Questions tagged [forecast]

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Why there is a gap when generating lags in time series?

I just started heading into time series forecasting, and a friend of mine who is doing this for several years showed me one of his projects. In his project, he was forecasting monthly sales quantity ...
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Which dataset for multivariate time series forecasting

I'm trying to forecast Real estate Price , it's not a prédiction. But a forecast Like the Price of a an appartement in 2023 or 2024, i'm asking about how should be my dataset ? Can I use a dataset ...
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Forecasting out of sample with Fourier terms as regressors

I'm trying to create a multivariate multi-step-ahead forecast using machine learning (weekly and yearly seasonality). I use some exogenous variables, including Fourier terms. I'm happy with the ...
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Interpretation of VAR model: about impulse function and lag of p

For example, I have three time series, Y,X1,X2. After using time series cross validation and utilizing BIC/AIC to determine the best p as the lag of the VAR model, in which I got p = 1 to estimate the ...
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Forecasting profit based on allocation of labor and time-series data [closed]

Situation: a store sells services A & B, and we have historical data for daily sales/revenue/profit of each service. The store is interested in whether they should staff for more of service A or ...
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Forecast methodology for geographic variables that are somewhat related

I'm creating time series forecasts for different geographies and wanted an expert opinion on how I can take into account geographic relationship to improve my model. Is there an algorithm that's ...
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Best forecast model for insurance policies volumes

I am new in forecasting and I am studying a dataset from an insurance company that contains the volume on a monthly basis of new policies, renewals & cancellations. New policies of a given month ...
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Demand forecasting with marketing budget data

I'm trying to build a demand forecasting model to predict future daily orders of an online food takeout service (similar to UberEats or DoorDash). My first model uses a univariate approach, which is ...
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Impact of covid19 in forecasting models

I have sales training data from 2019-06 to 2020-06 and I have to predict sales from 2020-06 ...
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How to model a conditional demand forecast model as ex-ante forecast for a moving population?

Goal: I am trying to forecast demand from a specific population given a specific promotion on a certain period of time frame. The data is of the format: date | promotion % | sales in 1000s(y) 01-Jan |...
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2 answers
946 views

Time series forecast for small data set

I am new in data science so please accept my apology in advance if my question sounds stupid. I want to do a time series forecast of outage mins in the current regulatory year. The regulatory year ...
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2 answers
120 views

Low precision on classification model

I am working since some months on a prediction from lead to a sale. Someone makes a lead on my website and I want to predict if this user will make a sale. I have these metrics on the test data. Now ...
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How to condition your neural network to seek a specific amount of output? [closed]

I transformed a time series with collection values ​​from 0 to 100 into a windows of 60 elements with ...
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2 votes
1 answer
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(Not a programming question) Is there a common word to indicate predict or forecast

The general understanding is: Predict: past value - could be data the model has seen (data from train set) or past data model has not seen (data from test set) (together in-sample data) Forecast: ...
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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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0 answers
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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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Multiple Values for One Day

I have two questions. 1- I have weather data of 10 turbines and I know their collective production(Power).I also know maximum power a turbine can make. How can I forecast collective production if I ...
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2 answers
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Convey time lag information to a linear regression model

I am using a simple linear regression to predict the number of units an item has moved and price of the item is one of the input parameters. For a few items, the older prices are not relevant and ...
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1 answer
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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) ...
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201 views

Sliding window approach using SVR & LightGBM

I'm working on a multivariate time series forecast using a couple of ML algorithms (Neural Networks, Support Vector Machines & Gradient boosting algorithms). I need to measure the performance of ...
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3 votes
1 answer
952 views

ValueError from statsmodels ExponentialSmoothing

I've been having a frustrating issue with the ExponentialSmoothing module from statsmodels. My data is a pandas series with 74 weekly data points that looks like this: ...
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How to fix "'The `start` argument could not be matched to a location related to the index of the data." Error?

When I try to predict the results using ARIMA for a specific train/test split, its throwing an error like this: "'The start argument could not be matched to a ...
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1 answer
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Auto.arima with xreg in R, restriction on forecast periods

I am using the forecast package and implement auto.arima with xreg. Here I want to forecast ...
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0 answers
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How to interpret the graph representing the fit provided by the ARIMA model?

I'm following this tutorial here to build an ARIMA model in R. I've done a Forecast using a fitted model in R. I specified the forecast horizon h periods ahead for predictions to be made and used ...
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How to explain calm period in energy consumption

I have a dataset of energy consumption that looks like this: I got it from Kaggle.com and all it says is that it's energy consumption data from PJM (this is the PJMW_hourly that corresponds to the ...
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3 votes
1 answer
77 views

Next year forecasting with monthly data from many, correlated, non-monotonic trends

I have trend data from many health departments in a local territory (eg. cardiology, orthopedics, etc...). These trends represent health service (visits, diagnostic, admissions) production, service ...
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3 votes
2 answers
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How can I go about building a model for large number of outputs?

