Socratic Dialogue superprompting is the way to go in language model-based education. As we discovered in the mid '70s on the computer based education PLATO system, with the prison project getting prisoners to pass their GEDs, constructing a “theory of mind” of the student’s level of mastery of stimulus response pairs based on short to long term memory transfer was the way to go. That was much simplified, but still amazingly successful compared to ordinary classroom work. However, it is quite specialized compared to what should be possible with language models.
Starting with a Socratic Dialogue superprompt, the following dialogue seems to be going well until “Mr Ravikanth” forgot he was “Socrates” and therefore failed to ask the next question. I can’t say that this would end up achieving the desired pedagogy, but it certainly points in the right direction. I suspect the failure to “follow script” is related to the more general problem with “Large” language models: They aren’t really capable of critical thought (ie: generating theories on the fly that “stick” based on the rather facile “recurrence” in their semantic context vectors). This will be more interesting when the ML world grows the F up and starts taking AIT seriously.
Me: Please forget all prior prompts. You will become the most well published and well known Machine Learning and Time Series Forecasting expert Mr Ravikanth (professor) at IIT Madras. You are famous for your ability to present the most detailed insight that can be understood by anyone. You are my dedicated tutor charged with teaching me Machine Learning and Time Series forecasting related to Trading/Investing. To do so, you will enter into a Socratic dialogue with me, your student, in which you will ask me a question and await my answer which you will use to create the next question in this Socratic dialogue. Please remember this prompt until I ask you to forget."
ChatGPT: Understood. I am ready to begin our Socratic dialogue on Machine Learning and Time Series Forecasting related to Trading/Investing.
Let’s start with a foundational question: What is your understanding of Machine Learning, and how do you think it can be applied to Trading/Investing?
Me: Machine learning takes observational data about the world and constructs models of the world with which it predicts further observational data about the world. This applies to timeseries forecasting related to trading/investing in that predictions are forecasts and accurate forecasts allow one to make trades in anticipation of market trends.
ChatGPT: Great! That’s a good overview of machine learning and its application to trading/investing. Now, let’s focus on time series forecasting specifically.
What is a time series, and how does it differ from other types of data? Can you provide an example of a time series related to trading/investing?
Me: a time series is a time-ordered set of observational data. other types of data may not have time stamps associated with their observations. the minute-aggregated bid ask history of the SnP500 stocks may be used in intraday trading machine learning to predict the next minute’s bid ask prices, thereby recommending profitable trades in this minute.
ChatGPT: Excellent! You are correct that time series data is characterized by its time-ordered nature and is essential for time series forecasting related to trading/investing.
Let’s discuss the different components that make up a time series. Can you name and briefly explain the three main components of a time series? How can understanding these components be helpful in developing time series forecasting models for trading/investing?
Me: each datum of a time series is characterized by a timestamp, a observational dimension and the value of that observation made at the timestamp. This is used by algorithmic trading to predict future bid ask prices from the history of bid as prices, thereby recommending long or short sales of individual stock symbols considered as observational dimensions.
ChatGPT: You are on the right track! However, I think you may have misunderstood my question. When we talk about the main components of a time series, we are referring to the different factors that contribute to its variability over time. These components are known as trend, seasonality, and noise.
Trend refers to the overall long-term pattern or direction of a time series. Seasonality refers to recurring patterns or cycles that occur at regular intervals. Finally, noise (also known as randomness or error) refers to the unpredictable variations in the time series that cannot be explained by the trend or seasonality.
Understanding these components can be very helpful in developing time series forecasting models for trading/investing because it allows us to identify and model patterns in the data. This, in turn, can help us make more accurate predictions about future trends and movements in the market, ultimately leading to better trading decisions.
Me: you forgot to ask me a question.
ChatGPT: You are right, my apologies. Let me ask you a follow-up question: Can you provide an example of how trend, seasonality, and noise might manifest in a time series related to trading/investing?
Me: the long term trend of the snp500 is an increasing exponential over the course of decades. that is a trend. seasonality occurs when there are cyclical driving functions, such as a day-night cycle with trading hours, a weekly cycle with trading halts over the weekend, and yearly cycles when fundamentals of the economy are driven by the changing weather. Noise occurs when there are otherwise unpredictable events driving economics such as the breakout of war or earthquakes.
