Questions tagged [prediction]
prediction is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future.
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seasonal adjustment to testset
I would like to understand how to apply a seasonal adjustment to my testset. Let's imagine we have a time series and divide it into trainigset (80% of the samples) and testset (20% of the samples). I ...
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Winter Holt's Time series model
I am confused with the Winter Holt's Time series model usage. I use 2 years of data to train and want to predict 3rd-year data.
Note1: I have partial 3rd year's data, but I want to use it to check my ...
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Prediction of Quarterly Financial Numbers
The task is to predict the quarterly revenue numbers using machine learning. Only 28 quarterly data points for financial numbers are available as companies release the revenue data quarterly. I have ...
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Can a multivariate MIMO LSTM forecaster learn the relationships between the multiple feature outputs?
Question: Can a multivariate MIMO LSTM learn the relationships between the multiple feature outputs?
This question arose when I decided to modify a multivariate (Multiple Input - Single Output, MISO) ...
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Calculate the curve of a list and apply that to calculate the values of a larger list
I have a list of ~125 sorted ascending values and I am converting those to percentages of total so that they add up to 100% (shortened example would be: 0%, 0%, 15%, 15%, 30%, 40% with the total of ...
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Prediction of the next number 0 to 9 that doesn't have a particular sequence
How to predict a number from 0 to 9 that are random and doesn't have any particular sequence that the numbers are following?
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Creating insights from Battery monitoring parameters (State-of-charge, battery cell voltages, temperature, etc.) to use with AI or model based
So, in a new role currently and I decided to pursue the health monitoring and impending failures of batteries (Lithium Iron Phosphate, Lithium Ion and a few lead-acid as well) but having never done it ...
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Dynamic dosing recipe for accurate pH
I want to have a script for adjusting dosage of ingredients in each batch dynamically. Assuming that the requirement is to have a specific value of pH from each batch but with the variation of raw ...
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Finding/analyzing data competitions that share the individual model's predicted outcomes?
I would like to analyze past machine-learning data competitions, focusing on bias and variance of predictions. I thought I could look at past kaggle competitions, but unfortunately kaggle typically ...
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Dealing with varying predictive horizon
I know that the predictive horizon is the time window that runs from the observation of the data to the manifestation of the target variable.
But how can I deal with prediction if the time horizon ...
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accuracy on test set is always close to the distribution of label
here is a time series, at each time $i,$ we have several features $(a_1, a_2, ... a_k)$ and a binary label $y.$ Now I use a window
$$(a_1[i], a_1[i-1],..., a_1[i-s],..., a_k[i], a_k[i-1],..., a_k[i-s])...
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What Can Prevent Time-Series Prediction Model From Learning Trend?
I am building an encoder-decoder prediction model based on this paper:
https://www.sciencedirect.com/science/article/pii/S0952197623001483
It is made of a transformer encoder and a 1D CNN Decoder. The ...
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Feature selection - How to identify the best subset
I am using three feature selection method on a dataset containing 15 inputs. I need to extract the best 5 features. Each of the three method gave a subset of the input dataset, but they are different. ...
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What is the highest possible prediction accuracy when I flip some labels at random?
I want to predict MNIST labels in a binary setting using a simple MLP model (0 for digits 0-4 and 1 for 5-9). For the train and test data, I randomly flip 25% of the labels.
Is the maximum achievable ...
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Do prediction intervals for Random Forest predictions are average of prediction intervals of trees being Random Forest estimators?
I am working on adding prediction intervals for each prediction value of new input samples.
Do prediction intervals for Random Forest predictions are average of prediction intervals of trees being ...
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After training an LSTM model using log returns as the input and obtaining a binary output of either 0 or 1, how can I predict stock movement?
I have been working on a personal project called "Predicting Stock Movement using LSTM." For my project, I have selected log returns as the input (X) and the target (y) is whether today's ...
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Large Language model for regression on urls
I am trying to fit a BERT model for a URL regression task. I have a URL as a feature and I have to predict a metric M for it. Keeping a learning rate like $10^{-5}$, the model is overfitting in about ...
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ML with Python - extract information from user text
I'm a web developer, got a little experience with Python but none on ML.
Tour operator customer want to introduce AI/ML on his website, the goal is to have a single text input where user can prompt ...
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Classifying a set of time series with restrictions on the classes
I have $N$ time series, each having $T$ time points, with the class of each time series known. There are $K$ different classes. I want to predict the classes for a set of $M$ time series (with time ...
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Sequence prediction in Parent - Child dataset
We have a large collection of documents (D), each accompanied by a set of metadata (M). Within this collection, some documents act as parent documents and have multiple child documents. Both parent ...
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How to predicting the next date with Python ML
I have a list of dates around 10 dates in asc order. These are the dates a buidling was open. I need to predict the next date using this. I tried scikit learn like below
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How to predict what someone will order?
Suppose Prof. X goes to a road side tea-coffee shop everyday at 5pm just after his office. After reaching there he tosses a coin, and places his order tea or coffee. The shop owner Y has been ...
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LSTM multivariate time-series with categorical, numerical and non-temporal inputs
I have a dataset that contains different data types and I'm working on a prediction task of dataset features with LSTM network, but I'm struggling in finding the right way to construct the neural ...
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Ordinal logistic regression prediction and accuracy using statsmodels
I am trying to do a ordinal logistic regression analysis using statsmodels. However, the predictions I'm getting are vastly different from that I get when using SciKit-Learn ...
