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Comparing two countries using time series data

I'm considering using time series data to compare Spain's and China's economic performance during various years of economic growth. However, I'm wondering if it's valid to compare them when they ...
Alexandra Liu's user avatar
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Resources for Probabilstic forecasting

I was planning on learning probabilistic forecasting and im completely lost. Suggest some online resources. I have started with Probabilistic Forecasting and Bayesian Data Assimilation but its a bit ...
cosm1c v1bes's user avatar
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Design of experiment in multiple regression

Suppose we have the following model of our environment: $\hat{y}_t = e^{dayofweekeffect} * x_{1, t}^{\beta_0} * x_{2, t}^{\beta_1}$ which we can linearize into: $log(\hat{y}_t)= dayofweekeffect + \...
hugh's user avatar
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Prediction based on new data in the tsDyn package [closed]

I decided to use the tsDyn package to build the model and precision the values. The argument for this package (I previously worked with urca, vars) was the ability to make predictions based on new ...
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Prediction when Target's lag values are part of Predictors

I'm using LGBM for regression, where the Target column's lagged values (7 columns for each lag day) are also used as predictors when training the model. Absence of the 7Day lag values severely ...
fast_crawler's user avatar
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Due to limited information How can I restructure my model to capture this relationship? [duplicate]

I created a model for prediction. The target data has a total of 4 classes (0-3), which makes my model completely ineffective. Due to limited information How can I restructure my model to capture this ...
Max's user avatar
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2 votes
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E-commerce - Does higher delivery time lead to lower sales?

Question I have a practical problem and am seeking guidance on how to use statistical methods to solve it. I have an online store. The products in the online store have varying availability: 24 hours ...
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How come the deterministic part of Wold decomposition does not violate stationarity?

Wold's representation theorem states that every covariance-stationary time series $\{Y_t\}$ can be written as the sum of two time series, one deterministic and one stochastic: $$ Y_t=\sum_{j=0}^\infty ...
Richard Hardy's user avatar
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Notation for an ordered pair of stochastic processes

There are two stochastic processes, $\{ Y_{1,t} \}$ and $\{ Y_{2,t}\}$. If I take them as an ordered pair, what notation do I use: $\{(Y_{1,t},Y_{2,t})\}$, $\{(Y_1,Y_2)_t\}$, $(\{Y_{1,t}\},\{Y_{2,t}\}...
Richard Hardy's user avatar
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Preventing Data Leakage in Time Series Forecasting with Feature Engineering

In a previous question (linked here), I sought guidance on forecasting thousands of time series. Based on the suggestion to treat it as a regression problem, I used the LightGBM model with extensive ...
Tirth's user avatar
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How to handle recent variable change resulting in level-switch in time series modeling?

I developed a script to run time series models on people data, and I re-run the Arima model fitting/forecast reconciliation algorithm monthly as new data comes in. I use the grouped/hierarchical time ...
Gabrielle's user avatar
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Bad performance of ARIMA model on online buzz data, Any suggestions?

I was wondering if the ARIMA model is constrained to predict online buzz data (time series data). What I want to do: Use the past round 30 months data to predict next month; and I use Python Here are ...
Steven Wang's user avatar
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Can I test for significant difference between samples of a single time series?

I am trying to find a statistically sound way of analyzing my time series data. The data and questions are pretty simple: Experiment setup I have an estimate of the rate at which a crustacean consumed ...
user396988's user avatar
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Include variance change at point in ARIMA model estimation in R

I have a series which I am trying to model through ARIMA approach. However, when checking the residuals, there appears to be a change in variance at a specific point. This is, given the residuals $e_t$...
Jesús A. Piñera's user avatar
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Granger causality does not imply a pair of integrated time series are cointegrated: an example

If a pair of integrated time series $\{X_t\}$ and $\{Y_t\}$ are cointegrated, at least one of them must Granger-cause the other. Is the converse also true? I guess not, but I am struggling to come up ...
Robert H.'s user avatar
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How to construct forecast confidence interval from the historical RMSE?