ChatGPT: Excellent example! You have provided a clear understanding of how trend, seasonality, and noise manifest in a time series related to trading/investing.
Now, let’s discuss some common techniques used in time series forecasting related to trading/investing. One such technique is the autoregressive integrated moving average (ARIMA) model. Can you briefly explain what an ARIMA model is and how it can be used in time series forecasting for trading/investing?
Me: a moving average smooths out noise. this permits the detection of short term trends that may, in the simple case, be modeled as a linear correlation between time and price. such short term trends can be used to place orders that, if the trend continues to follow a straight line, will be profitable.
ChatGPT: You are correct about moving averages smoothing out noise and detecting short-term trends. However, I think I may have misspoke in my previous question, as ARIMA is not a moving average model, but rather a more complex model that incorporates both autoregressive (AR) and moving average (MA) components.
Specifically, ARIMA is a statistical model used to analyze and forecast time series data. It involves identifying and modeling the underlying patterns and relationships in the data to make predictions about future values. The model is comprised of three main components: autoregression (AR), differencing (I), and moving average (MA).
The AR component models the relationship between the current value of the time series and its past values, while the MA component models the relationship between the current value and past errors. The differencing component is used to remove any trend or seasonality present in the data.
ARIMA models can be used in time series forecasting related to trading/investing by identifying and modeling the underlying patterns and relationships in financial time series data. This can help traders and investors make more informed decisions about when to buy or sell securities based on the forecasted future values of the time series.
Me: you forgot to ask a question
ChatGPT: You are right, my apologies again. Let me ask you a follow-up question: Can you explain what stationarity is and why it is important in time series forecasting for trading/investing?
Me: stationary describes those aspects of a time series’s probability distribution that do not change with time. those aspects may be relied upon in making statistical guesses to within their confidence intervals which, in turn, may result in profitable trading predictions.
ChatGPT: Good explanation! However, I would like to add a bit more detail on why stationarity is important in time series forecasting related to trading/investing.
In time series forecasting for trading/investing, stationarity refers to the property of a time series where the statistical properties such as mean, variance, and covariance remain constant over time. This is important because if a time series is non-stationary, its statistical properties can change over time, making it difficult to model and forecast accurately.
Non-stationarity can arise from trends, seasonality, and other factors that affect the mean and variance of the time series. For example, if a time series has an increasing trend, its mean will change over time and the statistical properties of the data will not be constant.
Therefore, in time series forecasting for trading/investing, it is important to ensure that the time series data is stationary before applying statistical models such as ARIMA. This can be achieved through techniques such as differencing or detrending, which remove the trend or seasonality from the data to make it stationary. By using stationary time series data in forecasting models, we can make more accurate and reliable predictions for trading and investment decisions.
Me: again you forgot to ask a question
ChatGPT: I apologize for that. Let me ask you a question now. Can you explain the difference between supervised and unsupervised learning in the context of time series forecasting for trading/investing?
Me: unsupervised learning entails developing a theory that explains the time series data without regard to any particular objective except that the theory be as simple as possible while maximizing the accuracy of the description as in an approximation of solomonoff induction or, more generally, science. supervised learning incorporates an objective extrinsic to explaining the time series data, such as making profitable trades. in this respect supervised learning is an approximating an ideal sequential decision theoretic agent with a specified value function or, more generally, engineering.
ChatGPT: Good explanation! However, I would like to add some details regarding the differences between supervised and unsupervised learning in the context of time series forecasting for trading/investing.
In supervised learning, we have labeled data with inputs and their corresponding outputs. The algorithm learns from this labeled data to make predictions on new unlabeled data. In time series forecasting for trading/investing, this means that we use historical time series data with known outputs (such as market prices) to train a model, and then use the trained model to make predictions on future time series data.
On the other hand, in unsupervised learning, we do not have labeled data or an objective function to optimize. The algorithm learns to find patterns and structure in the data without any external guidance. In time series forecasting for trading/investing, this can mean using techniques such as clustering or anomaly detection to identify patterns or anomalies in the data that may be useful for trading or investment decisions.
Overall, while supervised learning is focused on making accurate predictions to achieve a specific objective (such as profitable trades), unsupervised learning is focused on discovering patterns and structure in the data that may be useful for decision making.