I have previously worked on small-scale feedforward neural network problems. But I have started working on a new project where the goal is to predict air quality in 25 locations throughout the ...
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1 vote
0 answers
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Model for time series analysis

I'm new to data analysis and ML in general. I'm working with some friends on this problem: We're trying to predict when a component of a machine will stop working properly so the client can change it ...
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Predicting Wave Trends of Candelestick Charts in Tensorflow (JS)

I'm relatively new to ML but my goal it to use Tensorflow.js and build a ML model that can help me detect a certain wave formation for an automated trading system. Examples of the 3-leg pattern I am ...
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5 votes
5 answers
35k views

Additive vs Multiplicative model in Time Series Data

The above time series plot is a daily closing stock index of a company. I want to know which model between additive and multiplicative best suits the above data. I know what the two models are, but i ...
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2 votes
2 answers
732 views

Analysis of Time Series data

The below graph is a scatterplot of daily stock price. My aim is to predict future stock price of the company. From the scatterplot it seems that it is a multiplicative model, so I tried to "...
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1 vote
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39 views

forecast product demand in one week using machine learning approach

I'm trying to predict product demand in store. The predictors I have include price, competitor's price, store ID, date. My target variable is sales volume (in a particular store). What I need to ...
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2 votes
1 answer
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how to predict content based demand

this is my first post at ds StackExchange, so please be gentle and let me know if something is not clear :) I have many products (>1M), and I save all the products purchases in a DB with a time stamp....
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1 answer
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ML technique to predict next year output based on text quantities [closed]

I have a random data that I would like to predict how much a quantity will be in 2020. The data looks like this: ...
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1 vote
0 answers
64 views

Tuning a sequence to sequence model

I have written a variable length sequence to seqeunce autoencoder in keras using this tutorial as a guideline: https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras....
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5 votes
1 answer
272 views

Is an Arma model equivalent to a 1-layer Recurrent Neural Network without activation function?

Given a time series $f(t)$ to forecast, let us consider an Arma model of the form: $$ f(t) = c + \sum_{i=1}^p a_i f(t-i) + e(t) + \sum_{j=1}^q b_j e(t-j) $$ where $e(t)$ are the forecast errors. On ...
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4 votes
1 answer
5k views

Monthly trend with fb prophet-Interpreting the graph

I have monthly data with month/year in one column and price on another. I would like to get a yearly trend with fb prophet library in python (how to use monthly data with the library is explained at ...
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1 vote
0 answers
42 views

Specific data formatting techniques for discontiguous time series?

I'm facing a predicting problem for food alerts. The goal is to predict the variables of the most probable alert in the next x days (also any information I could get about future alerts is really ...
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1 vote
0 answers
22 views

Observation Operator - Data Assimilation

In data assimilation, one assumes the existence of a observation operator $\mathcal{H}$ that maps the model-state vector $\bf{x_b}$ to $ \bf{y_b}$ (the model-equivalent of the observations $\bf{y_o}$) ...
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1 vote
1 answer
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Strategies for continuously assessing and improving model performance

I am building a supervised machine learning model to generate forecast. So I would have historic data like this: SKU, Month, .... other features, Actual Volume ...
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1 vote
1 answer
3k views

How to calculate prediction error in a LSTM keras

I have an LSTM which I have constructed and run in keras using python. I use this model to predict $n$ points into the future for a time series forecasting problem. When I use a method such as ...
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1 vote
2 answers
143 views

What are some appropriate models to use for inventory forecast based on consumption history or trends?

I am working on an inventory management system where I have daily/monthly/yearly consumption history for a particular item, which may or may not follow a repeating trend. In order to forecast demands ...
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0 votes
1 answer
34 views

which forecasting models could be chosen?

I'm new for data analysis. I got some data from the regional environmental center. Measurements: Datetime, PointID, SubstanceID, Value (substances concentrations in air), MeteoID ,NextValue ...
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1 vote
1 answer
169 views

Fully endogenous models for predicting multivariate time series

I have a formal social science background but I am new to data science. My interest is in building predictive models for applications in the social sciences, mostly (but not only) in economics. I am ...
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1 vote
1 answer
674 views

demand forecast for B2B

I am attempting to create a demand forecasting model in python to predict future sales of a particular category of product, using historical sales data. We are a B2B company, which means that we ...
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1 vote
0 answers
167 views

Naive/ Persistent Models & 7 Day Forecasts

When determining the baseline performance using a persistent or naive model I understand it to be using the value of the previous time step as the prediction for the next time step and then ...
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0 votes
1 answer
254 views

A timestep's prediction depends on future data

Consider an LSTM model with 100 timesteps, each of which with input and target data. Let f(99) be the function mapping the input data of the 99th timestep and hidden state of the 98th timestep to the ...
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1 answer
608 views

Modelling promotions for demand forecasting

I am trying to develop a model to predict future demand for a product. Now, there are always some promotional events that affect the sales. I am trying to solve this problem using dummy variables. ...
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2 answers
928 views

Fitting an arimax model on out of sample dataset

I have built an arimax model where we have sales data across time as the response variable and price is one of the external variables. I used the below code to build a simple arimax model. I had data ...
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5 votes
1 answer
2k views

Time series forecasting using multiple time series as training data

I am trying to forecast the total attendance (ie. the number of entrances, which is also the number of tickets bought) of a festival just two days after it started. That is, knowing how many people ...
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