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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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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Strange behaviour around zero for predicted distributions from a deep learning regression model
Can somebody help make sense of these very odd distributions that I obtained from my trained deep learning regression model?
The model was trained with either MAE or MSE loss, which is what the ...
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What model to fit to call center data
I have a dataset with calls from day 1 to day 340. What model can I fit to mathematically capture the pattern?
There are only 1 or 2 digit number of calls on all days except day 61.62.63 and 121.122....
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Sales forecasting with hyper-parameter optimization for categories
Example
Item Category X (e.g. short sleeve t-shirt) contains two items.
Item A (e.g. short sleeve t-shirt, white)
Item B (e.g. short sleeve t-shirt, red)
Question
Is it logical to do the method below? ...
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longitudinal sequence prediction
I am trying to predict what a user will pick next (let's say users have 4 options = 0,1,2, 3).
I have irregular historical data for each user, demographic and contextual information.
For instance:
...
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"Most forecasting algorithms assumes that each point is independent of one another." If so, how forecasting is being possible?
In stationary, if we want to make forecasting, we have to make our data stationary(On classical methods), I get that, but If every data point is independent of each other, how can we make the ...
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How can I create a prediction model with different input variables
I have the following structure for training data:
Input 1
Input 2
Input 3
Input 4
Input 5
Output 1
0.6
0.8
0.9
0.6
0.8
0.7
0.9
0.4
0.4
0.6
nil
0.6
0.8
0.7
0.7
0.8
nil
0.8
0.7
0.8
0.5
nil
nil
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Developing Modified KNN Approach
I want to divide the training set into n partitions further besides testing set. How can I do that?
Furthermore, I'm creating these groups in the training set. How ...
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How to flatten Python TF with Keras predictions?
Im quite new for Machine Learning and Neural Networks and I am struggling with time series prediction.
I have charted my LSTM predictions as below. You can see some noises in regular intervals (server ...
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How can I estimate the payment date?
I have a list of sellers and want to pay them after they sell on an e-commerce site (me). I am trying to create an equation where every seller has an estimated payment release date based on the risk ...
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How to select the best 30 features from 500 features for sales prediction model where feature importance can change over time?
I'm using data sets for sales prediction model which is trained every 2 weeks. It
has 200 features and 500 rows. I have to select the best 30 features that can be used in the
model instead of 200 ...
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How can I increase the correct predictions of one class using Weka?
I need to predict events (true or false) from a dataset, it has True and False samples. I'm just trying to predict as many "trues" as I can. Missing out some is no problem at all.
The ...
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loss of exponentially weighted forecaster
In Theorem 2.2 of the book "Prediction, Learning, Games" on page 16 they define the quantity $W_t = \sum_{i=1}^N w_{i,t} = \sum_{i=1}^N e^{-\eta L_{i,t}}$. However, $w_{i,t}$ is defind on ...
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What kind of models are suitable for predicting a proportional dependent variable (apart from logistic regression)?
I have a task of building two ML models in Python to predict a proportional value. I have a small data set of a fitness club's classes where each row was a class held this year. I have to predict the ...
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Best method for predicting percent usage based on time?
My organization implements wellness programs at other companies and we supply monthly reports on the percent of employees that use the program, as well as a prediction for what percentage will have ...
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Tools for predicting angles using tabular data
What are the best tools to predict a set of angles given tabular numeric features? Say 10-100 features, and 1000-10,000 observations. The goal is purely prediction. My initial thought was to transform ...
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Python pretrained model vgg19 predict all images in a directory
I am using VGG19 for transfer learning (I have 9 classes in my new model) and I want to use the build-in decode_predictions method to output the predictions of my model.
However, I have an error when ...
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Replace a lookup table with machine learning
I have a lookup table with 2 input columns and 2 output columns. I want to replace it with a value function such that with a given input pair, the function can give the output pair with minimal error. ...
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Queries on architecture of production pipeline for a batch predictions
I have a Problem statement to predict Expected delivery time (in days) and below is the information we have from client.
Prediction to be batch inference.
Each batch inference to have millions of ...
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What is the effect Cross-Multi-Labeling/Annotation on learning process?
I have a philosophical question regarding training convolution neuronal network. I am work on training NN for purpose of detection of Window and Window blind. This is an issue of cross labels; that is,...
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Need help diagnosing a training curve for LSTM-network
I am doing time series prediction using and LSTM-network. The dataset is divided into a training, test and validation set. The LSTM-model structure (number of neurons and layers), learning rate, batch ...
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Neural network training for multi time scale (fast and slow) data
I have a background in dynamical system control and I am new to machine learning field. In control, we sometimes have a system that has multi time scale dynamics, e.g., some states evolve much faster ...
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