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How to make a prediction if training time series have different lengths?
Description: I have 100 products. For each item I have the number of hours it has been used for each quarter of multiple years.
Item
2020_Q1
2020_Q2
2020_Q3
2020_Q4
2021_Q1
2021_Q2
2021_Q3
2021_Q4
...
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Remove Seasonality before applying SARIMA model on weekly data?
I am trying to predict average weekly stock prices for time series data.
Steps I followed:
I tested the data to check whether it was stationary or not using ADF ...
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How can I use a predicted machine algorithm model to find specific inputs?
I am relatively new to machine learning and now I am working on my thesis regarding that!
My goal is to find a prediction model to see if the strain of the three zones is similar to each other or not.
...
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how to analyze model performance without any ML libraries using a BI tool?
I have a dataset that has the ML model predictions along with the true labels column of the predictions and ...
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How to interpret predictions from a specific PyTorch Model
I have obtained the prediction values from this PyTorch model, at least I think so(https://github.com/allegro/allRank) by running:
slates_X, slates_y = __rank_slates(val_dl, model)
But the output ...
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LSTM model constantly underpredicts
I have a dataset that consists of temperature measurements. The temperature is increasing as time goes by. I have developed different RNN models (LSTM,BiLSTM,GRU,BiGRU) to predict future values of ...
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Appropriate sample size for prediction algorithm
Our study aims to develop a Random Forest algorithm to predict the incidence of suicidal thoughts, after one year, based on the responses given to four surveys at baseline (time-1). Each survey ...
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Integrating time context in a machine learning model
Basically, what I'm curious about, are there any methods in machine learning to make the model take into account events that happen in real time that affect the data points during that time period. ...
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Do models of social systems suffer from prediction drift?
Background
I've created a binary classification model that predicts the probability of fraud for a given sample.
The choice of threshold allows me to set how many frauds are captured in the training ...
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Differential equations, real time measurements of variables, and ML
TL;DR: We measure variable $x$ every $10$ minutes, solve a differential equation $\frac{\mathrm{d}y}{\mathrm{d}t}$ where $y=f(x)$. We are interested in the time it takes for the cumulative value of $y$...
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Understanding RSV in WrapCauchy
I am trying to predict using wrapcauchy. This was the best fit when running the fit_transform() method.
Unfortunately, I am running into some issues. I keep receiving the error:
...
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Testing RANSAC regression model
I am going to build the model (e.g. multiple linear regression) to predict the appartment cost in my city. First I have to find outliers in training data. For this task RANSAC regression algorithm ...
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Force network to weigh specific variables during learning
I have a pandas data frame containing around 100000 observations of plant species and their age with additional numerical predictors (climate). I used tensorflow ...
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Combine machine learning feature selection with time series
I have basic knowledge in time series prediction and supervised/unsupervised machine learning algorithms (clustering, classification, decision tree, etc.) I am now given a task to predict a bunch of ...
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Tensorflow prediction of (PRNG) future values
I would appreciate if you could help me with the following problem:
Future values of a given time series of pseudo-random values should be predicted. It is unclear how good the PRNG performs and if it ...
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Should we reinsert trend after doing forecast using detrended data?
Basically, when we detrend a signal, we detect and remove a linear component of that signal. This produces a stationary version of that signal. And we can use various forecasting algorithms to ...
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How to improve pytorch network to be more precise
I have a pytorch neural network which is being trained on data containing multiple financial values and some other non numeric values translated to numbers with self created logic (for example if ...
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Append prediction of tensorflow to a pandas dataframe
I built a tensorflow model to make text classification in four category, after testing and evaluating it, I need to apply it to actual data to predict the class of them, I create a predict function ...
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Consumption rate on a small dataset with variability
I am looking to find the consumption rate, or how fast I am consuming energy so that I can later predict when my energy will reach a certain threshold. My dataset is fairly small and looking to see ...
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Robustness of an ARIMA Model
I have built an ARIMAX model in python for predicting a time series. After presenting my findings ive been asked what robustness tests i have used. My skillset is more on the python side. I only have ...
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Get dependant probabilities in multiclassification
After training my CatBoostClassifier model I call get_proba function which returns me list of probabilities. The problem starts from an another point... I transfer that data into dataframe then to ...
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Excessive features in an Neural Network model
I am modeling a quality parameter of a chemical process product. I have a list of circa 400 process parameters sampled throughout the process. Most of them should have no meaningful impact on the ...
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How to deal with multiple and overlapping timeseries from weather prediction/forecasts in pytorch-forecasting?
Dear Data Science community,
I want to run temporal fusion transformer supposed by google research.
Meanwhile it is part of pytorch-forecasting and I want to setup a ...
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How to improve the AUC scores of my link prediction algorithm?
I am exploring a link prediction algorithm, but my AUC scores change every time I run it and it is always very low. It varies from 50 to around 72. I was wondering if there is a way to improve their ...
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Help in creating a prediction function for linear regression model
Hi i need some help to create a prediction function for a Multiple Linear Regression model.
This is the collab file Please help Collab File
I am confused how to apply transformation on the data and ...
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Predicting number of women who will give birth on a given day?
I have a set of data with information about women and their expected delivery dates for childbirth.
I have more columns in the table but for simplicity let's just focus on the below and assume that my ...
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