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Prediction of the future events. It is a special case of [prediction], in the context of [time-series].

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R forecast nnetar - how are the weights updated after random initialization?

R forecast package nnetar Documentation mentions that the NN weights are "learned" from data. How does that happen?
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24 views

Why are Gaussian Processes valid statistical models for time series forecasting?

Duplicates disclaimer: I know about the question Time series forecasting using Gaussian Process regression but this is not a duplicate, because that question is only concerned with modifications to ...
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13 views

Multivariate time series forcasting of small data in python

I have sports data with an average length of 192 per match. where each row is a 30 sec time portion of the game. The aim is to implement a predictive model. In order to predict at a time ...
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1answer
18 views

How can a person predicted best playing 11 in a match between two teams?

https://www.dream11.com/games/cricket/point-system This website allows people to bet on cricket and football matches. They ask people to select 11 players and there are point system, so at the end ...
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13 views

Default model used in stlm() function in R [on hold]

When forecasting of a seasonal time series is done using stlm() or stlf() in R and no model is specified, what is the default model used? Ps- I read the documentation and searched on the internet but ...
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1answer
180 views

Auto-Arima creates a straight line help

I'm trying to create a forecast using autoarima with some data, but i always get a straight-line, can someone please help me? :) This is what i've got so far ...
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1answer
9 views

Find out which characteristics to choose, based on the frequency measurement of characteristics of similar items

I have this statistics oriented question and I need some input on the approach. There is a new product introduced, a contact lence that has seleral ranges of attributes (powers) from which the users ...
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15 views

How to correctly compare the accuracy of different forecasting methods

I am currently working on a forecasting project and I have tried several different models to forecast with. Having trained and tuned my models I want to pick which model works best for each time ...
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1answer
425 views

Stepwise AIC - Does there exist controversy surrounding this topic?

I've read countless posts on this site that are incredibly against the use of stepwise selection of variables using any sort of criterion whether it be p-values based, AIC, BIC, etc. I understand why ...
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30 views

Time Series Prediction Model for Home Prices

I am building a time series model to predict the zillow home prices for march 2019.I have data for each zip code from the year 1993 - 2018 and i have prices for every month.I was trying to use ARIIMA ...
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0answers
20 views

Using maximum of forecasted values to forecast maximum

I am using an algortihm to generate a daily sales Forecast and have concluded that the Forecast is, for pratical purposes, of good enough quality ("low" wMAPE). In general, and without further ...
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1answer
97 views

How to properly utilize lag and errors in Time Series modelling

I have a dataset of 2 variables that should be heavily correlated. There are some underlying reasons why this set has an R^2 of only 0.620 when modeled in a simple Linear Regression; the independent ...
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1answer
42 views

Combining Intermittent Demand and ARIMA

I have a time series dataset, where a customer may purchase fuel one week and not purchase again for 2-3 weeks. I need to forecast when a customer is likely to purchase and how much they will spend. ...
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17 views

Adding noise to time series data to increase training data

I am dealing with a weekly time series forecasting problem and I am currently investigating the use of an LSTM to make a multi-step forecast for a univariate time series. I actually have a ...
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2answers
28 views

Demand prediction multiple target variables

I am working on prediction of porduct demand. I have dataset of transactions for many customers for a whole 2017 year. Problem is that I have lot of porducts with different quantities and packages in ...
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0answers
22 views

How can I normalize truncated variables for a neural network?

Generally, I normalize variables using standard normal variates or (x-xmin)/(xmax-xmin) But this only works well for variables that are not truncated, for example ...
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1answer
25 views

How do I determine an optimal threshold for a time series forecast?

I have a data set that includes sales dollars by sales order and I want to perform a time series forecast on it. Low dollar sales orders have very little noise and after detrending and doing some ...
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11 views

Predictive Modelling to predict probability % of occuranvce of an event after time 't' [closed]

Need suggestions on the best technique(preferred to be incorporated in R) to predict probability % of occurrance of an event in time 't'?
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37 views

multi-response forecasting

General Dear community, I really struggle with some imporant issues for my next project. In general, the investigation is about multi-response forecasting with financial data. The predicability of ...
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Query on LSTM (Request for guidance)

I recently started working on a project at the University. The main task of the project is to apply Deep Learning for forecasting. I have the dataset from a company that basically contains various ...
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1answer
32 views

How to deal with both stationary and non-stationary time series

I'm a bit frustrated since the time series I am trying to analyse right now has definitely non-stationary curve but it's last values differ greatly from the mean making the time series stationary. ...
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1answer
58 views

Advice on correcting for seasonality in data

I am a little new to time series in general, however, I have 5 years of weekly sales data and another variable of interest. I am trying to see if there is a trend in the sales data and the second ...
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0answers
25 views

What ARIMA to use

I have a data set which generally decreases over 24 period units. It then returns to its relatively highest state at the beginning of the period. So for instance the data may look like this: Period 1 ...
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0answers
25 views

Forecasting with ARMAX vs. Regression with ARMA errors

In this post Rob Hyndman says that for forecasting, it doesn't matter whether we fit an ARMAX model or an OLS model with ARMA errors: https://robjhyndman.com/hyndsight/arimax/ Why is that the case? ...
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29 views

Forecasting seasonality with Fourier terms in R

I am using the auto.arima from the forecast package in R to determine the optimal K-terms for fourier series. After I do that, ...
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1answer
19 views

R forecast multiple seasonality optimal model search using fourier and msts objects

Hi I have hourly data (one obs one hour) with multiple seasonality. I would like to fit an ARIMA model using forecast R package taking into account the multiple seasonality, maybe taking also in ...
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1answer
23 views