I'm reading a FED paper on forecast uncertainty estimation. Here's the link One of their methods to estimate forecast interval is illustrated in the following figure According to the description: &...
NonSleeper's user avatar
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How to interpret those ACF & PACF?

I have some problems when analyzing my time series dataset. Basically, the dataset is about the daily sales volume of an FMCG company (they work from Monday to Saturday with Sunday being a day off, so ...
Khoi Le's user avatar
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Is it possible to specify correlation structure for mixed models in Julia? [closed]

Is there an equivalent approach to the following mixed model, growth curve analysis in Julia: ...
SanMelkote's user avatar
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Multiple correlations across subjects

I have a bunch of data where I am recording two variables across time for many subjects: velocity of the right hand, and intensity of the speech. For each of my 20 subjects, I have ~30 recordings of ~...
Paidon's user avatar
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Improve HMM state estimation in latest data

I have a time-series dataset that is poisson-distributed, where each day I get a new additional datapoint. If I input all the data into a HMM (I am using code I found from hmmlearn in python) it does ...
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Adding a New Feature

My question is pretty straightforward and the task behind is related to binary classification. To add a new feature, do i first do train_test_split then add a new ...
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Optimization problem involving VAR

I'm looking to solve, from the paper https://arxiv.org/pdf/2210.02176.pdf, $$\min_{g\in VAR(p)} \sum_{z\in \mathcal{Z}} (f( h_{x}(z))-g(z))^2\pi_{x}(z)$$ where $\mathcal{Z}\in \\{0,1\\}^{N\times W}$ ...
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Moment-generating function of an ARMA process

If $X_t$ is an ARMA(p,q) process is it possible to derive its moment-generating function?
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where's a good reference and community for help with getting started on Bayesian time series analysis?

I am trying to learn the basics of Bayesian time series analysis, but am having trouble finding some up-to-date basic examples and a discussion forum where I might be able to get some guidance. (...
Ian's user avatar
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Can I use the residuals of a time series decomposition to estimate the effect of a covariate?

Context I work for a company that has an e-commerce website. Regularly we make specific campaigns in order to sell more. For example: We can make a campaign for fathers day, black Friday, crazy August,...
Guilherme Parreira's user avatar
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Statistical procedure to remove a time series which is an "outlier" in a set of replicated time series

This data are from measuring optical density in a bacterial growth experiment. These correspond to 4 time series which are biological replicates of exactly the same treatment (with the label 0_4) The ...
rgvalenciaalbornoz's user avatar
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Multiple comparison problems for Unit Root testing

Lets say I have a multivariate time series and I will run unit root tests for each variable to figure out if the variable is stationary and how many differencing are necessary to make it stationary. ...
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Metric for run-to-run consistency of time series data

If I run $n$ samples of a physical experiment, I expect to see roughly similar time vs. position plots but with slight variations run-to-run. What are good statistical metrics to quantify the ...
asyndeton256's user avatar
2 votes
1 answer
32 views

Statistical Method for Accurately Detecting Seasonality in Monthly Sales Data

I have a dataset containing monthly sales data for different product categories spanning five years (60 months of data). I am using a Python process to calculate the seasonality for each category, ...
francisco sollima's user avatar
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33 views

How to predict more than one future values in ARMA model

I want some help with predicting more than one future values in ARMA. I saw that the similar question has been asked here. But it is only helpful for predicting one future value. For estimation of the ...
Vishalkumar Rajeshbhai Gohel's user avatar
2 votes
1 answer
36 views

Forecasts fail for new period

I am working with prophet library in python where I do some forecasts. While I split to train and test to check, it shows very good performance, but when I forecast for the actual future period it ...
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How to obtain seasonally-adjusted time series data using STL in Python