Lag between forecast and actual value without lagged dependent variable as features

I'm trying to predict a time series using a model-tree (Cubist) and I'm getting a strange behavior, I think. This is a stock market data but I'm not using the raw level of the stock price but change ...
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1answer
38 views

Reverse prediction in a time series

We know using models like ARIMA we can do out of sample predictions for a Time Series. i.e. we can know what would be the value v at time t. Can we do the reverse of it, and find at what t will be v a ...
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34 views

Unable to completely extract annual seasonality from daily time series using R's decompose/stl functions

Context first, questions at the bottom. I have 10 years of daily precipitation data that exhibits an annual seasonality, which I am trying to model using ARMA methods and then forecast. Data here, ...
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1answer
17 views

Forecasting plot -adding fitting and validation area

I refer to the link: http://kourentzes.com/forecasting/2016/06/17/how-to-choose-a-forecast-for-your-time-series/#comments How should i add fitting and validation area in the plot, according to the ...
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1answer
24 views

ML classifications

I have a doubt in below ML families. If we are predicting: Yes, then we have classification and regression If No, then we have clustering In clustering, we have K-means algo In classification we ...
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1answer
60 views

How to meaningfully compute the accuracy of a multi-step forecast produced by a model

I am trying to measure the accuracy of my model in producing a multi-step forecast and I have read a lot of different opinions on the matter and am now rather confused. The goal of my model is to ...
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1answer
42 views

Python ARIMA generates different predictions than SARIMAX for same orders

I was under the impression that Python Statsmodels SARIMAX with seasonal order parameters set to 0 will generate the same forecasts as ARIMA. But apparently the forecasts are wildly different. What ...
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40 views

Relationship between Total Over/Under scores and actual total scores in sports

I have a data set of actual scores from sporting games, matched with the bookmaker's Total Over/Under Score (O/U Score) and the odds the bookmaker was offering that the game's total score would fall ...
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28 views

Autocorrelation in loss function, want to perform DM test. How many lags to use? (R)

I have two sets of forecasting errors, and want to perform a DM test. Both forecasts are a fixed size moving window, and are 1 day ahead forecasts. The first step of performing the DM test is to ...
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0answers
4 views

Meta-method in WEKA to apply forecasting for multiple sequences determined by a partitioning variable

Is there a way to implement multiple forecasting models in WEKA, where instead of one sequence of events there are multiple sequences, for different (user) identifiers? Let's say, a traditional ...
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0answers
27 views

How to globally determine (Seasonal) ARIMA Parameters

I have 15-minutely data (96 values per day) over several years for around 340 entities (i.e. 340 data sets or long ts). Now my task is to forecast a 4-hour window (i.e. 16 observations) for each day ...
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1answer
18 views

Are basic multilayer perceptrons well-suited to prediction of non-independent events?

Multilayer perceptrons are great for discovering associations between variables defining independent events based on the same underlying associations in reality. Less cryptically put, MLP's are great ...
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14 views

ACF plots before and after removing seasonality

In order to illustrate the effect of cleaning out predictable seasonalities, I plotted some autocorrelation functions before and after removing seasonalities. Before I cleaned the data for predicable ...
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36 views

Fourier Output Meaning

I just ran a fourier series on weekly sales data for 3 years worth of data. I optimally chose the number of k-terms based on the AIC. First 6 lines of my data: ...
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13 views

Forecast error evaluation based on Ljung-Box test

I'm generating an out of sample forecast using fbprophet and I'm thinking on how to evaluate the forecast. One possibility is to use the Ljung-Box test on the residuals up to order n and check if the ...
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2answers
85 views

Forecasting Intermittent Demand with zeroes in times series

I am trying to forecast intermittent demand (slow movers and extreme slow movers). Here's the type of data I am working with weekly data so I cannot really group it has zeroes in time series not ...
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0answers
18 views

Presenting simulation results on a research paper

I recently wrote a research paper on time series forecasting, "weights and biases initialization in ANN using multi-objective Cuckoo Search algorithm". The paper was rejected but they gave me comments ...
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1answer
68 views

Need advice concering forecasting next year based on irregular time-series - UPDATED

I need your advice with regards to the following inquiry: "Based on your observations, what could you say about the load for the same months in year 2019?" ...
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1answer
31 views

Modelling Idea for Big Jumps in Revenue

I'm trying to model some year-on-year data, as seen in the picture, each line represents a different year. From 52 to 0 (x-axis) are the weeks leading up to the last point on the left. I have been ...
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1answer
85 views

Updating a hypothesis on multiple partitions of uncertain evidence

I want to forecast $P(A)$ where $A$ is a messy real-world event, for which I have no analytical expression or statistical model. Assume, however, that for $b$ events $B_i$ I have forecasts for $P(...
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0answers
26 views

Does Nickell bias matter in forecasting?

The context is longitudinal data, with $i$ indexing individuals and $t$ indexing time. The goal is predicting $y_{it}$ as a function of lags of $y$ as well as $\mathbf{X}$, which might include lags. ...
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1answer
43 views

Usage of tsclean() in time series data

Consider the scenario, where I have many time series data. I have to make predictions for all.I made a ts object out the data. The data may contain outliers. I am not sure of it. But I always pass ...
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1answer
26 views

Sales Forecasting for Multiple Dealers

I have a data-set containing 7046 unique dealer codes and their monthly sales data from April 2013-August 2018. The Financial Year for the sales data begins in the month of April for a year and ends ...
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1answer
21 views

Forecasting adjustment factor in the gas consumption formula supplied by industry

I am trying to model gas consumption in France. The industry publishes a formula to use for this. A simplified version looks like this consumption = K * f(x), where ...