On the section "STL decomposition" in the 2nd edition of Forecasting: Principles and Practice, it says that the seasadj() function can be used to compute ...
Luca Guarro's user avatar
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6 views

How to dectect sudden change in signal frequency? (analysis of EEG signals)

I am trying to analyze data from EEG electrodes to understand how brain activity changes in different coginitive states (for simplicity, assume that there are only 2 states: baseline (A) and chanelled ...
Brzoskwinia's user avatar
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Simulation of a time series using the unconditional moments

$y_{t}$ follows a covariance stationary AR(1) process $ y_t = \phi y_{t-1} + \varepsilon_t \hspace{1cm} \varepsilon \sim \mathcal{N}(0,\sigma^2) $ I want to simulate a time series $y_{1:T}$. Can I use ...
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What should we consider when visually evaluating non-stationary data before making assertions?

I often see charts displaying multiple time series plotted together and they are often accompanied by explanations relating one series to the other. For example, in the below chart, one might say less ...
user220790b's user avatar
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Stationarity and Chow-Lin disaggregation

My goal is to have higher frequency data (quarterly) for the GDP of my region (yearly data) through quarterly auxiliary data. Therefore my goal is to perform a Chow-Lin regression using the package <...
Anna's user avatar
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Question on Cochrane–Orcutt estimation. Can I simply use inverse of the lag operator?

I came across the Cochrane–Orcutt estimation, which is concerned with regression-type estimates in cases where errors are AR(1) correlated. My question is whether I can formally apply the inverse of ...
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How to vectorize a recursive relation (AR in Bootstrapping)? Possible?

I´m working towards the resolution of all questions in Efron & Tibshirani 1993. The Introduction to the Bootstrap. I got stuck in question 8.6. It is related to the bootstrap of an AR process, ...
bluemaster's user avatar
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62 views

Testing if some process has missing data

I am studying a time process, consisting in events occuring at some dates, one after the other (typically one event every 1-2 days, sometimes several in a day, with data covering circa 20 years). I ...
user3771535's user avatar
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Matching time series plot with ACF plots

We have to match time series plot in the 1st row with the ACF plots in the second row. The solution for the same is given below. However , It would helpful if someone could explain the solution.
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1 vote
1 answer
39 views

Monthly data with seasonality: is linear regression appropriate?

I have the following situation: there is data for six years, per month. In a year you can see variation per month, since the data is influenced by the seasons. Also, there is an upward trend over the ...
Eva's user avatar
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1 answer
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Is the R2 statistic affected by normalizing your timeseries?

I have been trying on comparing to time series, one predicted and one measured. One of the first and most simplistic statistics that can help is R2. I have no issues calculating R2 but my supervisor ...
TryingCode's user avatar
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1 answer
26 views

High Level Time Series Forecasting Question given a set of unknown data sets

I have this question regarding 4 data sets where one of the data sets is missing half of the data. The data is relatively correlated, but not perfectly. How may we construct a model to produce the ...
ML-Enthusiast's user avatar
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1 answer
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Can this be considered overfitting?

I have been trying to use the LSTM model for a monthly time series with a length of 404 (384 for training and 20 for test). I created 4 pairs of training/validation sets, trained different models, and ...
Mamad Fasih's user avatar
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1 answer
21 views

Comparison of time series: Cluster behaviors / detect anomalies

I am studying a dataset of time series for different users. The dataset contains records of actions (or registrations) of the users over time. I have data of a whole week for about 80,000 users. ...
tms's user avatar
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1 answer
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Why use sliding window input features in sequence modeling?

I was reading through the DNABERT paper and found that their input features were k-mers. This is equivalent to using rolling/sliding window features in the other common family of sequential problem, ...
Avatrin's user avatar
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Simulate time series with periodic spikes

I have multiple time series representing a process that operates in batches. It consists of many small intervals interleaved by periodic long intervals (spikes) at the offset of each batch. The period ...
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